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# Function to print upper half of diamond (pyramid)
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
for i in range(0 , lowerCAmelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 , i + 1 ): # printing stars
print('''* ''' , end='''''' )
print()
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
for i in range(lowerCAmelCase__ , 0 , -1 ):
for _ in range(lowerCAmelCase__ , 0 , -1 ): # printing stars
print('''* ''' , end='''''' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(''' ''' , end='''''' )
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
if n <= 0:
print(''' ... .... nothing printing :(''' )
return
floyd(lowerCAmelCase__ ) # upper half
reverse_floyd(lowerCAmelCase__ ) # lower half
if __name__ == "__main__":
print(r"| /\ | |- | |- |--| |\ /| |-")
print(r"|/ \| |- |_ |_ |__| | \/ | |_")
lowercase__ :Dict = 1
while K:
lowercase__ :Optional[Any] = int(input("enter the number and , and see the magic : "))
print()
pretty_print(user_number)
lowercase__ :Any = int(input("press 0 to exit... and 1 to continue..."))
print("Good Bye...")
| 101 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_a = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = SavedModel()
__lowerCAmelCase: str = []
with open(os.path.join(SCREAMING_SNAKE_CASE , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__lowerCAmelCase: List[str] = json.load(SCREAMING_SNAKE_CASE )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(SCREAMING_SNAKE_CASE )] )
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__lowerCAmelCase: Optional[int] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowerCAmelCase: List[str] = sorted(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(SCREAMING_SNAKE_CASE )
if strict and len(SCREAMING_SNAKE_CASE ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(SCREAMING_SNAKE_CASE ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*SCREAMING_SNAKE_CASE , sep='\n' )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_a = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 322 | 0 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : List[str] = """%20""".join(argv[1:]) if len(argv) > 1 else quote(str(input("""Search: """)))
print("""Googling.....""")
SCREAMING_SNAKE_CASE : str = F'https://www.google.com/search?q={query}&num=100'
SCREAMING_SNAKE_CASE : Union[str, Any] = requests.get(
url,
headers={"""User-Agent""": str(UserAgent().random)},
)
try:
SCREAMING_SNAKE_CASE : List[Any] = (
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """yuRUbf"""})
.find("""a""")
.get("""href""")
)
except AttributeError:
SCREAMING_SNAKE_CASE : Optional[int] = parse_qs(
BeautifulSoup(res.text, """html.parser""")
.find("""div""", attrs={"""class""": """kCrYT"""})
.find("""a""")
.get("""href""")
)["""url"""][0]
webbrowser.open(link)
| 102 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase: Union[str, Any] = qiskit.QuantumRegister(4 , 'qr' )
__lowerCAmelCase: List[Any] = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCAmelCase: Any = [input_a, input_a, carry_in]
__lowerCAmelCase: List[str] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(SCREAMING_SNAKE_CASE ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(SCREAMING_SNAKE_CASE ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(SCREAMING_SNAKE_CASE ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , SCREAMING_SNAKE_CASE ) # measure the last two qbits
__lowerCAmelCase: List[str] = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase: List[Any] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 322 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase( __UpperCamelCase : Optional[int] ,__UpperCamelCase : str ):
# Load checkpoint
lowerCAmelCase_ : Union[str, Any] = torch.load(__UpperCamelCase ,map_location='''cpu''' )
lowerCAmelCase_ : List[str] = chkpt['''model''']
# We have the base model one level deeper than the original XLM repository
lowerCAmelCase_ : Union[str, Any] = {}
for k, v in state_dict.items():
if "pred_layer" in k:
lowerCAmelCase_ : str = v
else:
lowerCAmelCase_ : List[str] = v
lowerCAmelCase_ : Tuple = chkpt['''params''']
lowerCAmelCase_ : Optional[Any] = {n: v for n, v in config.items() if not isinstance(__UpperCamelCase ,(torch.FloatTensor, numpy.ndarray) )}
lowerCAmelCase_ : List[str] = chkpt['''dico_word2id''']
lowerCAmelCase_ : List[Any] = {s + '''</w>''' if s.find('''@@''' ) == -1 and i > 13 else s.replace('''@@''' ,'''''' ): i for s, i in vocab.items()}
# Save pytorch-model
lowerCAmelCase_ : Optional[int] = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
lowerCAmelCase_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
lowerCAmelCase_ : Optional[Any] = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']
print(f"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(__UpperCamelCase ,__UpperCamelCase )
print(f"""Save configuration file to {pytorch_config_dump_path}""" )
with open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(__UpperCamelCase ,indent=2 ) + '''\n''' )
print(f"""Save vocab file to {pytorch_config_dump_path}""" )
with open(__UpperCamelCase ,'''w''' ,encoding='''utf-8''' ) as f:
f.write(json.dumps(__UpperCamelCase ,indent=2 ) + '''\n''' )
if __name__ == "__main__":
A__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
A__ : Tuple = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 103 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
def __init__( self : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : int=3 , UpperCAmelCase : int=4 , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=9_9 , UpperCAmelCase : Tuple=3_6 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]=3_7 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[str]=5_1_2 , UpperCAmelCase : int=1_6 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[Any]=6 , UpperCAmelCase : int=6 , UpperCAmelCase : str=3 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[str]=1_0_0_0 , ) -> int:
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: List[str] = batch_size
__lowerCAmelCase: Optional[Any] = num_channels
__lowerCAmelCase: Tuple = image_size
__lowerCAmelCase: str = patch_size
__lowerCAmelCase: List[str] = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Union[str, Any] = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: Dict = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: str = hidden_dropout_prob
__lowerCAmelCase: str = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: str = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: List[str] = coordinate_size
__lowerCAmelCase: Tuple = shape_size
__lowerCAmelCase: List[Any] = num_labels
__lowerCAmelCase: Any = num_choices
__lowerCAmelCase: List[str] = scope
__lowerCAmelCase: Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase: Optional[Any] = text_seq_length
__lowerCAmelCase: List[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase: int = self.text_seq_length + self.image_seq_length
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase: str = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: Optional[Any] = bbox[i, j, 3]
__lowerCAmelCase: Tuple = bbox[i, j, 1]
__lowerCAmelCase: Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Any = bbox[i, j, 2]
__lowerCAmelCase: int = bbox[i, j, 0]
__lowerCAmelCase: int = tmp_coordinate
__lowerCAmelCase: List[Any] = tf.constant(UpperCAmelCase )
__lowerCAmelCase: Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase: Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase: List[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase: int = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase: str = None
__lowerCAmelCase: Dict = None
if self.use_labels:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase: Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> int:
__lowerCAmelCase: Tuple = TFLayoutLMvaModel(config=UpperCAmelCase )
# text + image
__lowerCAmelCase: Dict = model(UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , training=UpperCAmelCase , )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase: str = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase: List[str] = model({'pixel_values': pixel_values} , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] ) -> int:
__lowerCAmelCase: List[str] = self.num_labels
__lowerCAmelCase: Tuple = TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int ) -> Any:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: List[str] = TFLayoutLMvaForTokenClassification(config=UpperCAmelCase )
__lowerCAmelCase: Any = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> Any:
__lowerCAmelCase: str = 2
__lowerCAmelCase: Dict = TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)): List[str] = config_and_inputs
__lowerCAmelCase: List[str] = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : Tuple = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Dict = False
_lowercase : Tuple = False
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> List[str]:
return True
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=False ) -> dict:
__lowerCAmelCase: Optional[Any] = copy.deepcopy(UpperCAmelCase )
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: int = {
k: tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase: Tuple = TFLayoutLMvaModelTester(self )
__lowerCAmelCase: str = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase: List[Any] = model_class(UpperCAmelCase )
if getattr(UpperCAmelCase , 'hf_compute_loss' , UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase: Optional[int] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: List[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=UpperCAmelCase )[0]
]
__lowerCAmelCase: Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase: Optional[Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Tuple = prepared_for_class.pop('input_ids' )
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase: Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCAmelCase: str = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase: Tuple = -1_0_0
__lowerCAmelCase: Union[str, Any] = tf.convert_to_tensor(UpperCAmelCase )
__lowerCAmelCase: Dict = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase: str = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase: Any = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase: Tuple = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase: Dict = inspect.signature(model.call ).parameters
__lowerCAmelCase: Dict = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase: str = {0: 'input_ids'}
for label_key in label_keys:
__lowerCAmelCase: Optional[Any] = signature_names.index(UpperCAmelCase )
__lowerCAmelCase: Tuple = label_key
__lowerCAmelCase: Tuple = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase: List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase: Optional[Any] = prepared_for_class[value]
__lowerCAmelCase: Union[str, Any] = tuple(UpperCAmelCase )
# Send to model
__lowerCAmelCase: Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCAmelCase ( self : Dict ) -> Tuple:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> int:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: Tuple = type
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : int ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Optional[int] = TFLayoutLMvaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : int ) -> Dict:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Any ) -> List[str]:
__lowerCAmelCase: Any = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCAmelCase: Tuple = self.default_image_processor
__lowerCAmelCase: str = prepare_img()
__lowerCAmelCase: Optional[int] = image_processor(images=UpperCAmelCase , return_tensors='tf' ).pixel_values
__lowerCAmelCase: Dict = tf.constant([[1, 2]] )
__lowerCAmelCase: str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase: List[str] = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
__lowerCAmelCase: Tuple = (1, 1_9_9, 7_6_8)
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase )
__lowerCAmelCase: str = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''VisionEncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''TFVisionEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''FlaxVisionEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 104 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : str=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=9_9 , UpperCAmelCase : Optional[int]=3_2 , UpperCAmelCase : Dict=5 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : Dict=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : int=0.02 , UpperCAmelCase : List[Any]=4 , ) -> Optional[Any]:
__lowerCAmelCase: str = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Optional[int] = seq_length
__lowerCAmelCase: Dict = is_training
__lowerCAmelCase: Optional[Any] = use_attention_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Optional[int] = use_labels
__lowerCAmelCase: Optional[Any] = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: List[str] = num_attention_heads
__lowerCAmelCase: int = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Optional[int] = max_position_embeddings
__lowerCAmelCase: Union[str, Any] = type_vocab_size
__lowerCAmelCase: int = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Optional[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Optional[int] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : Dict ) -> Any:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: Optional[Any] = config_and_inputs
__lowerCAmelCase: Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( snake_case__ , unittest.TestCase ):
_lowercase : Dict = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase: List[Any] = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
__lowerCAmelCase: Optional[Any] = model_class_name.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
@require_flax
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: List[Any] = FlaxAlbertModel.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
__lowerCAmelCase: str = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
a : Union[str, Any] = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( _lowercase : torch.nn.Module , _lowercase : BnbQuantizationConfig , _lowercase : Union[str, os.PathLike] = None , _lowercase : Optional[Dict[str, Union[int, str, torch.device]]] = None , _lowercase : Optional[List[str]] = None , _lowercase : Optional[Dict[Union[int, str], Union[int, str]]] = None , _lowercase : Optional[Union[str, os.PathLike]] = None , _lowercase : bool = False , ) ->Union[str, Any]:
'''simple docstring'''
a : Tuple = bnb_quantization_config.load_in_abit
a : int = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
"You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"
" make sure you have the latest version of `bitsandbytes` installed." )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
"You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"
"make sure you have the latest version of `bitsandbytes` installed." )
a : Tuple = []
# custom device map
if isinstance(_lowercase , _lowercase ) and len(device_map.keys() ) > 1:
a : List[str] = [key for key, value in device_map.items() if value in ["disk", "cpu"]]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
a : List[str] = get_keys_to_not_convert(_lowercase )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(_lowercase )
a : Optional[Any] = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
a : List[str] = []
a : List[Any] = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(_lowercase )
# compatibility with peft
a : List[str] = load_in_abit
a : Optional[int] = load_in_abit
a : Union[str, Any] = get_parameter_device(_lowercase )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
"It is not recommended to quantize a loaded model. "
"The model should be instantiated under the `init_empty_weights` context manager." )
a : Optional[int] = replace_with_bnb_layers(_lowercase , _lowercase , modules_to_not_convert=_lowercase )
# convert param to the right dtype
a : Optional[int] = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
a : Optional[int] = name.replace(".weight" , "" ).replace(".bias" , "" )
a : Any = getattr(_lowercase , _lowercase , _lowercase )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(_lowercase ):
param.to(_lowercase )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError("No GPU found. A GPU is needed for quantization." )
logger.info(
F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
"We move the model to cuda." )
return model
elif weights_location is None:
raise RuntimeError(
F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
a : int = replace_with_bnb_layers(
_lowercase , _lowercase , modules_to_not_convert=_lowercase )
a : Tuple = get_quantized_model_device_map(
_lowercase , _lowercase , _lowercase , max_memory=_lowercase , no_split_module_classes=_lowercase , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
a : List[str] = True
a : Tuple = any(x in list(device_map.values() ) for x in ["cpu", "disk"] )
load_checkpoint_in_model(
_lowercase , _lowercase , _lowercase , dtype=bnb_quantization_config.torch_dtype , offload_folder=_lowercase , offload_state_dict=_lowercase , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(_lowercase , device_map=_lowercase , offload_dir=_lowercase )
def _SCREAMING_SNAKE_CASE ( _lowercase : Dict , _lowercase : int , _lowercase : Union[str, Any]=None , _lowercase : Any=None , _lowercase : Union[str, Any]=None ) ->List[Any]:
'''simple docstring'''
if device_map is None:
if torch.cuda.is_available():
a : Union[str, Any] = {"": torch.cuda.current_device()}
else:
raise RuntimeError("No GPU found. A GPU is needed for quantization." )
logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`." )
if isinstance(_lowercase , _lowercase ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
"If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
"'sequential'." )
a : List[str] = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
a : Optional[Any] = {}
a : Optional[int] = special_dtypes
a : Union[str, Any] = no_split_module_classes
a : List[str] = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
a : Optional[int] = get_balanced_memory(
_lowercase , low_zero=(device_map == "balanced_low_0") , max_memory=_lowercase , **_lowercase , )
a : Any = max_memory
a : Dict = infer_auto_device_map(_lowercase , **_lowercase )
if isinstance(_lowercase , _lowercase ):
# check if don't have any quantized module on the cpu
a : List[str] = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
a : Optional[int] = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
"\n Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n these modules in `torch_dtype`, you need to pass a custom `device_map` to\n `load_and_quantize_model`. Check\n https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n for more details.\n " )
else:
logger.info(
"Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit" )
del device_map_without_some_modules
return device_map
def _SCREAMING_SNAKE_CASE ( _lowercase : Any , _lowercase : Tuple , _lowercase : Tuple=None , _lowercase : List[Any]=None ) ->int:
'''simple docstring'''
if modules_to_not_convert is None:
a : Union[str, Any] = []
a, a : Optional[Any] = _replace_with_bnb_layers(
_lowercase , _lowercase , _lowercase , _lowercase )
if not has_been_replaced:
logger.warning(
"You are loading your model in 8bit or 4bit but no linear modules were found in your model."
" this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."
" Please double check your model architecture, or submit an issue on github if you think this is"
" a bug." )
return model
def _SCREAMING_SNAKE_CASE ( _lowercase : Dict , _lowercase : Dict , _lowercase : List[Any]=None , _lowercase : str=None , ) ->Optional[int]:
'''simple docstring'''
a : Tuple = False
for name, module in model.named_children():
if current_key_name is None:
a : Tuple = []
current_key_name.append(_lowercase )
if isinstance(_lowercase , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
a : Union[str, Any] = ".".join(_lowercase )
a : Optional[Any] = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
a : str = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
a : Union[str, Any] = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=_lowercase , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
a : Union[str, Any] = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError("load_in_8bit and load_in_4bit can't be both False" )
a : Union[str, Any] = module.weight.data
if module.bias is not None:
a : List[str] = module.bias.data
bnb_module.requires_grad_(_lowercase )
setattr(_lowercase , _lowercase , _lowercase )
a : Optional[int] = True
if len(list(module.children() ) ) > 0:
a, a : Union[str, Any] = _replace_with_bnb_layers(
_lowercase , _lowercase , _lowercase , _lowercase )
a : str = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] ) ->Union[str, Any]:
'''simple docstring'''
with init_empty_weights():
a : Dict = deepcopy(_lowercase ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
a : int = find_tied_parameters(_lowercase )
# For compatibility with Accelerate < 0.18
if isinstance(_lowercase , _lowercase ):
a : Dict = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
a : Union[str, Any] = sum(_lowercase , [] )
a : Any = len(_lowercase ) > 0
# Check if it is a base model
a : int = False
if hasattr(_lowercase , "base_model_prefix" ):
a : List[Any] = not hasattr(_lowercase , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
a : Optional[Any] = list(model.named_children() )
a : Dict = [list_modules[-1][0]]
# add last module together with tied weights
a : str = set(_lowercase ) - set(_lowercase )
a : Optional[Any] = list(set(_lowercase ) ) + list(_lowercase )
# remove ".weight" from the keys
a : Optional[int] = [".weight", ".bias"]
a : List[Any] = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
a : Dict = name.replace(_lowercase , "" )
filtered_module_names.append(_lowercase )
return filtered_module_names
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->Optional[int]:
'''simple docstring'''
for m in model.modules():
if isinstance(_lowercase , bnb.nn.Linearabit ):
return True
return False
def _SCREAMING_SNAKE_CASE ( _lowercase : nn.Module ) ->Union[str, Any]:
'''simple docstring'''
return next(parameter.parameters() ).device
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] , _lowercase : Any , _lowercase : Optional[Any] , _lowercase : str , _lowercase : str , _lowercase : Union[str, Any] , _lowercase : List[str] ) ->Dict:
'''simple docstring'''
if fpaa_statistics is None:
set_module_tensor_to_device(_lowercase , _lowercase , 0 , dtype=_lowercase , value=_lowercase )
a : str = param_name
a : int = model
if "." in tensor_name:
a : str = tensor_name.split("." )
for split in splits[:-1]:
a : Tuple = getattr(_lowercase , _lowercase )
if new_module is None:
raise ValueError(F"""{module} has no attribute {split}.""" )
a : Dict = new_module
a : Any = splits[-1]
# offload weights
a : Dict = False
offload_weight(module._parameters[tensor_name] , _lowercase , _lowercase , index=_lowercase )
if hasattr(module._parameters[tensor_name] , "SCB" ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace("weight" , "SCB" ) , _lowercase , index=_lowercase , )
else:
offload_weight(_lowercase , _lowercase , _lowercase , index=_lowercase )
offload_weight(_lowercase , param_name.replace("weight" , "SCB" ) , _lowercase , index=_lowercase )
set_module_tensor_to_device(_lowercase , _lowercase , "meta" , dtype=_lowercase , value=torch.empty(*param.size() ) )
| 105 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "timm_backbone"
def __init__( self : List[str] ,lowercase_ : Any=None ,lowercase_ : int=3 ,lowercase_ : List[Any]=True ,lowercase_ : str=True ,lowercase_ : Union[str, Any]=None ,**lowercase_ : List[Any] ,):
super().__init__(**lowercase_ )
lowerCAmelCase__ : Union[str, Any] = backbone
lowerCAmelCase__ : Optional[int] = num_channels
lowerCAmelCase__ : Optional[int] = features_only
lowerCAmelCase__ : Any = use_pretrained_backbone
lowerCAmelCase__ : Tuple = True
lowerCAmelCase__ : str = out_indices if out_indices is not None else (-1,)
| 106 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_a = [None] * 1_0_0_0_0_0_0_0
_a = True
_a = False
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCAmelCase: int = chain(next_number(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = number_chain
while number < 10_00_00_00:
__lowerCAmelCase: Dict = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE : int = 10_00_00_00 ) -> int:
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| 322 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class snake_case__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[Any]=13 , __lowerCamelCase : Union[str, Any]=7 , __lowerCamelCase : List[str]=True , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Tuple=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : str=99 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Optional[int]=5 , __lowerCamelCase : List[Any]=4 , __lowerCamelCase : Optional[int]=37 , __lowerCamelCase : List[str]="gelu" , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : List[Any]=0.1 , __lowerCamelCase : List[str]=5_12 , __lowerCamelCase : Any=16 , __lowerCamelCase : Tuple=2 , __lowerCamelCase : Optional[Any]=0.02 , __lowerCamelCase : List[Any]=3 , __lowerCamelCase : Optional[Any]=4 , __lowerCamelCase : str=None , ) -> int:
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_input_mask
a = use_token_type_ids
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_act
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = type_vocab_size
a = type_sequence_label_size
a = initializer_range
a = num_labels
a = num_choices
a = scope
a = vocab_size - 1
def __UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = None
if self.use_input_mask:
a = random_attention_mask([self.batch_size, self.seq_length] )
a = None
if self.use_labels:
a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a = self.get_config()
return config, input_ids, input_mask, token_labels
def __UpperCAmelCase ( self : Union[str, Any] ) -> str:
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowerCamelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def __UpperCAmelCase ( self : str ) -> Any:
a , a , a , a = self.prepare_config_and_inputs()
a = True
return config, input_ids, input_mask, token_labels
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[Any] ) -> Optional[int]:
a = GPTNeoXModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase )
a = model(__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str ) -> Dict:
a = True
a = GPTNeoXModel(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : Optional[Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Dict ) -> str:
a = GPTNeoXForCausalLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : Dict , __lowerCamelCase : Any , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] ) -> str:
a = self.num_labels
a = GPTNeoXForQuestionAnswering(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[Any] ) -> Union[str, Any]:
a = self.num_labels
a = GPTNeoXForSequenceClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : List[str] , __lowerCamelCase : Any , __lowerCamelCase : Tuple , __lowerCamelCase : Dict ) -> Dict:
a = self.num_labels
a = GPTNeoXForTokenClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCAmelCase ( self : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple , __lowerCamelCase : Dict ) -> List[Any]:
a = True
a = GPTNeoXForCausalLM(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
# first forward pass
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , use_cache=__lowerCamelCase )
a = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
a = torch.cat([input_ids, next_tokens] , dim=-1 )
a = torch.cat([input_mask, next_mask] , dim=-1 )
a = model(__lowerCamelCase , attention_mask=__lowerCamelCase , output_hidden_states=__lowerCamelCase )
a = output_from_no_past["hidden_states"][0]
a = model(
__lowerCamelCase , attention_mask=__lowerCamelCase , past_key_values=__lowerCamelCase , output_hidden_states=__lowerCamelCase , )["hidden_states"][0]
# select random slice
a = ids_tensor((1,) , output_from_past.shape[-1] ).item()
a = output_from_no_past[:, -3:, random_slice_idx].detach()
a = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 ) )
def __UpperCAmelCase ( self : Dict ) -> List[Any]:
a = self.prepare_config_and_inputs()
a , a , a , a = config_and_inputs
a = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class snake_case__ (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : List[Any] = (GPTNeoXForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE_ : List[str] = (
{
"""feature-extraction""": GPTNeoXModel,
"""question-answering""": GPTNeoXForQuestionAnswering,
"""text-classification""": GPTNeoXForSequenceClassification,
"""text-generation""": GPTNeoXForCausalLM,
"""token-classification""": GPTNeoXForTokenClassification,
"""zero-shot""": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : List[Any] = False
def __UpperCAmelCase ( self : List[str] ) -> Optional[int]:
a = GPTNeoXModelTester(self )
a = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=64 , num_attention_heads=8 )
def __UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : Dict ) -> Dict:
a , a , a , a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Tuple ) -> Tuple:
a , a , a , a = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] ) -> int:
# This regression test was failing with PyTorch < 1.3
a , a , a , a = self.model_tester.prepare_config_and_inputs_for_decoder()
a = None
self.model_tester.create_and_check_model_as_decoder(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : List[Any] ) -> List[Any]:
a , a , a , a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def __UpperCAmelCase ( self : str ) -> List[Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )
def __UpperCAmelCase ( self : str ) -> Union[str, Any]:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )
def __UpperCAmelCase ( self : int ) -> str:
a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase )
@unittest.skip(reason="Feed forward chunking is not implemented" )
def __UpperCAmelCase ( self : List[Any] ) -> Any:
pass
@parameterized.expand([("linear",), ("dynamic",)] )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Union[str, Any] ) -> str:
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = ids_tensor([1, 10] , config.vocab_size )
a = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
a = GPTNeoXModel(__lowerCamelCase )
original_model.to(__lowerCamelCase )
original_model.eval()
a = original_model(__lowerCamelCase ).last_hidden_state
a = original_model(__lowerCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
a = {"type": scaling_type, "factor": 10.0}
a = GPTNeoXModel(__lowerCamelCase )
scaled_model.to(__lowerCamelCase )
scaled_model.eval()
a = scaled_model(__lowerCamelCase ).last_hidden_state
a = scaled_model(__lowerCamelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-5 ) )
@require_torch
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def __UpperCAmelCase ( self : Any ) -> List[Any]:
a = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped" )
for checkpointing in [True, False]:
a = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(__lowerCamelCase )
a = tokenizer("My favorite food is" , return_tensors="pt" ).to(__lowerCamelCase )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
a = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
a = model.generate(**__lowerCamelCase , do_sample=__lowerCamelCase , max_new_tokens=20 )
a = tokenizer.batch_decode(__lowerCamelCase )[0]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
| 107 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCAmelCase__ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = '''cpu'''
lowerCAmelCase__ = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
lowerCAmelCase__ = '''path-to-your-trained-model'''
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
lowerCAmelCase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCAmelCase__ = pipe.to(device)
# to channels last
lowerCAmelCase__ = pipe.unet.to(memory_format=torch.channels_last)
lowerCAmelCase__ = pipe.vae.to(memory_format=torch.channels_last)
lowerCAmelCase__ = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
lowerCAmelCase__ = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
lowerCAmelCase__ = torch.randn(2, 4, 64, 64)
lowerCAmelCase__ = torch.rand(1) * 999
lowerCAmelCase__ = torch.randn(2, 77, 768)
lowerCAmelCase__ = (sample, timestep, encoder_hidden_status)
try:
lowerCAmelCase__ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
lowerCAmelCase__ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase__ = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase__ = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
lowerCAmelCase__ = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
lowerCAmelCase__ = 666
lowerCAmelCase__ = torch.Generator(device).manual_seed(seed)
lowerCAmelCase__ = {'''generator''': generator}
if args.steps is not None:
lowerCAmelCase__ = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
lowerCAmelCase__ = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 108 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str=1_3 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : str=True , UpperCAmelCase : Any=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : int=False , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Any=9_9 , UpperCAmelCase : str=0 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : int=5 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : int=5_1_2 , UpperCAmelCase : str=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict="last" , UpperCAmelCase : int=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Union[str, Any]=0 , ) -> Dict:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Tuple = seq_length
__lowerCAmelCase: Tuple = is_training
__lowerCAmelCase: Optional[Any] = use_input_lengths
__lowerCAmelCase: List[str] = use_token_type_ids
__lowerCAmelCase: Dict = use_labels
__lowerCAmelCase: int = gelu_activation
__lowerCAmelCase: Optional[int] = sinusoidal_embeddings
__lowerCAmelCase: Tuple = causal
__lowerCAmelCase: Optional[Any] = asm
__lowerCAmelCase: int = n_langs
__lowerCAmelCase: Tuple = vocab_size
__lowerCAmelCase: List[Any] = n_special
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: int = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Dict = max_position_embeddings
__lowerCAmelCase: List[str] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: List[str] = num_labels
__lowerCAmelCase: List[str] = num_choices
__lowerCAmelCase: Optional[int] = summary_type
__lowerCAmelCase: Any = use_proj
__lowerCAmelCase: Optional[Any] = scope
__lowerCAmelCase: Dict = bos_token_id
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Any = None
if self.use_input_lengths:
__lowerCAmelCase: Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowerCAmelCase: str = None
if self.use_token_type_ids:
__lowerCAmelCase: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: Optional[int] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
__lowerCAmelCase: str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase: Dict = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: List[str] = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , ) -> int:
__lowerCAmelCase: str = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Dict , ) -> List[str]:
__lowerCAmelCase: Dict = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: str = model(UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
__lowerCAmelCase: Any = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((__lowerCAmelCase) , ): List[str] = result_with_labels.to_tuple()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((__lowerCAmelCase) , ): List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , ) -> List[Any]:
__lowerCAmelCase: Optional[Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = model(UpperCAmelCase )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
__lowerCAmelCase: List[Any] = self.num_choices
__lowerCAmelCase: Optional[Any] = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Tuple ) -> int:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCAmelCase: Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple=False ) -> Dict:
__lowerCAmelCase: Optional[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__lowerCAmelCase: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: int = XLMModelTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=3_7 )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__lowerCAmelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] ) -> int:
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : Dict=1 ) -> Dict:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: int = min_length + idx + 1
__lowerCAmelCase: Union[str, Any] = min_length + idx + 1
__lowerCAmelCase: Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: Any = min_length + idx + 1
__lowerCAmelCase: str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def UpperCAmelCase ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: List[Any] = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=UpperCAmelCase ) # the president
__lowerCAmelCase: Union[str, Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__lowerCAmelCase: str = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 322 | 0 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def _snake_case ( UpperCamelCase : Dataset , UpperCamelCase : Dict[str, str] ):
UpperCAmelCase : Any = args.log_outputs
UpperCAmelCase : Any = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCAmelCase : List[Any] = load_metric("""wer""" )
UpperCAmelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCAmelCase : int = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
UpperCAmelCase : str = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
UpperCAmelCase : Tuple = F"WER: {wer_result}\nCER: {cer_result}"
print(UpperCamelCase )
with open(F"{dataset_id}_eval_results.txt" , """w""" ) as f:
f.write(UpperCamelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCAmelCase : str = F"log_{dataset_id}_predictions.txt"
UpperCAmelCase : Tuple = F"log_{dataset_id}_targets.txt"
with open(UpperCamelCase , """w""" ) as p, open(UpperCamelCase , """w""" ) as t:
# mapping function to write output
def write_to_file(UpperCamelCase : List[Any] , UpperCamelCase : List[Any] ):
p.write(F"{i}" + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F"{i}" + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(UpperCamelCase , with_indices=UpperCamelCase )
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : List[str] = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCAmelCase : Dict = re.sub(UpperCamelCase , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCAmelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCAmelCase : Optional[Any] = """ """.join(text.split(UpperCamelCase ) )
return text
def _snake_case ( UpperCamelCase : Tuple ):
# load dataset
UpperCAmelCase : Union[str, Any] = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=UpperCamelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCAmelCase : Any = feature_extractor.sampling_rate
# resample audio
UpperCAmelCase : List[str] = dataset.cast_column("""audio""" , Audio(sampling_rate=UpperCamelCase ) )
# load eval pipeline
if args.device is None:
UpperCAmelCase : Optional[int] = 0 if torch.cuda.is_available() else -1
UpperCAmelCase : Tuple = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(UpperCamelCase : Any ):
UpperCAmelCase : Any = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
UpperCAmelCase : Tuple = prediction["""text"""]
UpperCAmelCase : List[str] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCAmelCase : int = dataset.map(UpperCamelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(UpperCamelCase , UpperCamelCase )
if __name__ == "__main__":
A: List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
A: Union[str, Any] = parser.parse_args()
main(args)
| 109 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) <= 1:
return arr, 0
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE ) // 2
__lowerCAmelCase: str = arr[0:mid]
__lowerCAmelCase: int = arr[mid:]
__lowerCAmelCase , __lowerCAmelCase: List[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: int = _count_cross_inversions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: List[str] = 0
while i < len(SCREAMING_SNAKE_CASE ) and j < len(SCREAMING_SNAKE_CASE ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(SCREAMING_SNAKE_CASE ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: str = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# an empty list should also have zero inversions
__lowerCAmelCase: int = []
__lowerCAmelCase: Any = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2.76,
'M': 2.41,
'W': 2.36,
'F': 2.23,
'G': 2.02,
'Y': 1.97,
'P': 1.93,
'B': 1.29,
'V': 0.98,
'K': 0.77,
'J': 0.15,
'X': 0.15,
'Q': 0.10,
'Z': 0.07,
}
a_ = 'ETAOINSHRDLCUMWFGYPBVKJXQZ'
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict ={letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def _a( UpperCamelCase__ : tuple ):
'''simple docstring'''
return x[0]
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =get_letter_count(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : dict[int, list[str]] ={
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : dict[int, str] ={}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find, reverse=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =''.join(freq_to_letter[freq] )
SCREAMING_SNAKE_CASE__ : Any =list(freq_to_letter_str.items() )
freq_pairs.sort(key=UpperCamelCase__, reverse=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : list[str] =[freq_pair[1] for freq_pair in freq_pairs]
return "".join(UpperCamelCase__ )
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =get_frequency_order(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod() | 152 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ : int = 0
lowercase__ : Tuple = len(UpperCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ : Tuple = i + 1
else:
lowercase__ : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 198 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A =logging.get_logger(__name__)
__A ={
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class _SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ ):
lowerCAmelCase__ = 'nat'
lowerCAmelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , lowercase=4 , lowercase=3 , lowercase=64 , lowercase=[3, 4, 6, 5] , lowercase=[2, 4, 8, 16] , lowercase=7 , lowercase=3.0 , lowercase=True , lowercase=0.0 , lowercase=0.0 , lowercase=0.1 , lowercase="gelu" , lowercase=0.0_2 , lowercase=1e-5 , lowercase=0.0 , lowercase=None , lowercase=None , **lowercase , ) -> Any:
super().__init__(**lowercase )
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = embed_dim
lowerCamelCase_ = depths
lowerCamelCase_ = len(lowercase )
lowerCamelCase_ = num_heads
lowerCamelCase_ = kernel_size
lowerCamelCase_ = mlp_ratio
lowerCamelCase_ = qkv_bias
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = drop_path_rate
lowerCamelCase_ = hidden_act
lowerCamelCase_ = layer_norm_eps
lowerCamelCase_ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCamelCase_ = int(embed_dim * 2 ** (len(lowercase ) - 1) )
lowerCamelCase_ = layer_scale_init_value
lowerCamelCase_ = ['stem'] + [f'stage{idx}' for idx in range(1 , len(lowercase ) + 1 )]
lowerCamelCase_ = get_aligned_output_features_output_indices(
out_features=lowercase , out_indices=lowercase , stage_names=self.stage_names )
| 19 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.environ['GITHUB_TOKEN'] )
__lowerCAmelCase: Tuple = g.get_repo('huggingface/accelerate' )
__lowerCAmelCase: str = repo.get_issues(state='open' )
for issue in open_issues:
__lowerCAmelCase: Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda SCREAMING_SNAKE_CASE : i.created_at , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = comments[0] if len(SCREAMING_SNAKE_CASE ) > 0 else None
__lowerCAmelCase: Tuple = dt.utcnow()
__lowerCAmelCase: Optional[int] = (current_time - issue.updated_at).days
__lowerCAmelCase: str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 322 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_lowerCAmelCase : Tuple = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
"SpeechT5Config",
"SpeechT5HifiGanConfig",
],
"feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
"processing_speecht5": ["SpeechT5Processor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Optional[Any] = ["SpeechT5Tokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : str = [
"SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"SpeechT5ForSpeechToText",
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForTextToSpeech",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
"SpeechT5HifiGan",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 169 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 322 | 0 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
for i in range(n - 1 ):
for j in range(i + 1, __lowerCamelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def A__ ( __lowerCamelCase ):
if len(__lowerCamelCase ) <= 1:
return arr, 0
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase ) // 2
SCREAMING_SNAKE_CASE_ = arr[0:mid]
SCREAMING_SNAKE_CASE_ = arr[mid:]
SCREAMING_SNAKE_CASE_ = count_inversions_recursive(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = count_inversions_recursive(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = _count_cross_inversions(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 0
while i < len(__lowerCamelCase ) and j < len(__lowerCamelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__lowerCamelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__lowerCamelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def A__ ( ):
SCREAMING_SNAKE_CASE_ = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
SCREAMING_SNAKE_CASE_ = count_inversions_bf(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = count_inversions_recursive(__lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''', __lowerCamelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
SCREAMING_SNAKE_CASE_ = count_inversions_bf(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = count_inversions_recursive(__lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', __lowerCamelCase )
# an empty list should also have zero inversions
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = count_inversions_bf(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = count_inversions_recursive(__lowerCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', __lowerCamelCase )
if __name__ == "__main__":
main()
| 299 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Tuple , UpperCAmelCase : float , UpperCAmelCase : Callable , UpperCAmelCase : int , UpperCAmelCase : float = 1.0 , UpperCAmelCase : str = None , ) -> Union[str, Any]:
super().__init__()
__lowerCAmelCase: Optional[Any] = initial_learning_rate
__lowerCAmelCase: str = warmup_steps
__lowerCAmelCase: Optional[int] = power
__lowerCAmelCase: str = decay_schedule_fn
__lowerCAmelCase: Tuple = name
def __call__( self : int , UpperCAmelCase : Dict ) -> Optional[int]:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCAmelCase: List[str] = tf.cast(UpperCAmelCase , tf.floataa )
__lowerCAmelCase: Tuple = tf.cast(self.warmup_steps , tf.floataa )
__lowerCAmelCase: List[str] = global_step_float / warmup_steps_float
__lowerCAmelCase: List[str] = self.initial_learning_rate * tf.math.pow(UpperCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=UpperCAmelCase , )
def UpperCAmelCase ( self : Tuple ) -> int:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 0.9 , SCREAMING_SNAKE_CASE : float = 0.9_9_9 , SCREAMING_SNAKE_CASE : float = 1E-8 , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 , SCREAMING_SNAKE_CASE : Optional[List[str]] = None , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=SCREAMING_SNAKE_CASE , )
if num_warmup_steps:
__lowerCAmelCase: Optional[int] = WarmUp(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_schedule_fn=SCREAMING_SNAKE_CASE , warmup_steps=SCREAMING_SNAKE_CASE , )
if weight_decay_rate > 0.0:
__lowerCAmelCase: List[Any] = AdamWeightDecay(
learning_rate=SCREAMING_SNAKE_CASE , weight_decay_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase: Dict = tf.keras.optimizers.Adam(
learning_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , UpperCAmelCase : float = 0.9 , UpperCAmelCase : float = 0.999 , UpperCAmelCase : float = 1E-7 , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : str = "AdamWeightDecay" , **UpperCAmelCase : str , ) -> int:
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
__lowerCAmelCase: List[Any] = weight_decay_rate
__lowerCAmelCase: List[str] = include_in_weight_decay
__lowerCAmelCase: Optional[Any] = exclude_from_weight_decay
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Tuple ) -> Optional[int]:
__lowerCAmelCase: Union[str, Any] = {'WarmUp': WarmUp}
return super(UpperCAmelCase , cls ).from_config(UpperCAmelCase , custom_objects=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
super(UpperCAmelCase , self )._prepare_local(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> List[str]:
__lowerCAmelCase: Dict = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None , **UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase: Tuple = list(zip(*UpperCAmelCase ) )
return super(UpperCAmelCase , self ).apply_gradients(zip(UpperCAmelCase , UpperCAmelCase ) , name=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCAmelCase: Dict = apply_state or {}
__lowerCAmelCase: Union[str, Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCAmelCase: str = self._fallback_apply_state(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Tuple = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any]=None ) -> List[Any]:
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_dense(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[Any]=None ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase: Any = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: str = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_sparse(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase: List[str] = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return False
return True
class A_ ( snake_case__ ):
def __init__( self : int ) -> List[Any]:
__lowerCAmelCase: Tuple = []
__lowerCAmelCase: int = None
@property
def UpperCAmelCase ( self : Dict ) -> List[Any]:
if self._accum_steps is None:
__lowerCAmelCase: List[Any] = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Optional[Any] , UpperCAmelCase : Any ) -> Any:
if not self._gradients:
__lowerCAmelCase: Any = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(UpperCAmelCase ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(UpperCAmelCase ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(UpperCAmelCase )}''' )
for accum_gradient, gradient in zip(self._gradients , UpperCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(UpperCAmelCase )
self._accum_steps.assign_add(1 )
def UpperCAmelCase ( self : int ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(UpperCAmelCase ) )
| 322 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class _a ( snake_case__ ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
__a : str = field(default="""summarization""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__a : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__a : ClassVar[Features] = Features({"""summary""": Value("""string""" )} )
__a : str = "text"
__a : str = "summary"
@property
def A ( self : Tuple ):
'''simple docstring'''
return {self.text_column: "text", self.summary_column: "summary"}
| 34 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any]=[] ) -> str:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = size[0] - overlap_pixels * 2
__lowerCAmelCase: str = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__lowerCAmelCase: Any = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
__lowerCAmelCase: int = np.pad(SCREAMING_SNAKE_CASE , mode='linear_ramp' , pad_width=SCREAMING_SNAKE_CASE , end_values=0 )
if "l" in remove_borders:
__lowerCAmelCase: Dict = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__lowerCAmelCase: Tuple = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__lowerCAmelCase: List[Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__lowerCAmelCase: List[str] = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
return max(SCREAMING_SNAKE_CASE , min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] ) -> int:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : [int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = list(SCREAMING_SNAKE_CASE )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__lowerCAmelCase: int = clamp_rect(SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase: List[Any] = Image.new('RGB' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(SCREAMING_SNAKE_CASE , (original_slice, 0) )
return result
def _a ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__lowerCAmelCase: List[Any] = tile.crop(SCREAMING_SNAKE_CASE )
return tile
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = n % d
return n - divisor
class A_ ( snake_case__ ):
def __init__( self : Optional[Any] , UpperCAmelCase : AutoencoderKL , UpperCAmelCase : CLIPTextModel , UpperCAmelCase : CLIPTokenizer , UpperCAmelCase : UNetaDConditionModel , UpperCAmelCase : DDPMScheduler , UpperCAmelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase : int = 3_5_0 , ) -> Optional[Any]:
super().__init__(
vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , low_res_scheduler=UpperCAmelCase , scheduler=UpperCAmelCase , max_noise_level=UpperCAmelCase , )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ) -> Optional[int]:
torch.manual_seed(0 )
__lowerCAmelCase: Optional[int] = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__lowerCAmelCase: Optional[Any] = add_overlap_rect(UpperCAmelCase , UpperCAmelCase , image.size )
__lowerCAmelCase: Any = image.crop(UpperCAmelCase )
__lowerCAmelCase: Any = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__lowerCAmelCase: Tuple = translated_slice_x - (original_image_slice / 2)
__lowerCAmelCase: Union[str, Any] = max(0 , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = squeeze_tile(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = to_input.size
__lowerCAmelCase: List[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__lowerCAmelCase: int = super(UpperCAmelCase , self ).__call__(image=UpperCAmelCase , **UpperCAmelCase ).images[0]
__lowerCAmelCase: Dict = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Union[str, Any] = unsqueeze_tile(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Optional[int] = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
__lowerCAmelCase: int = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=UpperCAmelCase ) , mode='L' , )
final_image.paste(
UpperCAmelCase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , UpperCAmelCase )
@torch.no_grad()
def __call__( self : Optional[Any] , UpperCAmelCase : Union[str, List[str]] , UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , UpperCAmelCase : int = 7_5 , UpperCAmelCase : float = 9.0 , UpperCAmelCase : int = 5_0 , UpperCAmelCase : Optional[Union[str, List[str]]] = None , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[torch.Generator] = None , UpperCAmelCase : Optional[torch.FloatTensor] = None , UpperCAmelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 1_2_8 , UpperCAmelCase : int = 3_2 , UpperCAmelCase : int = 3_2 , ) -> str:
__lowerCAmelCase: List[Any] = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
__lowerCAmelCase: str = math.ceil(image.size[0] / tile_size )
__lowerCAmelCase: List[Any] = math.ceil(image.size[1] / tile_size )
__lowerCAmelCase: Optional[Any] = tcx * tcy
__lowerCAmelCase: Tuple = 0
for y in range(UpperCAmelCase ):
for x in range(UpperCAmelCase ):
self._process_tile(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , prompt=UpperCAmelCase , num_inference_steps=UpperCAmelCase , guidance_scale=UpperCAmelCase , noise_level=UpperCAmelCase , negative_prompt=UpperCAmelCase , num_images_per_prompt=UpperCAmelCase , eta=UpperCAmelCase , generator=UpperCAmelCase , latents=UpperCAmelCase , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: Any = 'stabilityai/stable-diffusion-x4-upscaler'
__lowerCAmelCase: Dict = StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE , revision='fp16' , torch_dtype=torch.floataa )
__lowerCAmelCase: Optional[Any] = pipe.to('cuda' )
__lowerCAmelCase: Tuple = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(SCREAMING_SNAKE_CASE : Tuple ):
print(f'''progress: {obj['progress']:.4f}''' )
obj["image"].save('diffusers_library_progress.jpg' )
__lowerCAmelCase: str = pipe(image=SCREAMING_SNAKE_CASE , prompt='Black font, white background, vector' , noise_level=40 , callback=SCREAMING_SNAKE_CASE )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
_UpperCamelCase = 10
def a_ ( _lowerCAmelCase ) -> list[int]:
__lowerCamelCase : Optional[Any] = 1
__lowerCamelCase : str = max(_lowerCAmelCase )
while placement <= max_digit:
# declare and initialize empty buckets
__lowerCamelCase : list[list] = [[] for _ in range(_lowerCAmelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
__lowerCamelCase : str = int((i / placement) % RADIX )
buckets[tmp].append(_lowerCAmelCase )
# put each buckets' contents into list_of_ints
__lowerCamelCase : str = 0
for b in range(_lowerCAmelCase ):
for i in buckets[b]:
__lowerCamelCase : List[str] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 208 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowerCAmelCase: Tuple = True
for i in range(1 , s + 1 ):
__lowerCAmelCase: Any = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowerCAmelCase: Optional[int] = dp[i][j - 1]
if arr[i - 1] <= j:
__lowerCAmelCase: Union[str, Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowerCAmelCase: Tuple = s - 2 * j
break
return diff
| 322 | 0 |
from __future__ import annotations
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('''daily_interest_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * daily_interest_rate * days_between_payments
def a__ ( snake_case , snake_case , snake_case , ):
"""simple docstring"""
if number_of_compounding_periods <= 0:
raise ValueError('''number_of_compounding_periods must be > 0''' )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError('''nominal_annual_interest_rate_percentage must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def a__ ( snake_case , snake_case , snake_case , ):
"""simple docstring"""
if number_of_years <= 0:
raise ValueError('''number_of_years must be > 0''' )
if nominal_annual_percentage_rate < 0:
raise ValueError('''nominal_annual_percentage_rate must be >= 0''' )
if principal <= 0:
raise ValueError('''principal must be > 0''' )
return compound_interest(
snake_case , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 303 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__lowerCAmelCase: Tuple = i + 1
else:
__lowerCAmelCase: List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 1_1, 1_5], 9) = }")
| 322 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
snake_case_ = logging.get_logger(__name__)
snake_case_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
snake_case_ = {
"""vocab_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"""
},
"""merges_file""": {
"""allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"""
},
}
snake_case_ = {"""allegro/herbert-base-cased""": 514}
snake_case_ = {}
class A_ ( snake_case__ ):
"""simple docstring"""
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = HerbertTokenizer
def __init__( self :Optional[Any] , lowercase_ :Optional[int]=None , lowercase_ :Dict=None , lowercase_ :Optional[int]=None , lowercase_ :List[Any]="<s>" , lowercase_ :Optional[Any]="<unk>" , lowercase_ :Optional[int]="<pad>" , lowercase_ :Tuple="<mask>" , lowercase_ :List[Any]="</s>" , **lowercase_ :Union[str, Any] , ) -> int:
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , cls_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , mask_token=lowercase_ , sep_token=lowercase_ , **lowercase_ , )
def UpperCAmelCase__ ( self :List[Any] , lowercase_ :List[int] , lowercase_ :Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase = [self.cls_token_id]
UpperCAmelCase = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__ ( self :List[str] , lowercase_ :List[int] , lowercase_ :Optional[List[int]] = None , lowercase_ :bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_ )
if token_ids_a is None:
return [1] + ([0] * len(lowercase_ )) + [1]
return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ )) + [1]
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :List[int] , lowercase_ :Optional[List[int]] = None ) -> List[int]:
UpperCAmelCase = [self.sep_token_id]
UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :str , lowercase_ :Optional[str] = None ) -> Tuple[str]:
UpperCAmelCase = self._tokenizer.model.save(lowercase_ , name=lowercase_ )
return tuple(lowercase_ )
| 78 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_a = '''scheduler_config.json'''
class A_ ( snake_case__ ):
_lowercase : Optional[Any] = 1
_lowercase : Tuple = 2
_lowercase : Dict = 3
_lowercase : int = 4
_lowercase : Optional[Any] = 5
@dataclass
class A_ ( snake_case__ ):
_lowercase : jnp.ndarray
class A_ :
_lowercase : Optional[int] = SCHEDULER_CONFIG_NAME
_lowercase : Dict = ['dtype']
_lowercase : int = []
_lowercase : Union[str, Any] = True
@classmethod
def UpperCAmelCase ( cls : Union[str, Any] , UpperCAmelCase : Dict[str, Any] = None , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : List[str]=False , **UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCAmelCase , subfolder=UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase , )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.from_config(UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase )
if hasattr(UpperCAmelCase , 'create_state' ) and getattr(UpperCAmelCase , 'has_state' , UpperCAmelCase ):
__lowerCAmelCase: Dict = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, os.PathLike] , UpperCAmelCase : bool = False , **UpperCAmelCase : Any ) -> List[str]:
self.save_config(save_directory=UpperCAmelCase , push_to_hub=UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : str ) -> Dict:
return self._get_compatibles()
@classmethod
def UpperCAmelCase ( cls : Optional[int] ) -> Any:
__lowerCAmelCase: Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
__lowerCAmelCase: Dict = importlib.import_module(__name__.split('.' )[0] )
__lowerCAmelCase: Dict = [
getattr(UpperCAmelCase , UpperCAmelCase ) for c in compatible_classes_str if hasattr(UpperCAmelCase , UpperCAmelCase )
]
return compatible_classes
def _a ( SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : Tuple[int] ) -> jnp.ndarray:
"""simple docstring"""
assert len(SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE ) - x.ndim) ) , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any=0.9_9_9 , SCREAMING_SNAKE_CASE : List[Any]=jnp.floataa ) -> jnp.ndarray:
"""simple docstring"""
def alpha_bar(SCREAMING_SNAKE_CASE : str ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowerCAmelCase: str = []
for i in range(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Union[str, Any] = i / num_diffusion_timesteps
__lowerCAmelCase: List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE ) / alpha_bar(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) )
return jnp.array(SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class A_ :
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Optional[int] ) -> Any:
__lowerCAmelCase: str = scheduler.config
if config.trained_betas is not None:
__lowerCAmelCase: Tuple = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowerCAmelCase: Any = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCAmelCase: List[Any] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCAmelCase: str = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
__lowerCAmelCase: Optional[Any] = 1.0 - betas
__lowerCAmelCase: Optional[Any] = jnp.cumprod(UpperCAmelCase , axis=0 )
return cls(
alphas=UpperCAmelCase , betas=UpperCAmelCase , alphas_cumprod=UpperCAmelCase , )
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = state.alphas_cumprod
__lowerCAmelCase: str = alphas_cumprod[timesteps] ** 0.5
__lowerCAmelCase: Any = sqrt_alpha_prod.flatten()
__lowerCAmelCase: Any = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
__lowerCAmelCase: Any = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCAmelCase: str = sqrt_one_minus_alpha_prod.flatten()
__lowerCAmelCase: str = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Tuple = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 322 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : float , UpperCamelCase__ : Callable , UpperCamelCase__ : int , UpperCamelCase__ : float = 1.0 , UpperCamelCase__ : str = None , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
__magic_name__ = initial_learning_rate
__magic_name__ = warmup_steps
__magic_name__ = power
__magic_name__ = decay_schedule_fn
__magic_name__ = name
def __call__( self : int , UpperCamelCase__ : Dict ) -> Optional[int]:
"""simple docstring"""
with tf.name_scope(self.name or """WarmUp""" ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__magic_name__ = tf.cast(UpperCamelCase__ , tf.floataa )
__magic_name__ = tf.cast(self.warmup_steps , tf.floataa )
__magic_name__ = global_step_float / warmup_steps_float
__magic_name__ = self.initial_learning_rate * tf.math.pow(UpperCamelCase__ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=UpperCamelCase__ , )
def _lowercase ( self : Tuple ) -> int:
"""simple docstring"""
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def a__ ( A_, A_, A_, A_ = 0.0, A_ = 0.9, A_ = 0.999, A_ = 1e-8, A_ = None, A_ = None, A_ = 0.0, A_ = 1.0, A_ = None, ):
'''simple docstring'''
__magic_name__ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=A_, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=init_lr * min_lr_ratio, power=A_, )
if num_warmup_steps:
__magic_name__ = WarmUp(
initial_learning_rate=A_, decay_schedule_fn=A_, warmup_steps=A_, )
if weight_decay_rate > 0.0:
__magic_name__ = AdamWeightDecay(
learning_rate=A_, weight_decay_rate=A_, beta_a=A_, beta_a=A_, epsilon=A_, clipnorm=A_, global_clipnorm=A_, exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""], include_in_weight_decay=A_, )
else:
__magic_name__ = tf.keras.optimizers.Adam(
learning_rate=A_, beta_a=A_, beta_a=A_, epsilon=A_, clipnorm=A_, global_clipnorm=A_, )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase_ ( snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , UpperCamelCase__ : float = 0.9 , UpperCamelCase__ : float = 0.999 , UpperCamelCase__ : float = 1E-7 , UpperCamelCase__ : bool = False , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : Optional[List[str]] = None , UpperCamelCase__ : Optional[List[str]] = None , UpperCamelCase__ : str = "AdamWeightDecay" , **UpperCamelCase__ : str , ) -> int:
"""simple docstring"""
super().__init__(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
__magic_name__ = weight_decay_rate
__magic_name__ = include_in_weight_decay
__magic_name__ = exclude_from_weight_decay
@classmethod
def _lowercase ( cls : str , UpperCamelCase__ : Tuple ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = {'WarmUp': WarmUp}
return super(UpperCamelCase__ , cls ).from_config(UpperCamelCase__ , custom_objects=UpperCamelCase__ )
def _lowercase ( self : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
super(UpperCamelCase__ , self )._prepare_local(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = tf.constant(
self.weight_decay_rate , name="""adam_weight_decay_rate""" )
def _lowercase ( self : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
__magic_name__ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""] , use_locking=self._use_locking , )
return tf.no_op()
def _lowercase ( self : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[int]=None , **UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = list(zip(*UpperCamelCase__ ) )
return super(UpperCamelCase__ , self ).apply_gradients(zip(UpperCamelCase__ , UpperCamelCase__ ) , name=UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any ) -> str:
"""simple docstring"""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__magic_name__ = apply_state or {}
__magic_name__ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__magic_name__ = self._fallback_apply_state(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def _lowercase ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any]=None ) -> List[Any]:
"""simple docstring"""
__magic_name__ = self._get_lr(var.device , var.dtype.base_dtype , UpperCamelCase__ )
__magic_name__ = self._decay_weights_op(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
with tf.control_dependencies([decay] ):
return super(UpperCamelCase__ , self )._resource_apply_dense(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : str , UpperCamelCase__ : List[Any]=None ) -> List[str]:
"""simple docstring"""
__magic_name__ = self._get_lr(var.device , var.dtype.base_dtype , UpperCamelCase__ )
__magic_name__ = self._decay_weights_op(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
with tf.control_dependencies([decay] ):
return super(UpperCamelCase__ , self )._resource_apply_sparse(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__magic_name__ = super().get_config()
config.update({"""weight_decay_rate""": self.weight_decay_rate} )
return config
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(UpperCamelCase__ , UpperCamelCase__ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(UpperCamelCase__ , UpperCamelCase__ ) is not None:
return False
return True
class UpperCAmelCase_ ( snake_case__ ):
'''simple docstring'''
def __init__( self : int ) -> List[Any]:
"""simple docstring"""
__magic_name__ = []
__magic_name__ = None
@property
def _lowercase ( self : Dict ) -> List[Any]:
"""simple docstring"""
if self._accum_steps is None:
__magic_name__ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=UpperCamelCase__ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def _lowercase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
if not self._gradients:
raise ValueError("""The accumulator should be called first to initialize the gradients""" )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Optional[Any] , UpperCamelCase__ : Any ) -> Any:
"""simple docstring"""
if not self._gradients:
__magic_name__ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(UpperCamelCase__ ) , trainable=UpperCamelCase__ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(UpperCamelCase__ ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(UpperCamelCase__ )}''' )
for accum_gradient, gradient in zip(self._gradients , UpperCamelCase__ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(UpperCamelCase__ )
self._accum_steps.assign_add(1 )
def _lowercase ( self : int ) -> int:
"""simple docstring"""
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(UpperCamelCase__ ) )
| 88 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ) -> list[str]:
"""simple docstring"""
__lowerCAmelCase: int = set()
# keep track of all the paths to be checked
__lowerCAmelCase: str = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: Union[str, Any] = path[-1]
if node not in explored:
__lowerCAmelCase: Dict = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
new_path.append(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Optional[int] = [start]
__lowerCAmelCase: Dict = set(SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: Optional[int] = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Any = queue.pop(0 )
if node == target:
__lowerCAmelCase: Optional[int] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 322 | 0 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
A__: List[Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : List[str] ,_UpperCAmelCase : Any ,_UpperCAmelCase : Dict=False ) -> Union[str, Any]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
if not is_sharded:
_a : Any =os.path.abspath(_UpperCAmelCase )
logger.info(F"Loading PyTorch weights from {pt_path}" )
_a : Tuple =torch.load(_UpperCAmelCase ,map_location="""cpu""" )
logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters." )
_a : int =convert_pytorch_state_dict_to_flax(_UpperCAmelCase ,_UpperCAmelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
_a : Union[str, Any] =convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase ,_UpperCAmelCase )
return flax_state_dict
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Tuple[str] ,_UpperCAmelCase : np.ndarray ,_UpperCAmelCase : Dict[str, jnp.ndarray] ,_UpperCAmelCase : str ,) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(_UpperCAmelCase : Tuple[str] ) -> bool:
return len(set(_UpperCAmelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
_a : Union[str, Any] =pt_tuple_key[:-1] + ('scale',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
_a : Union[str, Any] =pt_tuple_key[:-1] + ('mean',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
_a : Optional[Any] =pt_tuple_key[:-1] + ('var',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
_a : Tuple =pt_tuple_key[:-1] + ('embedding',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
_a : Optional[Any] =pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
_a : int =pt_tensor.transpose(2 ,3 ,1 ,0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_a : str =pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase ):
_a : Dict =pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_a : List[Any] =pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_a : List[Any] =pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
_a : str =None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
_a : Any =pt_tuple_key[-2] + '_g'
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
_a : Dict =pt_tuple_key[-2] + '_v'
if name is not None:
_a : Tuple =pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : List[str] ) -> Any:
_a : str ={k: v.numpy() for k, v in pt_state_dict.items()}
_a : List[Any] =flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
_a : Optional[Any] =flax_model.params['params']
else:
_a : Optional[int] =flax_model.params
_a : Optional[Any] =flatten_dict(_UpperCAmelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_a : Any =flatten_dict(flax_model.params["""batch_stats"""] )
random_flax_state_dict.update(_UpperCAmelCase )
_a : Any ={}
_a : Optional[int] =(model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
_a : List[str] =(model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_a : Tuple =tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
_a : str =pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_a : Tuple =pt_tuple_key[1:]
# Correctly rename weight parameters
_a : Optional[int] =rename_key_and_reshape_tensor(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
# add model prefix if necessary
_a : str =(model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_a : Dict =(model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
_a : int =jnp.asarray(_UpperCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_UpperCAmelCase ,_UpperCAmelCase )
continue
# also add unexpected weight so that warning is thrown
_a : str =jnp.asarray(_UpperCAmelCase )
else:
# also add unexpected weight so that warning is thrown
_a : Union[str, Any] =jnp.asarray(_UpperCAmelCase )
return unflatten_dict(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Optional[int] ) -> List[Any]:
import torch
# Load the index
_a : Optional[int] ={}
for shard_file in shard_filenames:
# load using msgpack utils
_a : Any =torch.load(_UpperCAmelCase )
_a : Tuple ={k: v.numpy() for k, v in pt_state_dict.items()}
_a : Union[str, Any] =flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_a : Dict =flax_model.params['params']
_a : Any =flatten_dict(_UpperCAmelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
else:
_a : Optional[int] =flax_model.params
_a : List[Any] =flatten_dict(_UpperCAmelCase )
_a : Optional[Any] =(model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
_a : Tuple =(model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_a : Any =tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
_a : List[Any] =pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_a : Dict =pt_tuple_key[1:]
# Correctly rename weight parameters
_a : List[str] =rename_key_and_reshape_tensor(
_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
# add model prefix if necessary
_a : str =(model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_a : Any =(model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
_a : Optional[Any] =jnp.asarray(_UpperCAmelCase )
continue
if "var" in flax_key[-1]:
_a : int =jnp.asarray(_UpperCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_UpperCAmelCase ,_UpperCAmelCase )
continue
# also add unexpected weight so that warning is thrown
_a : Optional[int] =jnp.asarray(_UpperCAmelCase )
else:
# also add unexpected weight so that warning is thrown
_a : Union[str, Any] =jnp.asarray(_UpperCAmelCase )
return unflatten_dict(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ,_UpperCAmelCase : Optional[int] ) -> List[str]:
_a : Any =os.path.abspath(_UpperCAmelCase )
logger.info(F"Loading Flax weights from {flax_checkpoint_path}" )
# import correct flax class
_a : Dict =getattr(_UpperCAmelCase ,"""Flax""" + model.__class__.__name__ )
# load flax weight dict
with open(_UpperCAmelCase ,"""rb""" ) as state_f:
try:
_a : Tuple =from_bytes(_UpperCAmelCase ,state_f.read() )
except UnpicklingError:
raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(_UpperCAmelCase ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Union[str, Any] ) -> Optional[int]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
_a : Optional[int] =flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase : x.dtype == jnp.bfloataa ,_UpperCAmelCase ) ).values()
if any(_UpperCAmelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
_a : List[Any] =jax.tree_util.tree_map(
lambda _UpperCAmelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params ,_UpperCAmelCase )
_a : int =flatten_dict(_UpperCAmelCase )
_a : Optional[Any] =pt_model.state_dict()
_a : int =(pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
_a : Dict =(pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
_a : List[str] =[]
_a : Any =set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_a : str =flax_key_tuple[0] == pt_model.base_model_prefix
_a : str ='.'.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
_a : Optional[int] =flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
_a : List[Any] =(pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase ) not in pt_model_dict:
# conv layer
_a : str =flax_key_tuple[:-1] + ('weight',)
_a : Dict =jnp.transpose(_UpperCAmelCase ,(3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase ) not in pt_model_dict:
# linear layer
_a : Union[str, Any] =flax_key_tuple[:-1] + ('weight',)
_a : List[Any] =flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_a : Any =flax_key_tuple[:-1] + ('weight',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
_a : str =flax_key_tuple[:-1] + ('running_mean',)
elif "var" in flax_key_tuple[-1]:
_a : Tuple =flax_key_tuple[:-1] + ('running_var',)
if "batch_stats" in flax_state:
_a : Union[str, Any] ='.'.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
_a : List[Any] ='.'.join(_UpperCAmelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
_a : List[Any] ={}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
_a : Dict =key.split(""".""" )
_a : Optional[Any] =None
if key_components[-3::2] == ["parametrizations", "original0"]:
_a : List[str] =key_components[-2] + '_g'
elif key_components[-3::2] == ["parametrizations", "original1"]:
_a : Any =key_components[-2] + '_v'
if name is not None:
_a : List[str] =key_components[:-3] + [name]
_a : Tuple ='.'.join(_UpperCAmelCase )
_a : List[str] =key
if flax_key in special_pt_names:
_a : Dict =special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
_a : Union[str, Any] =np.asarray(_UpperCAmelCase ) if not isinstance(_UpperCAmelCase ,np.ndarray ) else flax_tensor
_a : Union[str, Any] =torch.from_numpy(_UpperCAmelCase )
# remove from missing keys
missing_keys.remove(_UpperCAmelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_UpperCAmelCase )
pt_model.load_state_dict(_UpperCAmelCase )
# re-transform missing_keys to list
_a : Any =list(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
else:
logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n" )
if len(_UpperCAmelCase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
""" use it for predictions and inference.""" )
else:
logger.warning(
F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
"""If your task is similar to the task the model of the checkpoint was trained on, """
F"you can already use {pt_model.__class__.__name__} for predictions without further training." )
return pt_model
| 276 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
_lowercase : int = ['image_processor', 'tokenizer']
_lowercase : Union[str, Any] = 'LayoutLMv3ImageProcessor'
_lowercase : List[str] = ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Any , UpperCAmelCase : Dict=None , UpperCAmelCase : Tuple=None , **UpperCAmelCase : Optional[Any] ) -> str:
__lowerCAmelCase: str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase , )
__lowerCAmelCase: List[Any] = kwargs.pop('feature_extractor' )
__lowerCAmelCase: Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCAmelCase , UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__lowerCAmelCase: str = self.image_processor(images=UpperCAmelCase , return_tensors=UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCAmelCase: List[str] = features['words']
__lowerCAmelCase: List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel values
__lowerCAmelCase: Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__lowerCAmelCase: int = self.get_overflowing_images(UpperCAmelCase , encoded_inputs['overflow_to_sample_mapping'] )
__lowerCAmelCase: str = images
return encoded_inputs
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCAmelCase: str = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
return images_with_overflow
def UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Any , *UpperCAmelCase : Dict , **UpperCAmelCase : Any ) -> List[str]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase , )
return self.image_processor
| 322 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
snake_case_ = 'falcon'
snake_case_ = ['past_key_values']
def __init__( self : Dict , __lowercase : Dict=6_50_24 , __lowercase : Optional[int]=45_44 , __lowercase : List[str]=32 , __lowercase : List[str]=71 , __lowercase : Union[str, Any]=1e-5 , __lowercase : Dict=0.02 , __lowercase : Dict=True , __lowercase : List[str]=0.0 , __lowercase : Any=0.0 , __lowercase : Tuple=None , __lowercase : str=False , __lowercase : List[Any]=False , __lowercase : List[Any]=True , __lowercase : int=True , __lowercase : Optional[int]=False , __lowercase : str=11 , __lowercase : Optional[Any]=11 , **__lowercase : Dict , ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =vocab_size
# Backward compatibility with n_embed kwarg
SCREAMING_SNAKE_CASE__ : str =kwargs.pop('''n_embed''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =hidden_size if n_embed is None else n_embed
SCREAMING_SNAKE_CASE__ : Tuple =num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Any =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =use_cache
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : Any =attention_dropout
SCREAMING_SNAKE_CASE__ : int =bos_token_id
SCREAMING_SNAKE_CASE__ : Tuple =eos_token_id
SCREAMING_SNAKE_CASE__ : Dict =num_attention_heads if num_kv_heads is None else num_kv_heads
SCREAMING_SNAKE_CASE__ : Tuple =alibi
SCREAMING_SNAKE_CASE__ : Tuple =new_decoder_architecture
SCREAMING_SNAKE_CASE__ : Dict =multi_query # Ignored when new_decoder_architecture is True
SCREAMING_SNAKE_CASE__ : Tuple =parallel_attn
SCREAMING_SNAKE_CASE__ : Optional[Any] =bias
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
return self.hidden_size // self.num_attention_heads
@property
def __magic_name__ ( self : Tuple ) -> Tuple:
return not self.alibi | 152 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : tuple , SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int]=False , ) -> str:
"""simple docstring"""
output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , use_external_data_format=SCREAMING_SNAKE_CASE , enable_onnx_checker=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
else:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool = False ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__lowerCAmelCase: str = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__lowerCAmelCase: Dict = 'cpu'
__lowerCAmelCase: Optional[int] = Path(SCREAMING_SNAKE_CASE )
# VAE DECODER
__lowerCAmelCase: Optional[Any] = AutoencoderKL.from_pretrained(model_path + '/vae' )
__lowerCAmelCase: Union[str, Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
__lowerCAmelCase: Any = vae_decoder.decode
onnx_export(
SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 322 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Any = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
"""Blip2VisionConfig""",
],
"""processing_blip_2""": ["""Blip2Processor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a: Union[str, Any] = [
"""BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Blip2Model""",
"""Blip2QFormerModel""",
"""Blip2PreTrainedModel""",
"""Blip2ForConditionalGeneration""",
"""Blip2VisionModel""",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
__a: int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 198 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square(SCREAMING_SNAKE_CASE , col + 1 )
__lowerCAmelCase: Tuple = update_area_of_max_square(row + 1 , col + 1 )
__lowerCAmelCase: int = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: List[str] = 1 + min([right, diagonal, down] )
__lowerCAmelCase: List[str] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
return sub_problem_sol
else:
return 0
__lowerCAmelCase: List[str] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__lowerCAmelCase: List[Any] = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: int = 1 + min([right, diagonal, down] )
__lowerCAmelCase: Union[str, Any] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sub_problem_sol
return sub_problem_sol
else:
return 0
__lowerCAmelCase: int = [0]
__lowerCAmelCase: int = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: int = [[0] * (cols + 1) for _ in range(rows + 1 )]
__lowerCAmelCase: Optional[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: Union[str, Any] = dp_array[row][col + 1]
__lowerCAmelCase: str = dp_array[row + 1][col + 1]
__lowerCAmelCase: Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
__lowerCAmelCase: Optional[Any] = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(dp_array[row][col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Dict = 0
return largest_square_area
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: Tuple = [0] * (cols + 1)
__lowerCAmelCase: Optional[int] = [0] * (cols + 1)
__lowerCAmelCase: str = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: int = current_row[col + 1]
__lowerCAmelCase: Union[str, Any] = next_row[col + 1]
__lowerCAmelCase: Any = next_row[col]
if mat[row][col] == 1:
__lowerCAmelCase: str = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(current_row[col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: int = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 322 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Tuple:
return f'gaussian_noise_s={seed}_shape={"_".join([str(lowercase ) for s in shape] )}.npy'
def SCREAMING_SNAKE_CASE_( self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def SCREAMING_SNAKE_CASE_( self , lowercase=0 , lowercase=(4, 4, 64, 64) , lowercase=False ) -> int:
lowerCamelCase_ = jnp.bfloataa if fpaa else jnp.floataa
lowerCamelCase_ = jnp.array(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) , dtype=lowercase )
return image
def SCREAMING_SNAKE_CASE_( self , lowercase=False , lowercase="CompVis/stable-diffusion-v1-4" ) -> Dict:
lowerCamelCase_ = jnp.bfloataa if fpaa else jnp.floataa
lowerCamelCase_ = 'bf16' if fpaa else None
lowerCamelCase_ = FlaxUNetaDConditionModel.from_pretrained(
lowercase , subfolder="unet" , dtype=lowercase , revision=lowercase )
return model, params
def SCREAMING_SNAKE_CASE_( self , lowercase=0 , lowercase=(4, 77, 768) , lowercase=False ) -> List[str]:
lowerCamelCase_ = jnp.bfloataa if fpaa else jnp.floataa
lowerCamelCase_ = jnp.array(load_hf_numpy(self.get_file_format(lowercase , lowercase ) ) , dtype=lowercase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
[17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
[8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
[3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> Any:
lowerCamelCase_ = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4" , fpaa=lowercase )
lowerCamelCase_ = self.get_latents(lowercase , fpaa=lowercase )
lowerCamelCase_ = self.get_encoder_hidden_states(lowercase , fpaa=lowercase )
lowerCamelCase_ = model.apply(
{"params": params} , lowercase , jnp.array(lowercase , dtype=jnp.intaa ) , encoder_hidden_states=lowercase , ).sample
assert sample.shape == latents.shape
lowerCamelCase_ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowerCamelCase_ = jnp.array(lowercase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(lowercase , lowercase , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
[17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
[8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
[3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> List[Any]:
lowerCamelCase_ = self.get_unet_model(model_id="stabilityai/stable-diffusion-2" , fpaa=lowercase )
lowerCamelCase_ = self.get_latents(lowercase , shape=(4, 4, 96, 96) , fpaa=lowercase )
lowerCamelCase_ = self.get_encoder_hidden_states(lowercase , shape=(4, 77, 1024) , fpaa=lowercase )
lowerCamelCase_ = model.apply(
{"params": params} , lowercase , jnp.array(lowercase , dtype=jnp.intaa ) , encoder_hidden_states=lowercase , ).sample
assert sample.shape == latents.shape
lowerCamelCase_ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowerCamelCase_ = jnp.array(lowercase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(lowercase , lowercase , atol=1e-2 )
| 19 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_a = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = SavedModel()
__lowerCAmelCase: str = []
with open(os.path.join(SCREAMING_SNAKE_CASE , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__lowerCAmelCase: List[str] = json.load(SCREAMING_SNAKE_CASE )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(SCREAMING_SNAKE_CASE )] )
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__lowerCAmelCase: Optional[int] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowerCAmelCase: List[str] = sorted(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(SCREAMING_SNAKE_CASE )
if strict and len(SCREAMING_SNAKE_CASE ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(SCREAMING_SNAKE_CASE ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*SCREAMING_SNAKE_CASE , sep='\n' )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_a = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 322 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_lowerCAmelCase : int = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lowerCAmelCase ( _lowerCAmelCase : Any , _lowerCAmelCase : tuple , _lowerCAmelCase : Path , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=False , ):
"""simple docstring"""
output_path.parent.mkdir(parents=_lowerCAmelCase , exist_ok=_lowerCAmelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_lowerCAmelCase , _lowerCAmelCase , f=output_path.as_posix() , input_names=_lowerCAmelCase , output_names=_lowerCAmelCase , dynamic_axes=_lowerCAmelCase , do_constant_folding=_lowerCAmelCase , use_external_data_format=_lowerCAmelCase , enable_onnx_checker=_lowerCAmelCase , opset_version=_lowerCAmelCase , )
else:
export(
_lowerCAmelCase , _lowerCAmelCase , f=output_path.as_posix() , input_names=_lowerCAmelCase , output_names=_lowerCAmelCase , dynamic_axes=_lowerCAmelCase , do_constant_folding=_lowerCAmelCase , opset_version=_lowerCAmelCase , )
@torch.no_grad()
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : bool = False ):
"""simple docstring"""
UpperCAmelCase__ = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCAmelCase__ = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA" )
else:
UpperCAmelCase__ = 'cpu'
UpperCAmelCase__ = Path(_lowerCAmelCase )
# VAE DECODER
UpperCAmelCase__ = AutoencoderKL.from_pretrained(model_path + "/vae" )
UpperCAmelCase__ = vae_decoder.config.latent_channels
# forward only through the decoder part
UpperCAmelCase__ = vae_decoder.decode
onnx_export(
_lowerCAmelCase , model_args=(
torch.randn(1 , _lowerCAmelCase , 25 , 25 ).to(device=_lowerCAmelCase , dtype=_lowerCAmelCase ),
False,
) , output_path=output_path / "vae_decoder" / "model.onnx" , ordered_input_names=["latent_sample", "return_dict"] , output_names=["sample"] , dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
} , opset=_lowerCAmelCase , )
del vae_decoder
if __name__ == "__main__":
_lowerCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
required=True,
help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
)
parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--opset",
default=1_4,
type=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
_lowerCAmelCase : List[str] = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print("SD: Done: ONNX")
| 169 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase: Union[str, Any] = qiskit.QuantumRegister(4 , 'qr' )
__lowerCAmelCase: List[Any] = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCAmelCase: Any = [input_a, input_a, carry_in]
__lowerCAmelCase: List[str] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(SCREAMING_SNAKE_CASE ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(SCREAMING_SNAKE_CASE ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(SCREAMING_SNAKE_CASE ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , SCREAMING_SNAKE_CASE ) # measure the last two qbits
__lowerCAmelCase: List[str] = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase: List[Any] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 322 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
"kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
"kssteven/ibert-roberta-large-mnli": (
"https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
),
}
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
UpperCAmelCase_ ='ibert'
def __init__( self , _A=30522 , _A=768 , _A=12 , _A=12 , _A=3072 , _A="gelu" , _A=0.1 , _A=0.1 , _A=512 , _A=2 , _A=0.02 , _A=1E-12 , _A=1 , _A=0 , _A=2 , _A="absolute" , _A=False , _A="none" , **_A , ) -> List[Any]:
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
SCREAMING_SNAKE_CASE_ = vocab_size
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = layer_norm_eps
SCREAMING_SNAKE_CASE_ = position_embedding_type
SCREAMING_SNAKE_CASE_ = quant_mode
SCREAMING_SNAKE_CASE_ = force_dequant
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
@property
def _UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
SCREAMING_SNAKE_CASE_ = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 299 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
def __init__( self : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : int=3 , UpperCAmelCase : int=4 , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=9_9 , UpperCAmelCase : Tuple=3_6 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]=3_7 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[str]=5_1_2 , UpperCAmelCase : int=1_6 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[Any]=6 , UpperCAmelCase : int=6 , UpperCAmelCase : str=3 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[str]=1_0_0_0 , ) -> int:
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: List[str] = batch_size
__lowerCAmelCase: Optional[Any] = num_channels
__lowerCAmelCase: Tuple = image_size
__lowerCAmelCase: str = patch_size
__lowerCAmelCase: List[str] = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Union[str, Any] = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: Dict = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: str = hidden_dropout_prob
__lowerCAmelCase: str = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: str = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: List[str] = coordinate_size
__lowerCAmelCase: Tuple = shape_size
__lowerCAmelCase: List[Any] = num_labels
__lowerCAmelCase: Any = num_choices
__lowerCAmelCase: List[str] = scope
__lowerCAmelCase: Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase: Optional[Any] = text_seq_length
__lowerCAmelCase: List[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase: int = self.text_seq_length + self.image_seq_length
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase: str = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: Optional[Any] = bbox[i, j, 3]
__lowerCAmelCase: Tuple = bbox[i, j, 1]
__lowerCAmelCase: Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Any = bbox[i, j, 2]
__lowerCAmelCase: int = bbox[i, j, 0]
__lowerCAmelCase: int = tmp_coordinate
__lowerCAmelCase: List[Any] = tf.constant(UpperCAmelCase )
__lowerCAmelCase: Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase: Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase: List[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase: int = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase: str = None
__lowerCAmelCase: Dict = None
if self.use_labels:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase: Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> int:
__lowerCAmelCase: Tuple = TFLayoutLMvaModel(config=UpperCAmelCase )
# text + image
__lowerCAmelCase: Dict = model(UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , training=UpperCAmelCase , )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase: str = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase: List[str] = model({'pixel_values': pixel_values} , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] ) -> int:
__lowerCAmelCase: List[str] = self.num_labels
__lowerCAmelCase: Tuple = TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int ) -> Any:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: List[str] = TFLayoutLMvaForTokenClassification(config=UpperCAmelCase )
__lowerCAmelCase: Any = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> Any:
__lowerCAmelCase: str = 2
__lowerCAmelCase: Dict = TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)): List[str] = config_and_inputs
__lowerCAmelCase: List[str] = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : Tuple = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Dict = False
_lowercase : Tuple = False
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> List[str]:
return True
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=False ) -> dict:
__lowerCAmelCase: Optional[Any] = copy.deepcopy(UpperCAmelCase )
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: int = {
k: tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase: Tuple = TFLayoutLMvaModelTester(self )
__lowerCAmelCase: str = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase: List[Any] = model_class(UpperCAmelCase )
if getattr(UpperCAmelCase , 'hf_compute_loss' , UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase: Optional[int] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: List[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=UpperCAmelCase )[0]
]
__lowerCAmelCase: Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase: Optional[Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Tuple = prepared_for_class.pop('input_ids' )
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase: Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCAmelCase: str = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase: Tuple = -1_0_0
__lowerCAmelCase: Union[str, Any] = tf.convert_to_tensor(UpperCAmelCase )
__lowerCAmelCase: Dict = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase: str = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase: Any = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase: Tuple = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase: Dict = inspect.signature(model.call ).parameters
__lowerCAmelCase: Dict = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase: str = {0: 'input_ids'}
for label_key in label_keys:
__lowerCAmelCase: Optional[Any] = signature_names.index(UpperCAmelCase )
__lowerCAmelCase: Tuple = label_key
__lowerCAmelCase: Tuple = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase: List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase: Optional[Any] = prepared_for_class[value]
__lowerCAmelCase: Union[str, Any] = tuple(UpperCAmelCase )
# Send to model
__lowerCAmelCase: Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCAmelCase ( self : Dict ) -> Tuple:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> int:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: Tuple = type
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : int ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Optional[int] = TFLayoutLMvaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : int ) -> Dict:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Any ) -> List[str]:
__lowerCAmelCase: Any = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCAmelCase: Tuple = self.default_image_processor
__lowerCAmelCase: str = prepare_img()
__lowerCAmelCase: Optional[int] = image_processor(images=UpperCAmelCase , return_tensors='tf' ).pixel_values
__lowerCAmelCase: Dict = tf.constant([[1, 2]] )
__lowerCAmelCase: str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase: List[str] = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
__lowerCAmelCase: Tuple = (1, 1_9_9, 7_6_8)
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase )
__lowerCAmelCase: str = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _a ( snake_case__ , unittest.TestCase ):
__a : Any = AudioLDMPipeline
__a : Optional[int] = TEXT_TO_AUDIO_PARAMS
__a : Tuple = TEXT_TO_AUDIO_BATCH_PARAMS
__a : Any = frozenset(
[
"""num_inference_steps""",
"""num_waveforms_per_prompt""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def A ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=(32, 64) , class_embed_type='''simple_projection''' , projection_class_embeddings_input_dim=32 , class_embeddings_concat=lowercase , )
UpperCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowercase , set_alpha_to_one=lowercase , )
torch.manual_seed(0 )
UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCAmelCase = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , projection_dim=32 , )
UpperCAmelCase = ClapTextModelWithProjection(lowercase )
UpperCAmelCase = RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta''' , model_max_length=77 )
UpperCAmelCase = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=16_000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=lowercase , )
UpperCAmelCase = SpeechTaHifiGan(lowercase )
UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'vocoder': vocoder,
}
return components
def A ( self : Tuple , lowercase : Union[str, Any] , lowercase : Optional[int]=0 ):
'''simple docstring'''
if str(lowercase ).startswith('''mps''' ):
UpperCAmelCase = torch.manual_seed(lowercase )
else:
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
UpperCAmelCase = {
'prompt': 'A hammer hitting a wooden surface',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
}
return inputs
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = audioldm_pipe(**lowercase )
UpperCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(lowercase ) == 256
UpperCAmelCase = audio[:10]
UpperCAmelCase = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = 3 * [inputs['prompt']]
# forward
UpperCAmelCase = audioldm_pipe(**lowercase )
UpperCAmelCase = output.audios[0]
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = 3 * [inputs.pop('''prompt''' )]
UpperCAmelCase = audioldm_pipe.tokenizer(
lowercase , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=lowercase , return_tensors='''pt''' , )
UpperCAmelCase = text_inputs['input_ids'].to(lowercase )
UpperCAmelCase = audioldm_pipe.text_encoder(
lowercase , )
UpperCAmelCase = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCAmelCase = F.normalize(lowercase , dim=-1 )
UpperCAmelCase = prompt_embeds
# forward
UpperCAmelCase = audioldm_pipe(**lowercase )
UpperCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = 3 * ['this is a negative prompt']
UpperCAmelCase = negative_prompt
UpperCAmelCase = 3 * [inputs['prompt']]
# forward
UpperCAmelCase = audioldm_pipe(**lowercase )
UpperCAmelCase = output.audios[0]
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = 3 * [inputs.pop('''prompt''' )]
UpperCAmelCase = []
for p in [prompt, negative_prompt]:
UpperCAmelCase = audioldm_pipe.tokenizer(
lowercase , padding='''max_length''' , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=lowercase , return_tensors='''pt''' , )
UpperCAmelCase = text_inputs['input_ids'].to(lowercase )
UpperCAmelCase = audioldm_pipe.text_encoder(
lowercase , )
UpperCAmelCase = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCAmelCase = F.normalize(lowercase , dim=-1 )
embeds.append(lowercase )
UpperCAmelCase = embeds
# forward
UpperCAmelCase = audioldm_pipe(**lowercase )
UpperCAmelCase = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1E-2
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = PNDMScheduler(skip_prk_steps=lowercase )
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = 'egg cracking'
UpperCAmelCase = audioldm_pipe(**lowercase , negative_prompt=lowercase )
UpperCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(lowercase ) == 256
UpperCAmelCase = audio[:10]
UpperCAmelCase = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1E-2
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = PNDMScheduler(skip_prk_steps=lowercase )
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = 'A hammer hitting a wooden surface'
# test num_waveforms_per_prompt=1 (default)
UpperCAmelCase = audioldm_pipe(lowercase , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCAmelCase = 2
UpperCAmelCase = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCAmelCase = 2
UpperCAmelCase = audioldm_pipe(lowercase , num_inference_steps=2 , num_waveforms_per_prompt=lowercase ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCAmelCase = 2
UpperCAmelCase = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=lowercase ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = audioldm_pipe.vocoder.config.sampling_rate
UpperCAmelCase = self.get_dummy_inputs(lowercase )
UpperCAmelCase = audioldm_pipe(audio_length_in_s=0.016 , **lowercase )
UpperCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(lowercase ) / vocoder_sampling_rate == 0.016
UpperCAmelCase = audioldm_pipe(audio_length_in_s=0.032 , **lowercase )
UpperCAmelCase = output.audios[0]
assert audio.ndim == 1
assert len(lowercase ) / vocoder_sampling_rate == 0.032
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.get_dummy_components()
UpperCAmelCase = AudioLDMPipeline(**lowercase )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = ['hey']
UpperCAmelCase = audioldm_pipe(lowercase , num_inference_steps=1 )
UpperCAmelCase = output.audios.shape
assert audio_shape == (1, 256)
UpperCAmelCase = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCAmelCase = SpeechTaHifiGan(lowercase ).to(lowercase )
UpperCAmelCase = audioldm_pipe(lowercase , num_inference_steps=1 )
UpperCAmelCase = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def A ( self : Optional[Any] ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def A ( self : List[str] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase )
@slow
class _a ( unittest.TestCase ):
def A ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Any , lowercase : Optional[Any] , lowercase : Any="cpu" , lowercase : List[str]=torch.floataa , lowercase : Tuple=0 ):
'''simple docstring'''
UpperCAmelCase = torch.Generator(device=lowercase ).manual_seed(lowercase )
UpperCAmelCase = np.random.RandomState(lowercase ).standard_normal((1, 8, 128, 16) )
UpperCAmelCase = torch.from_numpy(lowercase ).to(device=lowercase , dtype=lowercase )
UpperCAmelCase = {
'prompt': 'A hammer hitting a wooden surface',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 2.5,
}
return inputs
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_inputs(lowercase )
UpperCAmelCase = 25
UpperCAmelCase = audioldm_pipe(**lowercase ).audios[0]
assert audio.ndim == 1
assert len(lowercase ) == 81_920
UpperCAmelCase = audio[77_230:77_240]
UpperCAmelCase = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1E-2
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = AudioLDMPipeline.from_pretrained('''cvssp/audioldm''' )
UpperCAmelCase = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCAmelCase = audioldm_pipe.to(lowercase )
audioldm_pipe.set_progress_bar_config(disable=lowercase )
UpperCAmelCase = self.get_inputs(lowercase )
UpperCAmelCase = audioldm_pipe(**lowercase ).audios[0]
assert audio.ndim == 1
assert len(lowercase ) == 81_920
UpperCAmelCase = audio[27_780:27_790]
UpperCAmelCase = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCAmelCase = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3E-2
| 34 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : str=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=9_9 , UpperCAmelCase : Optional[int]=3_2 , UpperCAmelCase : Dict=5 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : Dict=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : int=0.02 , UpperCAmelCase : List[Any]=4 , ) -> Optional[Any]:
__lowerCAmelCase: str = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Optional[int] = seq_length
__lowerCAmelCase: Dict = is_training
__lowerCAmelCase: Optional[Any] = use_attention_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Optional[int] = use_labels
__lowerCAmelCase: Optional[Any] = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: List[str] = num_attention_heads
__lowerCAmelCase: int = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Optional[int] = max_position_embeddings
__lowerCAmelCase: Union[str, Any] = type_vocab_size
__lowerCAmelCase: int = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Optional[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Optional[int] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : Dict ) -> Any:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: Optional[Any] = config_and_inputs
__lowerCAmelCase: Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( snake_case__ , unittest.TestCase ):
_lowercase : Dict = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase: List[Any] = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
__lowerCAmelCase: Optional[Any] = model_class_name.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
@require_flax
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: List[Any] = FlaxAlbertModel.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
__lowerCAmelCase: str = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
_UpperCamelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def a_ ( _lowerCAmelCase ) -> int:
__lowerCamelCase : Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
number //= 100000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_UpperCamelCase = [None] * 10000000
_UpperCamelCase = True
_UpperCamelCase = False
def a_ ( _lowerCAmelCase ) -> bool:
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCamelCase : int = chain(next_number(_lowerCAmelCase ) )
__lowerCamelCase : Tuple = number_chain
while number < 10000000:
__lowerCamelCase : Dict = number_chain
number *= 10
return number_chain
def a_ ( _lowerCAmelCase = 10000000 ) -> int:
for i in range(1 ,_lowerCAmelCase ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{solution() = }''')
| 208 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
from manim import *
class __UpperCamelCase ( snake_case__ ):
"""simple docstring"""
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = Rectangle(height=0.5 , width=0.5 )
__SCREAMING_SNAKE_CASE : str = Rectangle(height=0.25 , width=0.25 )
__SCREAMING_SNAKE_CASE : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__SCREAMING_SNAKE_CASE : str = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : List[str] = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : Optional[Any] = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : Any = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : str = VGroup(_A , _A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = Text('''CPU''' , font_size=24 )
__SCREAMING_SNAKE_CASE : int = Group(_A , _A ).arrange(_A , buff=0.5 , aligned_edge=_A )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [mem.copy() for i in range(4 )]
__SCREAMING_SNAKE_CASE : Tuple = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : int = Text('''GPU''' , font_size=24 )
__SCREAMING_SNAKE_CASE : Optional[int] = Group(_A , _A ).arrange(_A , buff=0.5 , aligned_edge=_A )
gpu.move_to([-1, -1, 0] )
self.add(_A )
__SCREAMING_SNAKE_CASE : Tuple = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : Any = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : List[Any] = Text('''Model''' , font_size=24 )
__SCREAMING_SNAKE_CASE : List[Any] = Group(_A , _A ).arrange(_A , buff=0.5 , aligned_edge=_A )
model.move_to([3, -1.0, 0] )
self.add(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : Any = []
__SCREAMING_SNAKE_CASE : Optional[int] = []
for i, rect in enumerate(_A ):
rect.set_stroke(_A )
__SCREAMING_SNAKE_CASE : Tuple = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_A , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_A )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=_A , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=_A , buff=0.0 )
self.add(_A )
model_cpu_arr.append(_A )
self.add(*_A , *_A , *_A )
__SCREAMING_SNAKE_CASE : Tuple = [mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : Tuple = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : int = Text('''Loaded Checkpoint''' , font_size=24 )
__SCREAMING_SNAKE_CASE : Tuple = Group(_A , _A ).arrange(_A , buff=0.5 , aligned_edge=_A )
checkpoint.move_to([3, 0.5, 0] )
self.add(_A )
__SCREAMING_SNAKE_CASE : Any = []
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for i, rect in enumerate(_A ):
__SCREAMING_SNAKE_CASE : Any = fill.copy().set_fill(_A , opacity=0.7 )
target.move_to(_A )
ckpt_arr.append(_A )
__SCREAMING_SNAKE_CASE : int = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(_A )
self.add(*_A , *_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__SCREAMING_SNAKE_CASE : Any = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_A , _A )
__SCREAMING_SNAKE_CASE : Tuple = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(_A , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(_A )
__SCREAMING_SNAKE_CASE : Tuple = MarkupText(
F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
__SCREAMING_SNAKE_CASE : Optional[int] = [meta_mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : Dict = [meta_mem.copy() for i in range(6 )]
__SCREAMING_SNAKE_CASE : List[str] = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : Tuple = VGroup(*_A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : Optional[int] = VGroup(_A , _A ).arrange(_A , buff=0 )
__SCREAMING_SNAKE_CASE : Any = Text('''Disk''' , font_size=24 )
__SCREAMING_SNAKE_CASE : Any = Group(_A , _A ).arrange(_A , buff=0.5 , aligned_edge=_A )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(_A , run_time=3 ) , Write(_A , run_time=1 ) , Create(_A , run_time=1 ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
for i, rect in enumerate(_A ):
__SCREAMING_SNAKE_CASE : Dict = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(_A , run_time=1.5 ) )
self.play(*_A )
self.play(FadeOut(_A ) )
__SCREAMING_SNAKE_CASE : str = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(_A , run_time=3 ) )
self.play(
FadeOut(_A , _A , *_A , *_A ) , )
self.wait()
| 303 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_a = [None] * 1_0_0_0_0_0_0_0
_a = True
_a = False
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCAmelCase: int = chain(next_number(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = number_chain
while number < 10_00_00_00:
__lowerCAmelCase: Dict = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE : int = 10_00_00_00 ) -> int:
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| 322 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class A_ ( snake_case__ ):
"""simple docstring"""
__UpperCamelCase = 'wav2vec2'
def __init__( self :str , lowercase_ :Any=32 , lowercase_ :Optional[Any]=7_68 , lowercase_ :Any=12 , lowercase_ :Optional[int]=12 , lowercase_ :Any=30_72 , lowercase_ :Optional[Any]="gelu" , lowercase_ :List[str]=0.1 , lowercase_ :List[str]=0.1 , lowercase_ :Dict=0.1 , lowercase_ :str=0.0 , lowercase_ :str=0.0 , lowercase_ :Tuple=0.1 , lowercase_ :int=0.1 , lowercase_ :Dict=0.02 , lowercase_ :Dict=1E-5 , lowercase_ :Optional[Any]="group" , lowercase_ :Union[str, Any]="gelu" , lowercase_ :Any=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , lowercase_ :Optional[Any]=(5, 2, 2, 2, 2, 2, 2) , lowercase_ :Optional[Any]=(10, 3, 3, 3, 3, 2, 2) , lowercase_ :List[Any]=False , lowercase_ :Union[str, Any]=1_28 , lowercase_ :List[str]=16 , lowercase_ :str=False , lowercase_ :Union[str, Any]=True , lowercase_ :Dict=0.05 , lowercase_ :List[str]=10 , lowercase_ :List[str]=2 , lowercase_ :Optional[Any]=0.0 , lowercase_ :Union[str, Any]=10 , lowercase_ :List[Any]=0 , lowercase_ :List[Any]=3_20 , lowercase_ :Union[str, Any]=2 , lowercase_ :Optional[Any]=0.1 , lowercase_ :Union[str, Any]=1_00 , lowercase_ :Optional[Any]=2_56 , lowercase_ :Optional[int]=2_56 , lowercase_ :str=0.1 , lowercase_ :Dict="sum" , lowercase_ :Union[str, Any]=False , lowercase_ :int=False , lowercase_ :Any=2_56 , lowercase_ :Optional[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) , lowercase_ :Tuple=(5, 3, 3, 1, 1) , lowercase_ :Tuple=(1, 2, 3, 1, 1) , lowercase_ :Optional[Any]=5_12 , lowercase_ :Optional[int]=0 , lowercase_ :Optional[int]=1 , lowercase_ :Any=2 , lowercase_ :int=False , lowercase_ :List[Any]=3 , lowercase_ :Dict=2 , lowercase_ :Tuple=3 , lowercase_ :List[str]=None , lowercase_ :List[str]=None , **lowercase_ :Union[str, Any] , ) -> Optional[int]:
super().__init__(**lowercase_ , pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ )
UpperCAmelCase = hidden_size
UpperCAmelCase = feat_extract_norm
UpperCAmelCase = feat_extract_activation
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = conv_bias
UpperCAmelCase = num_conv_pos_embeddings
UpperCAmelCase = num_conv_pos_embedding_groups
UpperCAmelCase = len(self.conv_dim )
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_dropout
UpperCAmelCase = attention_dropout
UpperCAmelCase = activation_dropout
UpperCAmelCase = feat_proj_dropout
UpperCAmelCase = final_dropout
UpperCAmelCase = layerdrop
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = initializer_range
UpperCAmelCase = vocab_size
UpperCAmelCase = do_stable_layer_norm
UpperCAmelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase = apply_spec_augment
UpperCAmelCase = mask_time_prob
UpperCAmelCase = mask_time_length
UpperCAmelCase = mask_time_min_masks
UpperCAmelCase = mask_feature_prob
UpperCAmelCase = mask_feature_length
UpperCAmelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
UpperCAmelCase = num_codevectors_per_group
UpperCAmelCase = num_codevector_groups
UpperCAmelCase = contrastive_logits_temperature
UpperCAmelCase = feat_quantizer_dropout
UpperCAmelCase = num_negatives
UpperCAmelCase = codevector_dim
UpperCAmelCase = proj_codevector_dim
UpperCAmelCase = diversity_loss_weight
# ctc loss
UpperCAmelCase = ctc_loss_reduction
UpperCAmelCase = ctc_zero_infinity
# adapter
UpperCAmelCase = add_adapter
UpperCAmelCase = adapter_kernel_size
UpperCAmelCase = adapter_stride
UpperCAmelCase = num_adapter_layers
UpperCAmelCase = output_hidden_size or hidden_size
UpperCAmelCase = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = list(lowercase_ )
UpperCAmelCase = xvector_output_dim
@property
def UpperCAmelCase__ ( self :Any ) -> Optional[Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 78 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
__lowerCAmelCase : Dict = getLogger(__name__)
def a__ ( A_, A_, A_, A_ = 8, A_ = 1024, A_="val", A_=None, A_=False, A_="summarization", A_=None, A_=1, A_ = None, A_="", **A_, ):
'''simple docstring'''
__magic_name__ = str(A_ )
assert local_rank is not None
torch.distributed.init_process_group(backend="""nccl""", rank=A_ )
__magic_name__ = Path(A_ )
__magic_name__ = save_dir.joinpath(f'''rank_{local_rank}_output.json''' )
torch.cuda.set_device(A_ )
__magic_name__ = AutoModelForSeqaSeqLM.from_pretrained(A_ ).cuda()
if fpaa:
__magic_name__ = model.half()
# determine if we need to increase num_beams
use_task_specific_params(A_, A_ ) # update config with task specific params
__magic_name__ = generate_kwargs.pop("""num_beams""", model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
__magic_name__ = num_return_sequences
__magic_name__ = AutoTokenizer.from_pretrained(A_ )
logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
if max_source_length is None:
__magic_name__ = tokenizer.model_max_length
if prefix is None:
__magic_name__ = prefix or getattr(model.config, """prefix""", """""" ) or ''
__magic_name__ = SeqaSeqDataset(
A_, A_, A_, max_target_length=1024, type_path=A_, n_obs=A_, prefix=A_, **A_, )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
__magic_name__ = ds.make_sortish_sampler(A_, distributed=A_, add_extra_examples=A_, shuffle=A_ )
__magic_name__ = DataLoader(A_, sampler=A_, batch_size=A_, collate_fn=ds.collate_fn )
__magic_name__ = []
for batch in tqdm(A_ ):
__magic_name__ = model.generate(
input_ids=batch["""input_ids"""].to(model.device ), attention_mask=batch["""attention_mask"""].to(model.device ), num_return_sequences=A_, num_beams=A_, **A_, )
__magic_name__ = tokenizer.batch_decode(A_, skip_special_tokens=A_, clean_up_tokenization_spaces=A_ )
__magic_name__ = batch['ids']
if num_return_sequences > 1:
__magic_name__ = chunks(A_, A_ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(A_ ):
results.append({"""pred""": pred, """id""": ids[i].item()} )
save_json(A_, A_ )
return results, sampler.num_replicas
def a__ ( ):
'''simple docstring'''
__magic_name__ = argparse.ArgumentParser(
epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate""" )
parser.add_argument("""--data_dir""", type=A_, help="""like cnn_dm/test.source""" )
parser.add_argument(
"""--model_name""", type=A_, help="""like facebook/bart-large-cnn,t5-base, etc.""", default="""sshleifer/distilbart-xsum-12-3""", )
parser.add_argument("""--save_dir""", type=A_, help="""where to save""", default="""tmp_gen""" )
parser.add_argument("""--max_source_length""", type=A_, default=A_ )
parser.add_argument(
"""--type_path""", type=A_, default="""test""", help="""which subset to evaluate typically train/val/test""" )
parser.add_argument("""--task""", type=A_, default="""summarization""", help="""used for task_specific_params + metrics""" )
parser.add_argument("""--bs""", type=A_, default=8, required=A_, help="""batch size""" )
parser.add_argument(
"""--local_rank""", type=A_, default=-1, required=A_, help="""should be passed by distributed.launch""" )
parser.add_argument(
"""--n_obs""", type=A_, default=A_, required=A_, help="""How many observations. Defaults to all.""" )
parser.add_argument(
"""--num_return_sequences""", type=A_, default=1, required=A_, help="""How many sequences to return""" )
parser.add_argument(
"""--sync_timeout""", type=A_, default=600, required=A_, help="""How long should master process wait for other processes to finish.""", )
parser.add_argument("""--src_lang""", type=A_, default=A_, required=A_ )
parser.add_argument("""--tgt_lang""", type=A_, default=A_, required=A_ )
parser.add_argument(
"""--prefix""", type=A_, required=A_, default=A_, help="""will be added to the begininng of src examples""" )
parser.add_argument("""--fp16""", action="""store_true""" )
parser.add_argument("""--debug""", action="""store_true""" )
__magic_name__ = time.time()
__magic_name__ = parser.parse_known_args()
__magic_name__ = parse_numeric_n_bool_cl_kwargs(A_ )
if generate_kwargs and args.local_rank <= 0:
print(f'''parsed the following generate kwargs: {generate_kwargs}''' )
__magic_name__ = Path(args.save_dir + """_tmp""" )
Path(A_ ).mkdir(exist_ok=A_ ) # this handles locking.
__magic_name__ = list(json_save_dir.glob("""rank_*.json""" ) )
if intermediate_files:
raise ValueError(f'''Found files at {json_save_dir} please move or remove them.''' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
__magic_name__ = {}
if args.src_lang is not None:
__magic_name__ = args.src_lang
if args.tgt_lang is not None:
__magic_name__ = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=A_ )
__magic_name__ = eval_data_dir(
args.data_dir, A_, args.model_name, type_path=args.type_path, bs=args.bs, fpaa=args.fpaa, task=args.task, local_rank=args.local_rank, n_obs=args.n_obs, max_source_length=args.max_source_length, num_return_sequences=args.num_return_sequences, prefix=args.prefix, dataset_kwargs=A_, **A_, )
if args.local_rank <= 0:
__magic_name__ = Path(args.save_dir )
save_dir.mkdir(exist_ok=A_ )
__magic_name__ = gather_results_from_each_node(A_, A_, args.sync_timeout )
__magic_name__ = combine_partial_results(A_ )
if args.num_return_sequences > 1:
__magic_name__ = save_dir.joinpath("""pseudolabel_results.json""" )
print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/''' )
save_json(A_, A_ )
return
__magic_name__ = Path(args.data_dir ).joinpath(args.type_path + """.target""" )
with open(A_ ) as f:
__magic_name__ = [x.rstrip() for x in f.readlines()][: len(A_ )]
# Calculate metrics, save metrics, and save _generations.txt
__magic_name__ = 'translation' in args.task
__magic_name__ = calculate_bleu if calc_bleu else calculate_rouge
__magic_name__ = 'bleu' if calc_bleu else 'rouge'
__magic_name__ = score_fn(A_, A_ )
__magic_name__ = len(A_ )
__magic_name__ = time.time() - start_time
__magic_name__ = round(runtime / metrics["""n_obs"""], 4 )
__magic_name__ = num_replicas
# TODO(@stas00): add whatever metadata to metrics
__magic_name__ = save_dir.joinpath(f'''{args.type_path}_{metric_name}.json''' )
save_json(A_, A_, indent=A_ )
print(A_ )
write_txt_file(A_, save_dir.joinpath(f'''{args.type_path}_generations.txt''' ) )
if args.debug:
write_txt_file(A_, save_dir.joinpath(f'''{args.type_path}.target''' ) )
else:
shutil.rmtree(A_ )
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = []
for partial_result in partial_results:
records.extend(A_ )
__magic_name__ = sorted(A_, key=lambda A_ : x["id"] )
__magic_name__ = [x['pred'] for x in records]
return preds
def a__ ( A_, A_, A_ ):
'''simple docstring'''
__magic_name__ = time.time()
logger.info("""waiting for all nodes to finish""" )
__magic_name__ = None
while (time.time() - start_wait) < timeout:
__magic_name__ = list(save_dir.glob("""rank_*.json""" ) )
if len(A_ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
__magic_name__ = lmap(A_, A_ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("""Rank 0 gave up on waiting for other processes""" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 88 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str=1_3 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : str=True , UpperCAmelCase : Any=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : int=False , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Any=9_9 , UpperCAmelCase : str=0 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : int=5 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : int=5_1_2 , UpperCAmelCase : str=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict="last" , UpperCAmelCase : int=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Union[str, Any]=0 , ) -> Dict:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Tuple = seq_length
__lowerCAmelCase: Tuple = is_training
__lowerCAmelCase: Optional[Any] = use_input_lengths
__lowerCAmelCase: List[str] = use_token_type_ids
__lowerCAmelCase: Dict = use_labels
__lowerCAmelCase: int = gelu_activation
__lowerCAmelCase: Optional[int] = sinusoidal_embeddings
__lowerCAmelCase: Tuple = causal
__lowerCAmelCase: Optional[Any] = asm
__lowerCAmelCase: int = n_langs
__lowerCAmelCase: Tuple = vocab_size
__lowerCAmelCase: List[Any] = n_special
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: int = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Dict = max_position_embeddings
__lowerCAmelCase: List[str] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: List[str] = num_labels
__lowerCAmelCase: List[str] = num_choices
__lowerCAmelCase: Optional[int] = summary_type
__lowerCAmelCase: Any = use_proj
__lowerCAmelCase: Optional[Any] = scope
__lowerCAmelCase: Dict = bos_token_id
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Any = None
if self.use_input_lengths:
__lowerCAmelCase: Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowerCAmelCase: str = None
if self.use_token_type_ids:
__lowerCAmelCase: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: Optional[int] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
__lowerCAmelCase: str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase: Dict = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: List[str] = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , ) -> int:
__lowerCAmelCase: str = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Dict , ) -> List[str]:
__lowerCAmelCase: Dict = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: str = model(UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
__lowerCAmelCase: Any = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((__lowerCAmelCase) , ): List[str] = result_with_labels.to_tuple()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((__lowerCAmelCase) , ): List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , ) -> List[Any]:
__lowerCAmelCase: Optional[Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = model(UpperCAmelCase )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
__lowerCAmelCase: List[Any] = self.num_choices
__lowerCAmelCase: Optional[Any] = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Tuple ) -> int:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCAmelCase: Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple=False ) -> Dict:
__lowerCAmelCase: Optional[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__lowerCAmelCase: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: int = XLMModelTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=3_7 )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__lowerCAmelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] ) -> int:
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : Dict=1 ) -> Dict:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: int = min_length + idx + 1
__lowerCAmelCase: Union[str, Any] = min_length + idx + 1
__lowerCAmelCase: Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: Any = min_length + idx + 1
__lowerCAmelCase: str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def UpperCAmelCase ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: List[Any] = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=UpperCAmelCase ) # the president
__lowerCAmelCase: Union[str, Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__lowerCAmelCase: str = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 322 | 0 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
A__: Optional[int] = logging.getLogger()
def SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_a : Dict =argparse.ArgumentParser()
parser.add_argument("""-f""" )
_a : List[Any] =parser.parse_args()
return args.f
class A__ ( snake_case__ ):
def __UpperCAmelCase ( self :Any ) -> None:
'''simple docstring'''
_a : Union[str, Any] =logging.StreamHandler(sys.stdout )
logger.addHandler(SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Union[str, Any] , SCREAMING_SNAKE_CASE :int ) -> Dict:
'''simple docstring'''
_a : int =get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(SCREAMING_SNAKE_CASE , """argv""" , SCREAMING_SNAKE_CASE ):
_a : int =run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(SCREAMING_SNAKE_CASE , 0.666 )
@slow
@require_torch_non_multi_gpu
def __UpperCAmelCase ( self :Dict ) -> List[Any]:
'''simple docstring'''
_a : List[Any] ='\n --model_type roberta\n --model_name_or_path roberta-base\n --task_name MRPC\n --do_train\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --max_seq_length 128\n --per_gpu_eval_batch_size=1\n --per_gpu_train_batch_size=8\n --learning_rate 2e-4\n --num_train_epochs 3\n --overwrite_output_dir\n --seed 42\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --save_steps 0\n --overwrite_cache\n --eval_after_first_stage\n '.split()
self.run_and_check(SCREAMING_SNAKE_CASE )
_a : List[Any] ='\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --eval_each_highway\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(SCREAMING_SNAKE_CASE )
_a : Any ='\n --model_type roberta\n --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --task_name MRPC\n --do_eval\n --do_lower_case\n --data_dir ./tests/fixtures/tests_samples/MRPC/\n --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n --plot_data_dir ./examples/deebert/results/\n --max_seq_length 128\n --early_exit_entropy 0.1\n --eval_highway\n --overwrite_cache\n --per_gpu_eval_batch_size=1\n '.split()
self.run_and_check(SCREAMING_SNAKE_CASE )
| 276 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) <= 1:
return arr, 0
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE ) // 2
__lowerCAmelCase: str = arr[0:mid]
__lowerCAmelCase: int = arr[mid:]
__lowerCAmelCase , __lowerCAmelCase: List[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: int = _count_cross_inversions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: List[str] = 0
while i < len(SCREAMING_SNAKE_CASE ) and j < len(SCREAMING_SNAKE_CASE ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(SCREAMING_SNAKE_CASE ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: str = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# an empty list should also have zero inversions
__lowerCAmelCase: int = []
__lowerCAmelCase: Any = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vector[row][0]
SCREAMING_SNAKE_CASE__ : List[str] =0
SCREAMING_SNAKE_CASE__ : Tuple =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : int =max((abs(augmented[rowa][col] ), rowa) for rowa in range(UpperCamelCase__, UpperCamelCase__ ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : List[str] =0
for cola in range(col + 1, size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1, UpperCamelCase__ ):
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Dict =augmented[row][col] / augmented[col][col]
for cola in range(UpperCamelCase__, size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : str =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : List[str] =y_val
SCREAMING_SNAKE_CASE__ : Any =solve(UpperCamelCase__, UpperCamelCase__ )
def interpolated_func(UpperCamelCase__ : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(UpperCamelCase__ ) )
return interpolated_func
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Optional[Any] =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''') | 152 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__a: Any = logging.get_logger("""transformers.models.speecht5""")
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
hf_model.apply_weight_norm()
lowercase__ : Optional[Any] = checkpoint['input_conv.weight_g']
lowercase__ : List[str] = checkpoint['input_conv.weight_v']
lowercase__ : Any = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
lowercase__ : List[str] = checkpoint[F"""upsamples.{i}.1.weight_g"""]
lowercase__ : int = checkpoint[F"""upsamples.{i}.1.weight_v"""]
lowercase__ : Union[str, Any] = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowercase__ : Tuple = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
lowercase__ : Optional[Any] = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
lowercase__ : List[str] = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
lowercase__ : Any = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
lowercase__ : Optional[Any] = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
lowercase__ : Union[str, Any] = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
lowercase__ : int = checkpoint['output_conv.1.weight_g']
lowercase__ : int = checkpoint['output_conv.1.weight_v']
lowercase__ : str = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , ):
if config_path is not None:
lowercase__ : Optional[int] = SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase )
else:
lowercase__ : Dict = SpeechTaHifiGanConfig()
lowercase__ : Tuple = SpeechTaHifiGan(UpperCAmelCase )
lowercase__ : Tuple = torch.load(UpperCAmelCase )
load_weights(orig_checkpoint['''model''']['''generator'''] , UpperCAmelCase , UpperCAmelCase )
lowercase__ : int = np.load(UpperCAmelCase )
lowercase__ : Optional[Any] = stats[0].reshape(-1 )
lowercase__ : Any = stats[1].reshape(-1 )
lowercase__ : List[str] = torch.from_numpy(UpperCAmelCase ).float()
lowercase__ : Union[str, Any] = torch.from_numpy(UpperCAmelCase ).float()
model.save_pretrained(UpperCAmelCase )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(UpperCAmelCase )
if __name__ == "__main__":
__a: List[str] = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
__a: Tuple = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 198 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ ):
@register_to_config
def __init__( self , lowercase = 768 , ) -> Tuple:
super().__init__()
lowerCamelCase_ = nn.Parameter(torch.zeros(1 , lowercase ) )
lowerCamelCase_ = nn.Parameter(torch.ones(1 , lowercase ) )
def SCREAMING_SNAKE_CASE_( self , lowercase = None , lowercase = None , ) -> Union[str, Any]:
lowerCamelCase_ = nn.Parameter(self.mean.to(lowercase ).to(lowercase ) )
lowerCamelCase_ = nn.Parameter(self.std.to(lowercase ).to(lowercase ) )
return self
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[str]:
lowerCamelCase_ = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Optional[Any]:
lowerCamelCase_ = (embeds * self.std) + self.mean
return embeds
| 19 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.environ['GITHUB_TOKEN'] )
__lowerCAmelCase: Tuple = g.get_repo('huggingface/accelerate' )
__lowerCAmelCase: str = repo.get_issues(state='open' )
for issue in open_issues:
__lowerCAmelCase: Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda SCREAMING_SNAKE_CASE : i.created_at , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = comments[0] if len(SCREAMING_SNAKE_CASE ) > 0 else None
__lowerCAmelCase: Tuple = dt.utcnow()
__lowerCAmelCase: Optional[int] = (current_time - issue.updated_at).days
__lowerCAmelCase: str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 322 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCAmelCase ( _lowerCAmelCase : Dict ):
"""simple docstring"""
UpperCAmelCase__ = filter(lambda _lowerCAmelCase : p.requires_grad , model.parameters() )
UpperCAmelCase__ = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_lowerCAmelCase : int = logging.getLogger(__name__)
def lowerCAmelCase ( _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
if metric == "rouge2":
UpperCAmelCase__ = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
UpperCAmelCase__ = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
UpperCAmelCase__ = '{val_avg_em:.4f}-{step_count}'
elif metric == "loss":
UpperCAmelCase__ = '{val_avg_loss:.4f}-{step_count}'
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
" function." )
UpperCAmelCase__ = ModelCheckpoint(
dirpath=_lowerCAmelCase , filename=_lowerCAmelCase , monitor=F'''val_{metric}''' , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCAmelCase ( _lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
"""simple docstring"""
return EarlyStopping(
monitor=F'''val_{metric}''' , mode="min" if "loss" in metric else "max" , patience=_lowerCAmelCase , verbose=_lowerCAmelCase , )
class _UpperCamelCase ( pl.Callback ):
def UpperCAmelCase_ ( self :Any , lowerCamelCase :str , lowerCamelCase :Any ) -> List[Any]:
UpperCAmelCase__ = {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCamelCase )
@rank_zero_only
def UpperCAmelCase_ ( self :Optional[int] , lowerCamelCase :pl.Trainer , lowerCamelCase :pl.LightningModule , lowerCamelCase :str , lowerCamelCase :Tuple=True ) -> None:
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
UpperCAmelCase__ = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
UpperCAmelCase__ = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCAmelCase__ = od / 'test_results.txt'
UpperCAmelCase__ = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCAmelCase__ = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
UpperCAmelCase__ = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=lowerCamelCase )
generations_file.parent.mkdir(exist_ok=lowerCamelCase )
with open(lowerCamelCase , "a+" ) as writer:
for key in sorted(lowerCamelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCAmelCase__ = metrics[key]
if isinstance(lowerCamelCase , torch.Tensor ):
UpperCAmelCase__ = val.item()
UpperCAmelCase__ = f'''{key}: {val:.6f}\n'''
writer.write(lowerCamelCase )
if not save_generations:
return
if "preds" in metrics:
UpperCAmelCase__ = '\n'.join(metrics["preds"] )
generations_file.open("w+" ).write(lowerCamelCase )
@rank_zero_only
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :List[str] , lowerCamelCase :List[str] ) -> Any:
try:
UpperCAmelCase__ = pl_module.model.model.num_parameters()
except AttributeError:
UpperCAmelCase__ = pl_module.model.num_parameters()
UpperCAmelCase__ = count_trainable_parameters(lowerCamelCase )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )
@rank_zero_only
def UpperCAmelCase_ ( self :Union[str, Any] , lowerCamelCase :pl.Trainer , lowerCamelCase :pl.LightningModule ) -> List[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCamelCase , lowerCamelCase , "test" )
@rank_zero_only
def UpperCAmelCase_ ( self :str , lowerCamelCase :pl.Trainer , lowerCamelCase :List[Any] ) -> List[str]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 169 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 322 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
BarkConfig,
BarkFineConfig,
BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
set_seed(7_70)
__UpperCAmelCase = {
"c_attn": "att_proj",
"c_proj": "out_proj",
"c_fc": "in_proj",
"transformer.": "",
"h.": "layers.",
"ln_1": "layernorm_1",
"ln_2": "layernorm_2",
"ln_f": "layernorm_final",
"wpe": "position_embeds_layer",
"wte": "input_embeds_layer",
}
__UpperCAmelCase = {
"text_small": {
"repo_id": "suno/bark",
"file_name": "text.pt",
},
"coarse_small": {
"repo_id": "suno/bark",
"file_name": "coarse.pt",
},
"fine_small": {
"repo_id": "suno/bark",
"file_name": "fine.pt",
},
"text": {
"repo_id": "suno/bark",
"file_name": "text_2.pt",
},
"coarse": {
"repo_id": "suno/bark",
"file_name": "coarse_2.pt",
},
"fine": {
"repo_id": "suno/bark",
"file_name": "fine_2.pt",
},
}
__UpperCAmelCase = os.path.dirname(os.path.abspath(__file__))
__UpperCAmelCase = os.path.join(os.path.expanduser("~"), ".cache")
__UpperCAmelCase = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")
def A__ ( __lowerCamelCase, __lowerCamelCase=False ):
SCREAMING_SNAKE_CASE_ = model_type
if use_small:
key += "_small"
return os.path.join(__lowerCamelCase, REMOTE_MODEL_PATHS[key]['''file_name'''] )
def A__ ( __lowerCamelCase, __lowerCamelCase ):
os.makedirs(__lowerCamelCase, exist_ok=__lowerCamelCase )
hf_hub_download(repo_id=__lowerCamelCase, filename=__lowerCamelCase, local_dir=__lowerCamelCase )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase=False, __lowerCamelCase="text" ):
if model_type == "text":
SCREAMING_SNAKE_CASE_ = BarkSemanticModel
SCREAMING_SNAKE_CASE_ = BarkSemanticConfig
SCREAMING_SNAKE_CASE_ = BarkSemanticGenerationConfig
elif model_type == "coarse":
SCREAMING_SNAKE_CASE_ = BarkCoarseModel
SCREAMING_SNAKE_CASE_ = BarkCoarseConfig
SCREAMING_SNAKE_CASE_ = BarkCoarseGenerationConfig
elif model_type == "fine":
SCREAMING_SNAKE_CASE_ = BarkFineModel
SCREAMING_SNAKE_CASE_ = BarkFineConfig
SCREAMING_SNAKE_CASE_ = BarkFineGenerationConfig
else:
raise NotImplementedError()
SCREAMING_SNAKE_CASE_ = F'''{model_type}_small''' if use_small else model_type
SCREAMING_SNAKE_CASE_ = REMOTE_MODEL_PATHS[model_key]
if not os.path.exists(__lowerCamelCase ):
logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.''' )
_download(model_info['''repo_id'''], model_info['''file_name'''] )
SCREAMING_SNAKE_CASE_ = torch.load(__lowerCamelCase, map_location=__lowerCamelCase )
# this is a hack
SCREAMING_SNAKE_CASE_ = checkpoint['model_args']
if "input_vocab_size" not in model_args:
SCREAMING_SNAKE_CASE_ = model_args['vocab_size']
SCREAMING_SNAKE_CASE_ = model_args['vocab_size']
del model_args["vocab_size"]
# convert Bark model arguments to HF Bark model arguments
SCREAMING_SNAKE_CASE_ = model_args.pop('''n_head''' )
SCREAMING_SNAKE_CASE_ = model_args.pop('''n_embd''' )
SCREAMING_SNAKE_CASE_ = model_args.pop('''n_layer''' )
SCREAMING_SNAKE_CASE_ = ConfigClass(**checkpoint['''model_args'''] )
SCREAMING_SNAKE_CASE_ = ModelClass(config=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = GenerationConfigClass()
SCREAMING_SNAKE_CASE_ = model_generation_config
SCREAMING_SNAKE_CASE_ = checkpoint['model']
# fixup checkpoint
SCREAMING_SNAKE_CASE_ = '_orig_mod.'
for k, v in list(state_dict.items() ):
if k.startswith(__lowerCamelCase ):
# replace part of the key with corresponding layer name in HF implementation
SCREAMING_SNAKE_CASE_ = k[len(__lowerCamelCase ) :]
for old_layer_name in new_layer_name_dict:
SCREAMING_SNAKE_CASE_ = new_k.replace(__lowerCamelCase, new_layer_name_dict[old_layer_name] )
SCREAMING_SNAKE_CASE_ = state_dict.pop(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = set(state_dict.keys() ) - set(model.state_dict().keys() )
SCREAMING_SNAKE_CASE_ = {k for k in extra_keys if not k.endswith('''.attn.bias''' )}
SCREAMING_SNAKE_CASE_ = set(model.state_dict().keys() ) - set(state_dict.keys() )
SCREAMING_SNAKE_CASE_ = {k for k in missing_keys if not k.endswith('''.attn.bias''' )}
if len(__lowerCamelCase ) != 0:
raise ValueError(F'''extra keys found: {extra_keys}''' )
if len(__lowerCamelCase ) != 0:
raise ValueError(F'''missing keys: {missing_keys}''' )
model.load_state_dict(__lowerCamelCase, strict=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = model.num_parameters(exclude_embeddings=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = checkpoint['best_val_loss'].item()
logger.info(F'''model loaded: {round(n_params/1E6, 1 )}M params, {round(__lowerCamelCase, 3 )} loss''' )
model.eval()
model.to(__lowerCamelCase )
del checkpoint, state_dict
return model
def A__ ( __lowerCamelCase, __lowerCamelCase=False, __lowerCamelCase="text" ):
if model_type not in ("text", "coarse", "fine"):
raise NotImplementedError()
SCREAMING_SNAKE_CASE_ = 'cpu' # do conversion on cpu
SCREAMING_SNAKE_CASE_ = _get_ckpt_path(__lowerCamelCase, use_small=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = _load_model(__lowerCamelCase, __lowerCamelCase, model_type=__lowerCamelCase, use_small=__lowerCamelCase )
# load bark initial model
SCREAMING_SNAKE_CASE_ = _bark_load_model(__lowerCamelCase, '''cpu''', model_type=__lowerCamelCase, use_small=__lowerCamelCase )
if model_type == "text":
SCREAMING_SNAKE_CASE_ = bark_model['model']
if model.num_parameters(exclude_embeddings=__lowerCamelCase ) != bark_model.get_num_params():
raise ValueError('''initial and new models don\'t have the same number of parameters''' )
# check if same output as the bark model
SCREAMING_SNAKE_CASE_ = 5
SCREAMING_SNAKE_CASE_ = 10
if model_type in ["text", "coarse"]:
SCREAMING_SNAKE_CASE_ = torch.randint(2_56, (batch_size, sequence_length), dtype=torch.int )
SCREAMING_SNAKE_CASE_ = bark_model(__lowerCamelCase )[0]
SCREAMING_SNAKE_CASE_ = model(__lowerCamelCase )
# take last logits
SCREAMING_SNAKE_CASE_ = output_new_model_total.logits[:, [-1], :]
else:
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = 8
SCREAMING_SNAKE_CASE_ = torch.randint(2_56, (batch_size, sequence_length, n_codes_total), dtype=torch.int )
SCREAMING_SNAKE_CASE_ = model(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = bark_model(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = output_new_model_total.logits
# output difference should come from the difference of self-attention implementation design
if output_new_model.shape != output_old_model.shape:
raise ValueError('''initial and new outputs don\'t have the same shape''' )
if (output_new_model - output_old_model).abs().max().item() > 1E-3:
raise ValueError('''initial and new outputs are not equal''' )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, ):
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase, '''config.json''' ) )
SCREAMING_SNAKE_CASE_ = BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase, '''config.json''' ) )
SCREAMING_SNAKE_CASE_ = BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase, '''config.json''' ) )
SCREAMING_SNAKE_CASE_ = EncodecConfig.from_pretrained('''facebook/encodec_24khz''' )
SCREAMING_SNAKE_CASE_ = BarkSemanticModel.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = BarkCoarseModel.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = BarkFineModel.from_pretrained(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = EncodecModel.from_pretrained('''facebook/encodec_24khz''' )
SCREAMING_SNAKE_CASE_ = BarkConfig.from_sub_model_configs(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = BarkGenerationConfig.from_sub_model_configs(
semantic.generation_config, coarseAcoustic.generation_config, fineAcoustic.generation_config )
SCREAMING_SNAKE_CASE_ = BarkModel(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = semantic
SCREAMING_SNAKE_CASE_ = coarseAcoustic
SCREAMING_SNAKE_CASE_ = fineAcoustic
SCREAMING_SNAKE_CASE_ = codec
SCREAMING_SNAKE_CASE_ = bark_generation_config
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
bark.save_pretrained(__lowerCamelCase, repo_id=__lowerCamelCase, push_to_hub=__lowerCamelCase )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("model_type", type=str, help="text, coarse or fine.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.")
__UpperCAmelCase = parser.parse_args()
load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
| 299 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Tuple , UpperCAmelCase : float , UpperCAmelCase : Callable , UpperCAmelCase : int , UpperCAmelCase : float = 1.0 , UpperCAmelCase : str = None , ) -> Union[str, Any]:
super().__init__()
__lowerCAmelCase: Optional[Any] = initial_learning_rate
__lowerCAmelCase: str = warmup_steps
__lowerCAmelCase: Optional[int] = power
__lowerCAmelCase: str = decay_schedule_fn
__lowerCAmelCase: Tuple = name
def __call__( self : int , UpperCAmelCase : Dict ) -> Optional[int]:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCAmelCase: List[str] = tf.cast(UpperCAmelCase , tf.floataa )
__lowerCAmelCase: Tuple = tf.cast(self.warmup_steps , tf.floataa )
__lowerCAmelCase: List[str] = global_step_float / warmup_steps_float
__lowerCAmelCase: List[str] = self.initial_learning_rate * tf.math.pow(UpperCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=UpperCAmelCase , )
def UpperCAmelCase ( self : Tuple ) -> int:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 0.9 , SCREAMING_SNAKE_CASE : float = 0.9_9_9 , SCREAMING_SNAKE_CASE : float = 1E-8 , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 , SCREAMING_SNAKE_CASE : Optional[List[str]] = None , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=SCREAMING_SNAKE_CASE , )
if num_warmup_steps:
__lowerCAmelCase: Optional[int] = WarmUp(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_schedule_fn=SCREAMING_SNAKE_CASE , warmup_steps=SCREAMING_SNAKE_CASE , )
if weight_decay_rate > 0.0:
__lowerCAmelCase: List[Any] = AdamWeightDecay(
learning_rate=SCREAMING_SNAKE_CASE , weight_decay_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase: Dict = tf.keras.optimizers.Adam(
learning_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , UpperCAmelCase : float = 0.9 , UpperCAmelCase : float = 0.999 , UpperCAmelCase : float = 1E-7 , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : str = "AdamWeightDecay" , **UpperCAmelCase : str , ) -> int:
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
__lowerCAmelCase: List[Any] = weight_decay_rate
__lowerCAmelCase: List[str] = include_in_weight_decay
__lowerCAmelCase: Optional[Any] = exclude_from_weight_decay
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Tuple ) -> Optional[int]:
__lowerCAmelCase: Union[str, Any] = {'WarmUp': WarmUp}
return super(UpperCAmelCase , cls ).from_config(UpperCAmelCase , custom_objects=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
super(UpperCAmelCase , self )._prepare_local(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> List[str]:
__lowerCAmelCase: Dict = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None , **UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase: Tuple = list(zip(*UpperCAmelCase ) )
return super(UpperCAmelCase , self ).apply_gradients(zip(UpperCAmelCase , UpperCAmelCase ) , name=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCAmelCase: Dict = apply_state or {}
__lowerCAmelCase: Union[str, Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCAmelCase: str = self._fallback_apply_state(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Tuple = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any]=None ) -> List[Any]:
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_dense(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[Any]=None ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase: Any = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: str = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_sparse(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase: List[str] = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return False
return True
class A_ ( snake_case__ ):
def __init__( self : int ) -> List[Any]:
__lowerCAmelCase: Tuple = []
__lowerCAmelCase: int = None
@property
def UpperCAmelCase ( self : Dict ) -> List[Any]:
if self._accum_steps is None:
__lowerCAmelCase: List[Any] = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Optional[Any] , UpperCAmelCase : Any ) -> Any:
if not self._gradients:
__lowerCAmelCase: Any = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(UpperCAmelCase ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(UpperCAmelCase ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(UpperCAmelCase )}''' )
for accum_gradient, gradient in zip(self._gradients , UpperCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(UpperCAmelCase )
self._accum_steps.assign_add(1 )
def UpperCAmelCase ( self : int ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(UpperCAmelCase ) )
| 322 | 0 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def snake_case_ (_a : Any , _a : Optional[int] , _a : Union[str, Any] , _a : int=1_0_2_4 ):
UpperCAmelCase = [], []
UpperCAmelCase = list(zip(_a , _a ) )
UpperCAmelCase = sorted_examples[0]
def is_too_big(_a : Tuple ):
return tok(_a , return_tensors='''pt''' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
UpperCAmelCase = new_src + ' ' + src
UpperCAmelCase = new_tgt + ' ' + tgt
if is_too_big(_a ) or is_too_big(_a ): # cant fit, finalize example
finished_src.append(_a )
finished_tgt.append(_a )
UpperCAmelCase = src, tgt
else: # can fit, keep adding
UpperCAmelCase = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(_a )
finished_tgt.append(_a )
return finished_src, finished_tgt
def snake_case_ (_a : Optional[Any] , _a : Path , _a : Dict , _a : Optional[int] ):
UpperCAmelCase = Path(_a )
save_path.mkdir(exist_ok=_a )
for split in ["train"]:
UpperCAmelCase = data_dir / F"{split}.source", data_dir / F"{split}.target"
UpperCAmelCase = [x.rstrip() for x in Path(_a ).open().readlines()]
UpperCAmelCase = [x.rstrip() for x in Path(_a ).open().readlines()]
UpperCAmelCase = pack_examples(_a , _a , _a , _a )
print(F"packed {split} split from {len(_a )} examples -> {len(_a )}." )
Path(save_path / F"{split}.source" ).open('''w''' ).write('''\n'''.join(_a ) )
Path(save_path / F"{split}.target" ).open('''w''' ).write('''\n'''.join(_a ) )
for split in ["val", "test"]:
UpperCAmelCase = data_dir / F"{split}.source", data_dir / F"{split}.target"
shutil.copyfile(_a , save_path / F"{split}.source" )
shutil.copyfile(_a , save_path / F"{split}.target" )
def snake_case_ ():
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--tok_name''' , type=_a , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''--max_seq_len''' , type=_a , default=1_2_8 )
parser.add_argument('''--data_dir''' , type=_a )
parser.add_argument('''--save_path''' , type=_a )
UpperCAmelCase = parser.parse_args()
UpperCAmelCase = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(_a , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 34 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any]=[] ) -> str:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = size[0] - overlap_pixels * 2
__lowerCAmelCase: str = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__lowerCAmelCase: Any = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
__lowerCAmelCase: int = np.pad(SCREAMING_SNAKE_CASE , mode='linear_ramp' , pad_width=SCREAMING_SNAKE_CASE , end_values=0 )
if "l" in remove_borders:
__lowerCAmelCase: Dict = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__lowerCAmelCase: Tuple = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__lowerCAmelCase: List[Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__lowerCAmelCase: List[str] = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
return max(SCREAMING_SNAKE_CASE , min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] ) -> int:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : [int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = list(SCREAMING_SNAKE_CASE )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__lowerCAmelCase: int = clamp_rect(SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase: List[Any] = Image.new('RGB' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(SCREAMING_SNAKE_CASE , (original_slice, 0) )
return result
def _a ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__lowerCAmelCase: List[Any] = tile.crop(SCREAMING_SNAKE_CASE )
return tile
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = n % d
return n - divisor
class A_ ( snake_case__ ):
def __init__( self : Optional[Any] , UpperCAmelCase : AutoencoderKL , UpperCAmelCase : CLIPTextModel , UpperCAmelCase : CLIPTokenizer , UpperCAmelCase : UNetaDConditionModel , UpperCAmelCase : DDPMScheduler , UpperCAmelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase : int = 3_5_0 , ) -> Optional[Any]:
super().__init__(
vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , low_res_scheduler=UpperCAmelCase , scheduler=UpperCAmelCase , max_noise_level=UpperCAmelCase , )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ) -> Optional[int]:
torch.manual_seed(0 )
__lowerCAmelCase: Optional[int] = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__lowerCAmelCase: Optional[Any] = add_overlap_rect(UpperCAmelCase , UpperCAmelCase , image.size )
__lowerCAmelCase: Any = image.crop(UpperCAmelCase )
__lowerCAmelCase: Any = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__lowerCAmelCase: Tuple = translated_slice_x - (original_image_slice / 2)
__lowerCAmelCase: Union[str, Any] = max(0 , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = squeeze_tile(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = to_input.size
__lowerCAmelCase: List[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__lowerCAmelCase: int = super(UpperCAmelCase , self ).__call__(image=UpperCAmelCase , **UpperCAmelCase ).images[0]
__lowerCAmelCase: Dict = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Union[str, Any] = unsqueeze_tile(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Optional[int] = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
__lowerCAmelCase: int = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=UpperCAmelCase ) , mode='L' , )
final_image.paste(
UpperCAmelCase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , UpperCAmelCase )
@torch.no_grad()
def __call__( self : Optional[Any] , UpperCAmelCase : Union[str, List[str]] , UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , UpperCAmelCase : int = 7_5 , UpperCAmelCase : float = 9.0 , UpperCAmelCase : int = 5_0 , UpperCAmelCase : Optional[Union[str, List[str]]] = None , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[torch.Generator] = None , UpperCAmelCase : Optional[torch.FloatTensor] = None , UpperCAmelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 1_2_8 , UpperCAmelCase : int = 3_2 , UpperCAmelCase : int = 3_2 , ) -> str:
__lowerCAmelCase: List[Any] = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
__lowerCAmelCase: str = math.ceil(image.size[0] / tile_size )
__lowerCAmelCase: List[Any] = math.ceil(image.size[1] / tile_size )
__lowerCAmelCase: Optional[Any] = tcx * tcy
__lowerCAmelCase: Tuple = 0
for y in range(UpperCAmelCase ):
for x in range(UpperCAmelCase ):
self._process_tile(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , prompt=UpperCAmelCase , num_inference_steps=UpperCAmelCase , guidance_scale=UpperCAmelCase , noise_level=UpperCAmelCase , negative_prompt=UpperCAmelCase , num_images_per_prompt=UpperCAmelCase , eta=UpperCAmelCase , generator=UpperCAmelCase , latents=UpperCAmelCase , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: Any = 'stabilityai/stable-diffusion-x4-upscaler'
__lowerCAmelCase: Dict = StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE , revision='fp16' , torch_dtype=torch.floataa )
__lowerCAmelCase: Optional[Any] = pipe.to('cuda' )
__lowerCAmelCase: Tuple = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(SCREAMING_SNAKE_CASE : Tuple ):
print(f'''progress: {obj['progress']:.4f}''' )
obj["image"].save('diffusers_library_progress.jpg' )
__lowerCAmelCase: str = pipe(image=SCREAMING_SNAKE_CASE , prompt='Black font, white background, vector' , noise_level=40 , callback=SCREAMING_SNAKE_CASE )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
def a_ ( _lowerCAmelCase ) -> bool:
if len(_lowerCAmelCase ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values must be greater than 0' )
__lowerCamelCase : Dict = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 208 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowerCAmelCase: Tuple = True
for i in range(1 , s + 1 ):
__lowerCAmelCase: Any = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowerCAmelCase: Optional[int] = dp[i][j - 1]
if arr[i - 1] <= j:
__lowerCAmelCase: Union[str, Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowerCAmelCase: Tuple = s - 2 * j
break
return diff
| 322 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = []
if isinstance(snake_case , snake_case ):
for v in tree.values():
shapes.extend(_fetch_dims(snake_case ) )
elif isinstance(snake_case , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(snake_case ) )
elif isinstance(snake_case , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
for d in reversed(snake_case ):
idx.append(flat_idx % d )
__SCREAMING_SNAKE_CASE : Any = flat_idx // d
return tuple(reversed(snake_case ) )
@torch.jit.ignore
def a__ ( snake_case , snake_case , snake_case , snake_case = None , snake_case = None , ):
"""simple docstring"""
def reduce_edge_list(snake_case ) -> None:
__SCREAMING_SNAKE_CASE : Dict = True
for i in range(len(snake_case ) ):
__SCREAMING_SNAKE_CASE : int = -1 * (i + 1)
l[reversed_idx] &= tally
__SCREAMING_SNAKE_CASE : List[Any] = l[reversed_idx]
if start_edges is None:
__SCREAMING_SNAKE_CASE : Optional[int] = [s == 0 for s in start]
reduce_edge_list(snake_case )
if end_edges is None:
__SCREAMING_SNAKE_CASE : Optional[Any] = [e == (d - 1) for e, d in zip(snake_case , snake_case )]
reduce_edge_list(snake_case )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(snake_case ) == 0:
return [()]
elif len(snake_case ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__SCREAMING_SNAKE_CASE : List[Tuple[slice, ...]] = []
__SCREAMING_SNAKE_CASE : List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(snake_case , snake_case ):
if s == e:
path_list.append(slice(snake_case , s + 1 ) )
else:
break
__SCREAMING_SNAKE_CASE : Tuple[slice, ...] = tuple(snake_case )
__SCREAMING_SNAKE_CASE : Any = len(snake_case )
# start == end, and we're done
if divergence_idx == len(snake_case ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__SCREAMING_SNAKE_CASE : Union[str, Any] = start[divergence_idx]
return tuple(
path + (slice(snake_case , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__SCREAMING_SNAKE_CASE : int = end[divergence_idx]
return tuple(
path + (slice(snake_case , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__SCREAMING_SNAKE_CASE : List[Any] = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = t.shape[:no_batch_dims]
__SCREAMING_SNAKE_CASE : Any = list(_flat_idx_to_idx(snake_case , snake_case ) )
# _get_minimal_slice_set is inclusive
__SCREAMING_SNAKE_CASE : Optional[int] = list(_flat_idx_to_idx(flat_end - 1 , snake_case ) )
# Get an ordered list of slices to perform
__SCREAMING_SNAKE_CASE : Tuple = _get_minimal_slice_set(
snake_case , snake_case , snake_case , )
__SCREAMING_SNAKE_CASE : Optional[int] = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def a__ ( snake_case , snake_case , snake_case , snake_case , snake_case = False , snake_case = None , snake_case = False , ):
"""simple docstring"""
if not (len(snake_case ) > 0):
raise ValueError('''Must provide at least one input''' )
__SCREAMING_SNAKE_CASE : int = [shape[:no_batch_dims] for shape in _fetch_dims(snake_case )]
__SCREAMING_SNAKE_CASE : Union[str, Any] = tuple([max(snake_case ) for s in zip(*snake_case )] )
def _prep_inputs(snake_case ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__SCREAMING_SNAKE_CASE : Union[str, Any] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__SCREAMING_SNAKE_CASE : Tuple = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__SCREAMING_SNAKE_CASE : Dict[str, Any] = tensor_tree_map(_prep_inputs , snake_case )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
if _out is not None:
__SCREAMING_SNAKE_CASE : List[str] = tensor_tree_map(lambda snake_case : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__SCREAMING_SNAKE_CASE : Any = 1
for d in orig_batch_dims:
flat_batch_dim *= d
__SCREAMING_SNAKE_CASE : List[str] = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(snake_case ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__SCREAMING_SNAKE_CASE : Any = 0
__SCREAMING_SNAKE_CASE : Optional[int] = prepped_outputs
for _ in range(snake_case ):
# Chunk the input
if not low_mem:
__SCREAMING_SNAKE_CASE : str = _select_chunk
else:
__SCREAMING_SNAKE_CASE : Dict = partial(
_chunk_slice , flat_start=snake_case , flat_end=min(snake_case , i + chunk_size ) , no_batch_dims=len(snake_case ) , )
__SCREAMING_SNAKE_CASE : Dict[str, Any] = tensor_tree_map(snake_case , snake_case )
# Run the layer on the chunk
__SCREAMING_SNAKE_CASE : Optional[int] = layer(**snake_case )
# Allocate space for the output
if out is None:
__SCREAMING_SNAKE_CASE : Optional[int] = tensor_tree_map(lambda snake_case : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , snake_case )
# Put the chunk in its pre-allocated space
if isinstance(snake_case , snake_case ):
def assign(snake_case , snake_case ) -> None:
for k, v in da.items():
if isinstance(snake_case , snake_case ):
assign(snake_case , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = da[k]
assign(snake_case , snake_case )
elif isinstance(snake_case , snake_case ):
for xa, xa in zip(snake_case , snake_case ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__SCREAMING_SNAKE_CASE : Dict = xa
elif isinstance(snake_case , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__SCREAMING_SNAKE_CASE : Dict = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = tensor_tree_map(lambda snake_case : t.view(orig_batch_dims + t.shape[1:] ) , snake_case )
return out
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , _A : int = 512 , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = max_chunk_size
__SCREAMING_SNAKE_CASE : Optional[int] = None
__SCREAMING_SNAKE_CASE : Optional[tuple] = None
def UpperCAmelCase__ ( self : List[Any] , _A : Callable , _A : tuple , _A : int ):
"""simple docstring"""
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__SCREAMING_SNAKE_CASE : List[int] = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
__SCREAMING_SNAKE_CASE : Tuple = [c for c in candidates if c > min_chunk_size]
__SCREAMING_SNAKE_CASE : int = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_A : int ) -> bool:
try:
with torch.no_grad():
fn(*_A , chunk_size=_A )
return True
except RuntimeError:
return False
__SCREAMING_SNAKE_CASE : Optional[Any] = 0
__SCREAMING_SNAKE_CASE : Any = len(_A ) - 1
while i > min_viable_chunk_size_index:
__SCREAMING_SNAKE_CASE : Tuple = test_chunk_size(candidates[i] )
if not viable:
__SCREAMING_SNAKE_CASE : Dict = (min_viable_chunk_size_index + i) // 2
else:
__SCREAMING_SNAKE_CASE : Optional[int] = i
__SCREAMING_SNAKE_CASE : Union[str, Any] = (i + len(_A ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def UpperCAmelCase__ ( self : Optional[int] , _A : Iterable , _A : Iterable ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = True
for aa, aa in zip(_A , _A ):
assert type(_A ) == type(_A )
if isinstance(_A , (list, tuple) ):
consistent &= self._compare_arg_caches(_A , _A )
elif isinstance(_A , _A ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [v for _, v in sorted(aa.items() , key=lambda _A : x[0] )]
__SCREAMING_SNAKE_CASE : Tuple = [v for _, v in sorted(aa.items() , key=lambda _A : x[0] )]
consistent &= self._compare_arg_caches(_A , _A )
else:
consistent &= aa == aa
return consistent
def UpperCAmelCase__ ( self : Dict , _A : Callable , _A : tuple , _A : int , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : tuple = tree_map(lambda _A : a.shape if isinstance(_A , torch.Tensor ) else a , _A , _A )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._compare_arg_caches(self.cached_arg_data , _A )
else:
# Otherwise, we can reuse the precomputed value
__SCREAMING_SNAKE_CASE : str = False
if not consistent:
__SCREAMING_SNAKE_CASE : Dict = self._determine_favorable_chunk_size(
_A , _A , _A , )
__SCREAMING_SNAKE_CASE : Tuple = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 303 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__lowerCAmelCase: Tuple = i + 1
else:
__lowerCAmelCase: List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 1_1, 1_5], 9) = }")
| 322 | 0 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
snake_case_ = ["""text""", """image""", """audio"""]
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = []
for input_type in input_types:
if input_type == "text":
inputs.append('Text input' )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO' ) ) / '000000039769.png' ).resize((512, 512) ) )
elif input_type == "audio":
inputs.append(torch.ones(3000 ) )
elif isinstance(lowercase_ , lowercase_ ):
inputs.append(create_inputs(lowercase_ ) )
else:
raise ValueError(F"""Invalid type requested: {input_type}""" )
return inputs
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = []
for output in outputs:
if isinstance(lowercase_ , (str, AgentText) ):
output_types.append('text' )
elif isinstance(lowercase_ , (Image.Image, AgentImage) ):
output_types.append('image' )
elif isinstance(lowercase_ , (torch.Tensor, AgentAudio) ):
output_types.append('audio' )
else:
raise ValueError(F"""Invalid output: {output}""" )
return output_types
@is_tool_test
class A_ :
"""simple docstring"""
def UpperCAmelCase__ ( self :str ) -> Optional[int]:
self.assertTrue(hasattr(self.tool , 'inputs' ) )
self.assertTrue(hasattr(self.tool , 'outputs' ) )
UpperCAmelCase = self.tool.inputs
for _input in inputs:
if isinstance(_input , lowercase_ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
UpperCAmelCase = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCAmelCase__ ( self :Union[str, Any] ) -> int:
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = self.tool(*lowercase_ )
# There is a single output
if len(self.tool.outputs ) == 1:
UpperCAmelCase = [outputs]
self.assertListEqual(output_types(lowercase_ ) , self.tool.outputs )
def UpperCAmelCase__ ( self :int ) -> Union[str, Any]:
self.assertTrue(hasattr(self.tool , 'description' ) )
self.assertTrue(hasattr(self.tool , 'default_checkpoint' ) )
self.assertTrue(self.tool.description.startswith('This is a tool that' ) )
def UpperCAmelCase__ ( self :Tuple ) -> Tuple:
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = self.tool(*lowercase_ )
if not isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [outputs]
self.assertEqual(len(lowercase_ ) , len(self.tool.outputs ) )
for output, output_type in zip(lowercase_ , self.tool.outputs ):
UpperCAmelCase = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
def UpperCAmelCase__ ( self :List[str] ) -> Any:
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = []
for _input, input_type in zip(lowercase_ , self.tool.inputs ):
if isinstance(lowercase_ , lowercase_ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
UpperCAmelCase = self.tool(*lowercase_ )
if not isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = [outputs]
self.assertEqual(len(lowercase_ ) , len(self.tool.outputs ) )
| 78 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_a = '''scheduler_config.json'''
class A_ ( snake_case__ ):
_lowercase : Optional[Any] = 1
_lowercase : Tuple = 2
_lowercase : Dict = 3
_lowercase : int = 4
_lowercase : Optional[Any] = 5
@dataclass
class A_ ( snake_case__ ):
_lowercase : jnp.ndarray
class A_ :
_lowercase : Optional[int] = SCHEDULER_CONFIG_NAME
_lowercase : Dict = ['dtype']
_lowercase : int = []
_lowercase : Union[str, Any] = True
@classmethod
def UpperCAmelCase ( cls : Union[str, Any] , UpperCAmelCase : Dict[str, Any] = None , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : List[str]=False , **UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCAmelCase , subfolder=UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase , )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.from_config(UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase )
if hasattr(UpperCAmelCase , 'create_state' ) and getattr(UpperCAmelCase , 'has_state' , UpperCAmelCase ):
__lowerCAmelCase: Dict = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, os.PathLike] , UpperCAmelCase : bool = False , **UpperCAmelCase : Any ) -> List[str]:
self.save_config(save_directory=UpperCAmelCase , push_to_hub=UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : str ) -> Dict:
return self._get_compatibles()
@classmethod
def UpperCAmelCase ( cls : Optional[int] ) -> Any:
__lowerCAmelCase: Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
__lowerCAmelCase: Dict = importlib.import_module(__name__.split('.' )[0] )
__lowerCAmelCase: Dict = [
getattr(UpperCAmelCase , UpperCAmelCase ) for c in compatible_classes_str if hasattr(UpperCAmelCase , UpperCAmelCase )
]
return compatible_classes
def _a ( SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : Tuple[int] ) -> jnp.ndarray:
"""simple docstring"""
assert len(SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE ) - x.ndim) ) , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any=0.9_9_9 , SCREAMING_SNAKE_CASE : List[Any]=jnp.floataa ) -> jnp.ndarray:
"""simple docstring"""
def alpha_bar(SCREAMING_SNAKE_CASE : str ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowerCAmelCase: str = []
for i in range(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Union[str, Any] = i / num_diffusion_timesteps
__lowerCAmelCase: List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE ) / alpha_bar(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) )
return jnp.array(SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class A_ :
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Optional[int] ) -> Any:
__lowerCAmelCase: str = scheduler.config
if config.trained_betas is not None:
__lowerCAmelCase: Tuple = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowerCAmelCase: Any = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCAmelCase: List[Any] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCAmelCase: str = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
__lowerCAmelCase: Optional[Any] = 1.0 - betas
__lowerCAmelCase: Optional[Any] = jnp.cumprod(UpperCAmelCase , axis=0 )
return cls(
alphas=UpperCAmelCase , betas=UpperCAmelCase , alphas_cumprod=UpperCAmelCase , )
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = state.alphas_cumprod
__lowerCAmelCase: str = alphas_cumprod[timesteps] ** 0.5
__lowerCAmelCase: Any = sqrt_alpha_prod.flatten()
__lowerCAmelCase: Any = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
__lowerCAmelCase: Any = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCAmelCase: str = sqrt_one_minus_alpha_prod.flatten()
__lowerCAmelCase: str = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Tuple = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 322 | 0 |
def a__ ( A_, A_, A_, A_ ):
'''simple docstring'''
__magic_name__ = len(A_ ), len(grid[0] )
if (
min(A_, A_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__magic_name__ = 0
count += depth_first_search(A_, row + 1, A_, A_ )
count += depth_first_search(A_, row - 1, A_, A_ )
count += depth_first_search(A_, A_, col + 1, A_ )
count += depth_first_search(A_, A_, col - 1, A_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 88 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ) -> list[str]:
"""simple docstring"""
__lowerCAmelCase: int = set()
# keep track of all the paths to be checked
__lowerCAmelCase: str = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: Union[str, Any] = path[-1]
if node not in explored:
__lowerCAmelCase: Dict = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
new_path.append(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Optional[int] = [start]
__lowerCAmelCase: Dict = set(SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: Optional[int] = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Any = queue.pop(0 )
if node == target:
__lowerCAmelCase: Optional[int] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 322 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
A__: Optional[Any] = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S 9D TH AD''',
'''KS 8D 4D 9S 4S''', # pair
'''8C 4S KH JS 4D''', # pair
'''QH 8H KD JH 8S''', # pair
'''KC 4H KS 2H 8D''', # pair
'''KD 4S KC 3H 8S''', # pair
'''AH 8S AS KC JH''', # pair
'''3H 4C 4H 3S 2H''', # 2 pairs
'''5S 5D 2C KH KH''', # 2 pairs
'''3C KH 5D 5S KH''', # 2 pairs
'''AS 3C KH AD KH''', # 2 pairs
'''7C 7S 3S 7H 5S''', # 3 of a kind
'''7C 7S KH 2H 7H''', # 3 of a kind
'''AC KH QH AH AS''', # 3 of a kind
'''2H 4D 3C AS 5S''', # straight (low ace)
'''3C 5C 4C 2C 6H''', # straight
'''6S 8S 7S 5H 9H''', # straight
'''JS QS 9H TS KH''', # straight
'''QC KH TS JS AH''', # straight (high ace)
'''8C 9C 5C 3C TC''', # flush
'''3S 8S 9S 5S KS''', # flush
'''4C 5C 9C 8C KC''', # flush
'''JH 8H AH KH QH''', # flush
'''3D 2H 3H 2C 2D''', # full house
'''2H 2C 3S 3H 3D''', # full house
'''KH KC 3S 3H 3D''', # full house
'''JC 6H JS JD JH''', # 4 of a kind
'''JC 7H JS JD JH''', # 4 of a kind
'''JC KH JS JD JH''', # 4 of a kind
'''2S AS 4S 5S 3S''', # straight flush (low ace)
'''2D 6D 3D 4D 5D''', # straight flush
'''5C 6C 3C 7C 4C''', # straight flush
'''JH 9H TH KH QH''', # straight flush
'''JH AH TH KH QH''', # royal flush (high ace straight flush)
)
A__: int = (
('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)
A__: List[str] = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', True),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', False),
('''AS 3S 4S 8S 2S''', True),
)
A__: List[str] = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', False),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', True),
)
A__: int = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
A__: int = (
('''JH AH TH KH QH''', 0),
('''JH 9H TH KH QH''', 0),
('''JC KH JS JD JH''', 7),
('''KH KC 3S 3H 3D''', 6),
('''8C 9C 5C 3C TC''', 0),
('''JS QS 9H TS KH''', 0),
('''7C 7S KH 2H 7H''', 3),
('''3C KH 5D 5S KH''', 2),
('''QH 8H KD JH 8S''', 1),
('''2D 6D 9D TH 7D''', 0),
)
A__: int = (
('''JH AH TH KH QH''', 23),
('''JH 9H TH KH QH''', 22),
('''JC KH JS JD JH''', 21),
('''KH KC 3S 3H 3D''', 20),
('''8C 9C 5C 3C TC''', 19),
('''JS QS 9H TS KH''', 18),
('''7C 7S KH 2H 7H''', 17),
('''3C KH 5D 5S KH''', 16),
('''QH 8H KD JH 8S''', 15),
('''2D 6D 9D TH 7D''', 14),
)
def SCREAMING_SNAKE_CASE_ ( ) -> str:
_a : Union[str, Any] =randrange(len(_UpperCAmelCase ) ), randrange(len(_UpperCAmelCase ) )
_a : Any =['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
_a : Optional[int] =SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 100 ) -> Dict:
return (generate_random_hand() for _ in range(_UpperCAmelCase ))
@pytest.mark.parametrize("""hand, expected""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Tuple ) -> Union[str, Any]:
assert PokerHand(_UpperCAmelCase )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Dict ,_UpperCAmelCase : Dict ) -> Tuple:
assert PokerHand(_UpperCAmelCase )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[Any] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : str ) -> Dict:
_a : Union[str, Any] =PokerHand(_UpperCAmelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
assert PokerHand(_UpperCAmelCase )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : List[str] ,_UpperCAmelCase : Optional[Any] ) -> List[Any]:
assert PokerHand(_UpperCAmelCase )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" ,_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Any ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : List[str] ) -> List[str]:
assert PokerHand(_UpperCAmelCase ).compare_with(PokerHand(_UpperCAmelCase ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" ,generate_random_hands() )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[int] ,_UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : int ) -> int:
assert PokerHand(_UpperCAmelCase ).compare_with(PokerHand(_UpperCAmelCase ) ) == expected
def SCREAMING_SNAKE_CASE_ ( ) -> Union[str, Any]:
_a : List[str] =[PokerHand(_UpperCAmelCase ) for hand in SORTED_HANDS]
_a : List[Any] =poker_hands.copy()
shuffle(_UpperCAmelCase )
_a : Tuple =chain(sorted(_UpperCAmelCase ) )
for index, hand in enumerate(_UpperCAmelCase ):
assert hand == poker_hands[index]
def SCREAMING_SNAKE_CASE_ ( ) -> List[Any]:
_a : Tuple =[PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=_UpperCAmelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def SCREAMING_SNAKE_CASE_ ( ) -> List[str]:
_a : List[str] =PokerHand("""2C 4S AS 3D 5C""" )
_a : Tuple =True
_a : List[str] =[5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def SCREAMING_SNAKE_CASE_ ( ) -> str:
_a : List[str] =0
_a : str =os.path.abspath(os.path.dirname(_UpperCAmelCase ) )
_a : Union[str, Any] =os.path.join(_UpperCAmelCase ,"""poker_hands.txt""" )
with open(_UpperCAmelCase ) as file_hand:
for line in file_hand:
_a : Optional[Any] =line[:14].strip()
_a : Optional[Any] =line[15:].strip()
_a : str =PokerHand(_UpperCAmelCase ), PokerHand(_UpperCAmelCase )
_a : int =player.compare_with(_UpperCAmelCase )
if output == "Win":
answer += 1
assert answer == 376
| 276 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
_lowercase : int = ['image_processor', 'tokenizer']
_lowercase : Union[str, Any] = 'LayoutLMv3ImageProcessor'
_lowercase : List[str] = ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Any , UpperCAmelCase : Dict=None , UpperCAmelCase : Tuple=None , **UpperCAmelCase : Optional[Any] ) -> str:
__lowerCAmelCase: str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase , )
__lowerCAmelCase: List[Any] = kwargs.pop('feature_extractor' )
__lowerCAmelCase: Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCAmelCase , UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__lowerCAmelCase: str = self.image_processor(images=UpperCAmelCase , return_tensors=UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCAmelCase: List[str] = features['words']
__lowerCAmelCase: List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel values
__lowerCAmelCase: Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__lowerCAmelCase: int = self.get_overflowing_images(UpperCAmelCase , encoded_inputs['overflow_to_sample_mapping'] )
__lowerCAmelCase: str = images
return encoded_inputs
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCAmelCase: str = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
return images_with_overflow
def UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Any , *UpperCAmelCase : Dict , **UpperCAmelCase : Any ) -> List[str]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase , )
return self.image_processor
| 322 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece.model')
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe.model')
a_ = 'pt' if is_torch_available() else 'tf'
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
snake_case_ = CamembertTokenizer
snake_case_ = CamembertTokenizerFast
snake_case_ = True
snake_case_ = True
def __magic_name__ ( self : List[Any] ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : int =CamembertTokenizer(__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='<pad>'
SCREAMING_SNAKE_CASE__ : int =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : int =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>NOTUSED''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(__lowercase ) , 10_04 )
def __magic_name__ ( self : int ) -> Optional[int]:
self.assertEqual(self.get_tokenizer().vocab_size , 10_05 )
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =CamembertTokenizer(__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Tuple =CamembertTokenizerFast.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Optional[int] ='I was born in 92000, and this is falsé.'
SCREAMING_SNAKE_CASE__ : int =tokenizer.encode(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : int =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
SCREAMING_SNAKE_CASE__ : Dict =tokenizer.convert_ids_to_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str =self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='I was born in 92000, and this is falsé.'
SCREAMING_SNAKE_CASE__ : str =tokenizer.tokenize(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.encode(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
@slow
def __magic_name__ ( self : Optional[Any] ) -> Dict:
# fmt: off
SCREAMING_SNAKE_CASE__ : int ={'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
SCREAMING_SNAKE_CASE__ : Tuple =[
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''camembert-base''' , revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' , sequences=__lowercase , ) | 152 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : tuple , SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int]=False , ) -> str:
"""simple docstring"""
output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , use_external_data_format=SCREAMING_SNAKE_CASE , enable_onnx_checker=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
else:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool = False ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__lowerCAmelCase: str = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__lowerCAmelCase: Dict = 'cpu'
__lowerCAmelCase: Optional[int] = Path(SCREAMING_SNAKE_CASE )
# VAE DECODER
__lowerCAmelCase: Optional[Any] = AutoencoderKL.from_pretrained(model_path + '/vae' )
__lowerCAmelCase: Union[str, Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
__lowerCAmelCase: Any = vae_decoder.decode
onnx_export(
SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 322 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> str:
lowercase__ : Tuple = 10
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Optional[Any] = [1, 2, 3, 4]
lowercase__ : int = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(__lowerCAmelCase , self.block_size , 0 ) , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> int:
lowercase__ : Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
lowercase__ : int = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__lowerCAmelCase , self.block_size , 0 ) , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Optional[int] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
lowercase__ : Optional[int] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(__lowerCAmelCase , self.block_size , 0 ) , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> Dict:
lowercase__ : Dict = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
lowercase__ : Any = process_story(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [] )
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : Optional[int] = ''
lowercase__ : int = process_story(__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [] )
self.assertEqual(__lowerCAmelCase , [] )
def _lowerCAmelCase( self ) -> Any:
lowercase__ : str = (
'It was the year of Our Lord one thousand seven hundred and '
'seventy-five\n\nSpiritual revelations were conceded to England '
'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
)
lowercase__ : Optional[Any] = process_story(__lowerCAmelCase )
lowercase__ : List[Any] = [
'It was the year of Our Lord one thousand seven hundred and seventy-five.',
'Spiritual revelations were conceded to England at that favoured period, as at this.',
]
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
lowercase__ : Optional[Any] = ['It was the best of times.']
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : int = torch.tensor([1, 2, 3, 4] )
lowercase__ : Optional[Any] = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(__lowerCAmelCase , 0 ).numpy() , expected.numpy() )
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : str = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
lowercase__ : str = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__lowerCAmelCase , 23 ).numpy() , expected.numpy() )
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ : Union[str, Any] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
lowercase__ : str = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(__lowerCAmelCase , 1 ).numpy() , expected.numpy() )
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : Optional[int] = 101
lowercase__ : List[str] = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
lowercase__ : List[str] = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
lowercase__ : Optional[int] = compute_token_type_ids(__lowerCAmelCase , __lowerCAmelCase )
np.testing.assert_array_equal(__lowerCAmelCase , __lowerCAmelCase )
| 198 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square(SCREAMING_SNAKE_CASE , col + 1 )
__lowerCAmelCase: Tuple = update_area_of_max_square(row + 1 , col + 1 )
__lowerCAmelCase: int = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: List[str] = 1 + min([right, diagonal, down] )
__lowerCAmelCase: List[str] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
return sub_problem_sol
else:
return 0
__lowerCAmelCase: List[str] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__lowerCAmelCase: List[Any] = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: int = 1 + min([right, diagonal, down] )
__lowerCAmelCase: Union[str, Any] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sub_problem_sol
return sub_problem_sol
else:
return 0
__lowerCAmelCase: int = [0]
__lowerCAmelCase: int = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: int = [[0] * (cols + 1) for _ in range(rows + 1 )]
__lowerCAmelCase: Optional[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: Union[str, Any] = dp_array[row][col + 1]
__lowerCAmelCase: str = dp_array[row + 1][col + 1]
__lowerCAmelCase: Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
__lowerCAmelCase: Optional[Any] = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(dp_array[row][col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Dict = 0
return largest_square_area
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: Tuple = [0] * (cols + 1)
__lowerCAmelCase: Optional[int] = [0] * (cols + 1)
__lowerCAmelCase: str = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: int = current_row[col + 1]
__lowerCAmelCase: Union[str, Any] = next_row[col + 1]
__lowerCAmelCase: Any = next_row[col]
if mat[row][col] == 1:
__lowerCAmelCase: str = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(current_row[col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: int = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 322 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A ={
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LayoutLMv3Config''',
'''LayoutLMv3OnnxConfig''',
],
'''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
'''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =['''LayoutLMv3TokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv3ForQuestionAnswering''',
'''LayoutLMv3ForSequenceClassification''',
'''LayoutLMv3ForTokenClassification''',
'''LayoutLMv3Model''',
'''LayoutLMv3PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =[
'''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLayoutLMv3ForQuestionAnswering''',
'''TFLayoutLMv3ForSequenceClassification''',
'''TFLayoutLMv3ForTokenClassification''',
'''TFLayoutLMv3Model''',
'''TFLayoutLMv3PreTrainedModel''',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A =['''LayoutLMv3FeatureExtractor''']
__A =['''LayoutLMv3ImageProcessor''']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
__A =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 19 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_a = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = SavedModel()
__lowerCAmelCase: str = []
with open(os.path.join(SCREAMING_SNAKE_CASE , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__lowerCAmelCase: List[str] = json.load(SCREAMING_SNAKE_CASE )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(SCREAMING_SNAKE_CASE )] )
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__lowerCAmelCase: Optional[int] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowerCAmelCase: List[str] = sorted(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(SCREAMING_SNAKE_CASE )
if strict and len(SCREAMING_SNAKE_CASE ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(SCREAMING_SNAKE_CASE ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*SCREAMING_SNAKE_CASE , sep='\n' )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_a = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 322 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
def UpperCAmelCase_ ( self :List[Any] ) -> List[Any]:
UpperCAmelCase__ = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase__ = ['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
UpperCAmelCase__ = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
UpperCAmelCase__ = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
UpperCAmelCase__ = {'unk_token': '<unk>'}
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCamelCase ) )
UpperCAmelCase__ = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
UpperCAmelCase__ = os.path.join(self.tmpdirname , lowerCamelCase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] , **lowerCamelCase :Tuple ) -> Union[str, Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token="!" , **lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] , **lowerCamelCase :List[Any] ) -> Union[str, Any]:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token="!" , **lowerCamelCase )
def UpperCAmelCase_ ( self :Union[str, Any] , **lowerCamelCase :Any ) -> str:
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase )
def UpperCAmelCase_ ( self :List[Any] ) -> List[Any]:
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self :Optional[int] ) -> List[Any]:
UpperCAmelCase__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase__ = [Image.fromarray(np.moveaxis(lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase_ ( self :List[Any] ) -> Dict:
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_rust_tokenizer()
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase )
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , lowerCamelCase )
def UpperCAmelCase_ ( self :List[str] ) -> List[str]:
UpperCAmelCase__ = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
UpperCAmelCase__ = self.get_image_processor(do_normalize=lowerCamelCase )
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowerCamelCase )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase )
def UpperCAmelCase_ ( self :Tuple ) -> Optional[Any]:
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = image_processor(lowerCamelCase , return_tensors="np" )
UpperCAmelCase__ = processor(images=lowerCamelCase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase_ ( self :str ) -> Union[str, Any]:
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
UpperCAmelCase__ = 'lower newer'
UpperCAmelCase__ = processor(text=lowerCamelCase , return_tensors="np" )
UpperCAmelCase__ = tokenizer(lowerCamelCase , return_tensors="np" )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() , encoded_processor[key][0].tolist() )
def UpperCAmelCase_ ( self :Optional[int] ) -> List[Any]:
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
UpperCAmelCase__ = 'lower newer'
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(text=lowerCamelCase , images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def UpperCAmelCase_ ( self :Any ) -> Tuple:
UpperCAmelCase__ = 'google/owlvit-base-patch32'
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(lowerCamelCase )
UpperCAmelCase__ = ['cat', 'nasa badge']
UpperCAmelCase__ = processor(text=lowerCamelCase )
UpperCAmelCase__ = 16
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def UpperCAmelCase_ ( self :Optional[int] ) -> Any:
UpperCAmelCase__ = 'google/owlvit-base-patch32'
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(lowerCamelCase )
UpperCAmelCase__ = [['cat', 'nasa badge'], ['person']]
UpperCAmelCase__ = processor(text=lowerCamelCase )
UpperCAmelCase__ = 16
UpperCAmelCase__ = len(lowerCamelCase )
UpperCAmelCase__ = max([len(lowerCamelCase ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def UpperCAmelCase_ ( self :List[Any] ) -> int:
UpperCAmelCase__ = 'google/owlvit-base-patch32'
UpperCAmelCase__ = OwlViTProcessor.from_pretrained(lowerCamelCase )
UpperCAmelCase__ = ['cat', 'nasa badge']
UpperCAmelCase__ = processor(text=lowerCamelCase )
UpperCAmelCase__ = 16
UpperCAmelCase__ = inputs['input_ids']
UpperCAmelCase__ = [
[4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask"] )
self.assertEqual(inputs["input_ids"].shape , (2, seq_length) )
self.assertListEqual(list(input_ids[0] ) , predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) , predicted_ids[1] )
def UpperCAmelCase_ ( self :Optional[Any] ) -> str:
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = self.prepare_image_inputs()
UpperCAmelCase__ = processor(images=lowerCamelCase , query_images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["query_pixel_values", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def UpperCAmelCase_ ( self :int ) -> Dict:
UpperCAmelCase__ = self.get_image_processor()
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = OwlViTProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
UpperCAmelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase__ = processor.batch_decode(lowerCamelCase )
UpperCAmelCase__ = tokenizer.batch_decode(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
| 169 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase: Union[str, Any] = qiskit.QuantumRegister(4 , 'qr' )
__lowerCAmelCase: List[Any] = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCAmelCase: Any = [input_a, input_a, carry_in]
__lowerCAmelCase: List[str] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(SCREAMING_SNAKE_CASE ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(SCREAMING_SNAKE_CASE ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(SCREAMING_SNAKE_CASE ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , SCREAMING_SNAKE_CASE ) # measure the last two qbits
__lowerCAmelCase: List[str] = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase: List[Any] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 322 | 0 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
UpperCAmelCase_ ='autoformer'
UpperCAmelCase_ ={
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self , _A = None , _A = None , _A = "student_t" , _A = "nll" , _A = 1 , _A = [1, 2, 3, 4, 5, 6, 7] , _A = True , _A = 0 , _A = 0 , _A = 0 , _A = 0 , _A = None , _A = None , _A = 64 , _A = 2 , _A = 2 , _A = 2 , _A = 2 , _A = 32 , _A = 32 , _A = "gelu" , _A = 0.1 , _A = 0.1 , _A = 0.1 , _A = 0.1 , _A = 0.1 , _A = 100 , _A = 0.02 , _A = True , _A=True , _A = 10 , _A = 25 , _A = 3 , **_A , ) -> str:
# time series specific configuration
SCREAMING_SNAKE_CASE_ = prediction_length
SCREAMING_SNAKE_CASE_ = context_length if context_length is not None else prediction_length
SCREAMING_SNAKE_CASE_ = distribution_output
SCREAMING_SNAKE_CASE_ = loss
SCREAMING_SNAKE_CASE_ = input_size
SCREAMING_SNAKE_CASE_ = num_time_features
SCREAMING_SNAKE_CASE_ = lags_sequence
SCREAMING_SNAKE_CASE_ = scaling
SCREAMING_SNAKE_CASE_ = num_dynamic_real_features
SCREAMING_SNAKE_CASE_ = num_static_real_features
SCREAMING_SNAKE_CASE_ = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(_A ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
SCREAMING_SNAKE_CASE_ = cardinality
else:
SCREAMING_SNAKE_CASE_ = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(_A ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
SCREAMING_SNAKE_CASE_ = embedding_dimension
else:
SCREAMING_SNAKE_CASE_ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
SCREAMING_SNAKE_CASE_ = num_parallel_samples
# Transformer architecture configuration
SCREAMING_SNAKE_CASE_ = input_size * len(self.lags_sequence ) + self._number_of_features
SCREAMING_SNAKE_CASE_ = d_model
SCREAMING_SNAKE_CASE_ = encoder_attention_heads
SCREAMING_SNAKE_CASE_ = decoder_attention_heads
SCREAMING_SNAKE_CASE_ = encoder_ffn_dim
SCREAMING_SNAKE_CASE_ = decoder_ffn_dim
SCREAMING_SNAKE_CASE_ = encoder_layers
SCREAMING_SNAKE_CASE_ = decoder_layers
SCREAMING_SNAKE_CASE_ = dropout
SCREAMING_SNAKE_CASE_ = attention_dropout
SCREAMING_SNAKE_CASE_ = activation_dropout
SCREAMING_SNAKE_CASE_ = encoder_layerdrop
SCREAMING_SNAKE_CASE_ = decoder_layerdrop
SCREAMING_SNAKE_CASE_ = activation_function
SCREAMING_SNAKE_CASE_ = init_std
SCREAMING_SNAKE_CASE_ = use_cache
# Autoformer
SCREAMING_SNAKE_CASE_ = label_length
SCREAMING_SNAKE_CASE_ = moving_average
SCREAMING_SNAKE_CASE_ = autocorrelation_factor
super().__init__(is_encoder_decoder=_A , **_A )
@property
def _UpperCamelCase ( self ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 299 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
def __init__( self : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : int=3 , UpperCAmelCase : int=4 , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=9_9 , UpperCAmelCase : Tuple=3_6 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]=3_7 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[str]=5_1_2 , UpperCAmelCase : int=1_6 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[Any]=6 , UpperCAmelCase : int=6 , UpperCAmelCase : str=3 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[str]=1_0_0_0 , ) -> int:
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: List[str] = batch_size
__lowerCAmelCase: Optional[Any] = num_channels
__lowerCAmelCase: Tuple = image_size
__lowerCAmelCase: str = patch_size
__lowerCAmelCase: List[str] = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Union[str, Any] = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: Dict = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: str = hidden_dropout_prob
__lowerCAmelCase: str = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: str = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: List[str] = coordinate_size
__lowerCAmelCase: Tuple = shape_size
__lowerCAmelCase: List[Any] = num_labels
__lowerCAmelCase: Any = num_choices
__lowerCAmelCase: List[str] = scope
__lowerCAmelCase: Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase: Optional[Any] = text_seq_length
__lowerCAmelCase: List[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase: int = self.text_seq_length + self.image_seq_length
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase: str = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: Optional[Any] = bbox[i, j, 3]
__lowerCAmelCase: Tuple = bbox[i, j, 1]
__lowerCAmelCase: Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Any = bbox[i, j, 2]
__lowerCAmelCase: int = bbox[i, j, 0]
__lowerCAmelCase: int = tmp_coordinate
__lowerCAmelCase: List[Any] = tf.constant(UpperCAmelCase )
__lowerCAmelCase: Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase: Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase: List[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase: int = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase: str = None
__lowerCAmelCase: Dict = None
if self.use_labels:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase: Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> int:
__lowerCAmelCase: Tuple = TFLayoutLMvaModel(config=UpperCAmelCase )
# text + image
__lowerCAmelCase: Dict = model(UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , training=UpperCAmelCase , )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase: str = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase: List[str] = model({'pixel_values': pixel_values} , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] ) -> int:
__lowerCAmelCase: List[str] = self.num_labels
__lowerCAmelCase: Tuple = TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int ) -> Any:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: List[str] = TFLayoutLMvaForTokenClassification(config=UpperCAmelCase )
__lowerCAmelCase: Any = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> Any:
__lowerCAmelCase: str = 2
__lowerCAmelCase: Dict = TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)): List[str] = config_and_inputs
__lowerCAmelCase: List[str] = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : Tuple = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Dict = False
_lowercase : Tuple = False
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> List[str]:
return True
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=False ) -> dict:
__lowerCAmelCase: Optional[Any] = copy.deepcopy(UpperCAmelCase )
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: int = {
k: tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase: Tuple = TFLayoutLMvaModelTester(self )
__lowerCAmelCase: str = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase: List[Any] = model_class(UpperCAmelCase )
if getattr(UpperCAmelCase , 'hf_compute_loss' , UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase: Optional[int] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: List[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=UpperCAmelCase )[0]
]
__lowerCAmelCase: Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase: Optional[Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Tuple = prepared_for_class.pop('input_ids' )
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase: Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCAmelCase: str = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase: Tuple = -1_0_0
__lowerCAmelCase: Union[str, Any] = tf.convert_to_tensor(UpperCAmelCase )
__lowerCAmelCase: Dict = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase: str = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase: Any = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase: Tuple = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase: Dict = inspect.signature(model.call ).parameters
__lowerCAmelCase: Dict = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase: str = {0: 'input_ids'}
for label_key in label_keys:
__lowerCAmelCase: Optional[Any] = signature_names.index(UpperCAmelCase )
__lowerCAmelCase: Tuple = label_key
__lowerCAmelCase: Tuple = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase: List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase: Optional[Any] = prepared_for_class[value]
__lowerCAmelCase: Union[str, Any] = tuple(UpperCAmelCase )
# Send to model
__lowerCAmelCase: Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCAmelCase ( self : Dict ) -> Tuple:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> int:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: Tuple = type
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : int ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Optional[int] = TFLayoutLMvaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : int ) -> Dict:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Any ) -> List[str]:
__lowerCAmelCase: Any = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCAmelCase: Tuple = self.default_image_processor
__lowerCAmelCase: str = prepare_img()
__lowerCAmelCase: Optional[int] = image_processor(images=UpperCAmelCase , return_tensors='tf' ).pixel_values
__lowerCAmelCase: Dict = tf.constant([[1, 2]] )
__lowerCAmelCase: str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase: List[str] = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
__lowerCAmelCase: Tuple = (1, 1_9_9, 7_6_8)
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase )
__lowerCAmelCase: str = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
A =logging.get_logger(__name__)
A ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A ={
'vocab_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
),
},
}
A ={
'yjernite/retribert-base-uncased': 5_12,
}
A ={
'yjernite/retribert-base-uncased': {'do_lower_case': True},
}
class _a ( snake_case__ ):
__a : Union[str, Any] = VOCAB_FILES_NAMES
__a : List[str] = PRETRAINED_VOCAB_FILES_MAP
__a : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : int = PRETRAINED_INIT_CONFIGURATION
__a : Optional[Any] = RetriBertTokenizer
__a : Optional[Any] = ['input_ids', 'attention_mask']
def __init__( self : str , lowercase : int=None , lowercase : Any=None , lowercase : Dict=True , lowercase : Optional[int]="[UNK]" , lowercase : Dict="[SEP]" , lowercase : List[str]="[PAD]" , lowercase : str="[CLS]" , lowercase : Tuple="[MASK]" , lowercase : Dict=True , lowercase : str=None , **lowercase : List[str] , ):
'''simple docstring'''
super().__init__(
lowercase , tokenizer_file=lowercase , do_lower_case=lowercase , unk_token=lowercase , sep_token=lowercase , pad_token=lowercase , cls_token=lowercase , mask_token=lowercase , tokenize_chinese_chars=lowercase , strip_accents=lowercase , **lowercase , )
UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowercase ) != tokenize_chinese_chars
):
UpperCAmelCase = getattr(lowercase , normalizer_state.pop('''type''' ) )
UpperCAmelCase = do_lower_case
UpperCAmelCase = strip_accents
UpperCAmelCase = tokenize_chinese_chars
UpperCAmelCase = normalizer_class(**lowercase )
UpperCAmelCase = do_lower_case
def A ( self : Dict , lowercase : Optional[int] , lowercase : Optional[int]=None ):
'''simple docstring'''
UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A ( self : Optional[Any] , lowercase : List[int] , lowercase : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase = [self.sep_token_id]
UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A ( self : List[str] , lowercase : str , lowercase : Optional[str] = None ):
'''simple docstring'''
UpperCAmelCase = self._tokenizer.model.save(lowercase , name=lowercase )
return tuple(lowercase )
| 34 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : str=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=9_9 , UpperCAmelCase : Optional[int]=3_2 , UpperCAmelCase : Dict=5 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : Dict=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : int=0.02 , UpperCAmelCase : List[Any]=4 , ) -> Optional[Any]:
__lowerCAmelCase: str = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Optional[int] = seq_length
__lowerCAmelCase: Dict = is_training
__lowerCAmelCase: Optional[Any] = use_attention_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Optional[int] = use_labels
__lowerCAmelCase: Optional[Any] = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: List[str] = num_attention_heads
__lowerCAmelCase: int = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Optional[int] = max_position_embeddings
__lowerCAmelCase: Union[str, Any] = type_vocab_size
__lowerCAmelCase: int = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Optional[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Optional[int] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : Dict ) -> Any:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: Optional[Any] = config_and_inputs
__lowerCAmelCase: Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( snake_case__ , unittest.TestCase ):
_lowercase : Dict = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase: List[Any] = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
__lowerCAmelCase: Optional[Any] = model_class_name.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
@require_flax
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: List[Any] = FlaxAlbertModel.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
__lowerCAmelCase: str = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class lowerCamelCase_ :
"""simple docstring"""
def __init__( self : List[str] , _a : Dict ) -> int:
__lowerCamelCase : Any = data
__lowerCamelCase : Any = None
__lowerCamelCase : Dict = None
def a_ ( ) -> TreeNode:
print('\n********Press N to stop entering at any point of time********\n' )
__lowerCamelCase : Dict = input('Enter the value of the root node: ' ).strip().lower()
__lowerCamelCase : queue.Queue = queue.Queue()
__lowerCamelCase : int = TreeNode(int(_lowerCAmelCase ) )
q.put(_lowerCAmelCase )
while not q.empty():
__lowerCamelCase : str = q.get()
__lowerCamelCase : str = F'Enter the left node of {node_found.data}: '
__lowerCamelCase : int = input(_lowerCAmelCase ).strip().lower() or 'n'
if check == "n":
return tree_node
__lowerCamelCase : Optional[Any] = TreeNode(int(_lowerCAmelCase ) )
__lowerCamelCase : str = left_node
q.put(_lowerCAmelCase )
__lowerCamelCase : List[str] = F'Enter the right node of {node_found.data}: '
__lowerCamelCase : Optional[Any] = input(_lowerCAmelCase ).strip().lower() or 'n'
if check == "n":
return tree_node
__lowerCamelCase : str = TreeNode(int(_lowerCAmelCase ) )
__lowerCamelCase : Union[str, Any] = right_node
q.put(_lowerCAmelCase )
raise
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
print(node.data ,end=',' )
pre_order(node.left )
pre_order(node.right )
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
in_order(node.left )
print(node.data ,end=',' )
in_order(node.right )
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data ,end=',' )
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
__lowerCamelCase : queue.Queue = queue.Queue()
q.put(_lowerCAmelCase )
while not q.empty():
__lowerCamelCase : Optional[int] = q.get()
print(node_dequeued.data ,end=',' )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
__lowerCamelCase : queue.Queue = queue.Queue()
q.put(_lowerCAmelCase )
while not q.empty():
__lowerCamelCase : Optional[Any] = []
while not q.empty():
__lowerCamelCase : Dict = q.get()
print(node_dequeued.data ,end=',' )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(_lowerCAmelCase )
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
__lowerCamelCase : list[TreeNode] = []
__lowerCamelCase : Any = node
while n or stack:
while n: # start from root node, find its left child
print(n.data ,end=',' )
stack.append(_lowerCAmelCase )
__lowerCamelCase : Dict = n.left
# end of while means current node doesn't have left child
__lowerCamelCase : List[Any] = stack.pop()
# start to traverse its right child
__lowerCamelCase : List[Any] = n.right
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
__lowerCamelCase : list[TreeNode] = []
__lowerCamelCase : List[str] = node
while n or stack:
while n:
stack.append(_lowerCAmelCase )
__lowerCamelCase : int = n.left
__lowerCamelCase : Union[str, Any] = stack.pop()
print(n.data ,end=',' )
__lowerCamelCase : List[str] = n.right
def a_ ( _lowerCAmelCase ) -> None:
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ) or not node:
return
__lowerCamelCase : Optional[Any] = [], []
__lowerCamelCase : Optional[int] = node
stacka.append(_lowerCAmelCase )
while stacka: # to find the reversed order of post order, store it in stack2
__lowerCamelCase : List[str] = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(_lowerCAmelCase )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data ,end=',' )
def a_ ( _lowerCAmelCase = "" ,_lowerCAmelCase=50 ,_lowerCAmelCase="*" ) -> str:
if not s:
return "\n" + width * char
__lowerCamelCase : List[str] = divmod(width - len(_lowerCAmelCase ) - 2 ,2 )
return F'{left * char} {s} {(left + extra) * char}'
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
_UpperCamelCase = build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 50 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 208 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[int] , _A : List[str] , _A : Dict=13 , _A : Dict=3 , _A : List[Any]=224 , _A : List[str]=30 , _A : str=400 , _A : Dict=True , _A : Dict=None , _A : List[str]=True , _A : Any=[0.5, 0.5, 0.5] , _A : Tuple=[0.5, 0.5, 0.5] , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = size if size is not None else {'height': 18, 'width': 18}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : str = batch_size
__SCREAMING_SNAKE_CASE : int = num_channels
__SCREAMING_SNAKE_CASE : Tuple = image_size
__SCREAMING_SNAKE_CASE : int = min_resolution
__SCREAMING_SNAKE_CASE : Optional[int] = max_resolution
__SCREAMING_SNAKE_CASE : int = do_resize
__SCREAMING_SNAKE_CASE : str = size
__SCREAMING_SNAKE_CASE : List[Any] = do_normalize
__SCREAMING_SNAKE_CASE : int = image_mean
__SCREAMING_SNAKE_CASE : Optional[Any] = image_std
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class __UpperCamelCase ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ = ViTImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = EfficientFormerImageProcessorTester(self )
@property
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
return self.image_proc_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''image_mean''' ) )
self.assertTrue(hasattr(_A , '''image_std''' ) )
self.assertTrue(hasattr(_A , '''do_normalize''' ) )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[Any] = prepare_image_inputs(self.image_proc_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Tuple = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : str = prepare_image_inputs(self.image_proc_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
# Test batched
__SCREAMING_SNAKE_CASE : str = image_processor(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['''height'''],
self.image_proc_tester.size['''width'''],
) , )
| 303 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_a = [None] * 1_0_0_0_0_0_0_0
_a = True
_a = False
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCAmelCase: int = chain(next_number(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = number_chain
while number < 10_00_00_00:
__lowerCAmelCase: Dict = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE : int = 10_00_00_00 ) -> int:
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| 322 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class A_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Tuple:
debug_launcher(test_script.main )
def UpperCAmelCase__ ( self :Any ) -> Optional[int]:
debug_launcher(test_ops.main )
| 78 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCAmelCase : str = ''
__lowerCAmelCase : Tuple = ''
__lowerCAmelCase : Tuple = ''
__lowerCAmelCase : Optional[int] = 1 # (0 is vertical, 1 is horizontal)
def a__ ( ):
'''simple docstring'''
__magic_name__ = get_dataset(A_, A_ )
print("""Processing...""" )
__magic_name__ = update_image_and_anno(A_, A_, A_ )
for index, image in enumerate(A_ ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
__magic_name__ = random_chars(32 )
__magic_name__ = paths[index].split(os.sep )[-1].rsplit(""".""", 1 )[0]
__magic_name__ = f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(f'''/{file_root}.jpg''', A_, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Success {index+1}/{len(A_ )} with {file_name}''' )
__magic_name__ = []
for anno in new_annos[index]:
__magic_name__ = f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(A_ )
with open(f'''/{file_root}.txt''', """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def a__ ( A_, A_ ):
'''simple docstring'''
__magic_name__ = []
__magic_name__ = []
for label_file in glob.glob(os.path.join(A_, """*.txt""" ) ):
__magic_name__ = label_file.split(os.sep )[-1].rsplit(""".""", 1 )[0]
with open(A_ ) as in_file:
__magic_name__ = in_file.readlines()
__magic_name__ = os.path.join(A_, f'''{label_name}.jpg''' )
__magic_name__ = []
for obj_list in obj_lists:
__magic_name__ = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(A_ )
labels.append(A_ )
return img_paths, labels
def a__ ( A_, A_, A_ = 1 ):
'''simple docstring'''
__magic_name__ = []
__magic_name__ = []
__magic_name__ = []
for idx in range(len(A_ ) ):
__magic_name__ = []
__magic_name__ = img_list[idx]
path_list.append(A_ )
__magic_name__ = anno_list[idx]
__magic_name__ = cva.imread(A_ )
if flip_type == 1:
__magic_name__ = cva.flip(A_, A_ )
for bbox in img_annos:
__magic_name__ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
__magic_name__ = cva.flip(A_, A_ )
for bbox in img_annos:
__magic_name__ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(A_ )
new_imgs_list.append(A_ )
return new_imgs_list, new_annos_lists, path_list
def a__ ( A_ = 32 ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
__magic_name__ = ascii_lowercase + digits
return "".join(random.choice(A_ ) for _ in range(A_ ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 88 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str=1_3 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : str=True , UpperCAmelCase : Any=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : int=False , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Any=9_9 , UpperCAmelCase : str=0 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : int=5 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : int=5_1_2 , UpperCAmelCase : str=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict="last" , UpperCAmelCase : int=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Union[str, Any]=0 , ) -> Dict:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Tuple = seq_length
__lowerCAmelCase: Tuple = is_training
__lowerCAmelCase: Optional[Any] = use_input_lengths
__lowerCAmelCase: List[str] = use_token_type_ids
__lowerCAmelCase: Dict = use_labels
__lowerCAmelCase: int = gelu_activation
__lowerCAmelCase: Optional[int] = sinusoidal_embeddings
__lowerCAmelCase: Tuple = causal
__lowerCAmelCase: Optional[Any] = asm
__lowerCAmelCase: int = n_langs
__lowerCAmelCase: Tuple = vocab_size
__lowerCAmelCase: List[Any] = n_special
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: int = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Dict = max_position_embeddings
__lowerCAmelCase: List[str] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: List[str] = num_labels
__lowerCAmelCase: List[str] = num_choices
__lowerCAmelCase: Optional[int] = summary_type
__lowerCAmelCase: Any = use_proj
__lowerCAmelCase: Optional[Any] = scope
__lowerCAmelCase: Dict = bos_token_id
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Any = None
if self.use_input_lengths:
__lowerCAmelCase: Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowerCAmelCase: str = None
if self.use_token_type_ids:
__lowerCAmelCase: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: Optional[int] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
__lowerCAmelCase: str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase: Dict = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: List[str] = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , ) -> int:
__lowerCAmelCase: str = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Dict , ) -> List[str]:
__lowerCAmelCase: Dict = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: str = model(UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
__lowerCAmelCase: Any = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((__lowerCAmelCase) , ): List[str] = result_with_labels.to_tuple()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((__lowerCAmelCase) , ): List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , ) -> List[Any]:
__lowerCAmelCase: Optional[Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = model(UpperCAmelCase )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
__lowerCAmelCase: List[Any] = self.num_choices
__lowerCAmelCase: Optional[Any] = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Tuple ) -> int:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCAmelCase: Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple=False ) -> Dict:
__lowerCAmelCase: Optional[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__lowerCAmelCase: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: int = XLMModelTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=3_7 )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__lowerCAmelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] ) -> int:
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : Dict=1 ) -> Dict:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: int = min_length + idx + 1
__lowerCAmelCase: Union[str, Any] = min_length + idx + 1
__lowerCAmelCase: Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: Any = min_length + idx + 1
__lowerCAmelCase: str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def UpperCAmelCase ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: List[Any] = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=UpperCAmelCase ) # the president
__lowerCAmelCase: Union[str, Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__lowerCAmelCase: str = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 322 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : str ) -> list[int]:
_a : Union[str, Any] =int(_UpperCAmelCase )
# Initialize Result
_a : int =[]
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCase ):
# Find denominations
while int(_UpperCAmelCase ) >= int(_UpperCAmelCase ):
total_value -= int(_UpperCAmelCase )
answer.append(_UpperCAmelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
A__: List[Any] = []
A__: Dict = '''0'''
if (
input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
== "y"
):
A__: Any = int(input('''Enter the number of denominations you want to add: ''').strip())
for i in range(0, n):
denominations.append(int(input(F"Denomination {i}: ").strip()))
A__: List[Any] = input('''Enter the change you want to make in Indian Currency: ''').strip()
else:
# All denominations of Indian Currency if user does not enter
A__: Dict = [1, 2, 5, 10, 20, 50, 100, 500, 2000]
A__: Dict = input('''Enter the change you want to make: ''').strip()
if int(value) == 0 or int(value) < 0:
print('''The total value cannot be zero or negative.''')
else:
print(F"Following is minimal change for {value}: ")
A__: Tuple = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=''' ''')
| 276 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) <= 1:
return arr, 0
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE ) // 2
__lowerCAmelCase: str = arr[0:mid]
__lowerCAmelCase: int = arr[mid:]
__lowerCAmelCase , __lowerCAmelCase: List[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: int = _count_cross_inversions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: List[str] = 0
while i < len(SCREAMING_SNAKE_CASE ) and j < len(SCREAMING_SNAKE_CASE ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(SCREAMING_SNAKE_CASE ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: str = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# an empty list should also have zero inversions
__lowerCAmelCase: int = []
__lowerCAmelCase: Any = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def _a( ):
'''simple docstring'''
print(sum_of_series(1, 1, 1_0 ) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 152 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=7 , __lowerCAmelCase=3 , __lowerCAmelCase=30 , __lowerCAmelCase=400 , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=True , __lowerCAmelCase=[0.5, 0.5, 0.5] , __lowerCAmelCase=[0.5, 0.5, 0.5] , __lowerCAmelCase=True , __lowerCAmelCase=1 / 255 , __lowerCAmelCase=True , ) -> Optional[int]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase__ : List[str] = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
lowercase__ : Optional[int] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : int = num_channels
lowercase__ : Optional[int] = min_resolution
lowercase__ : Union[str, Any] = max_resolution
lowercase__ : Union[str, Any] = do_resize
lowercase__ : str = size
lowercase__ : int = do_normalize
lowercase__ : Any = image_mean
lowercase__ : str = image_std
lowercase__ : Tuple = do_rescale
lowercase__ : Optional[int] = rescale_factor
lowercase__ : int = do_pad
def _lowerCAmelCase( self ) -> Any:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=False ) -> Dict:
if not batched:
lowercase__ : List[str] = image_inputs[0]
if isinstance(__lowerCAmelCase , Image.Image ):
lowercase__ : Union[str, Any] = image.size
else:
lowercase__ : int = image.shape[1], image.shape[2]
if w < h:
lowercase__ : Dict = int(self.size['''shortest_edge'''] * h / w )
lowercase__ : str = self.size['shortest_edge']
elif w > h:
lowercase__ : str = self.size['shortest_edge']
lowercase__ : Tuple = int(self.size['''shortest_edge'''] * w / h )
else:
lowercase__ : Any = self.size['shortest_edge']
lowercase__ : Optional[int] = self.size['shortest_edge']
else:
lowercase__ : int = []
for image in image_inputs:
lowercase__ : List[str] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase__ : str = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[0] )[0]
lowercase__ : Union[str, Any] = max(__lowerCAmelCase , key=lambda __lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase ( snake_case__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ConditionalDetrImageProcessor if is_vision_available() else None
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : List[Any] = ConditionalDetrImageProcessingTester(self )
@property
def _lowerCAmelCase( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , '''image_mean''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''image_std''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18, '''longest_edge''': 1333} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
lowercase__ : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__lowerCAmelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42, '''longest_edge''': 84} )
self.assertEqual(image_processor.do_pad , __lowerCAmelCase )
def _lowerCAmelCase( self ) -> List[str]:
pass
def _lowerCAmelCase( self ) -> int:
# Initialize image_processing
lowercase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , Image.Image )
# Test not batched input
lowercase__ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__ : Optional[int] = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ : Dict = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
lowercase__ : Tuple = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowerCAmelCase( self ) -> List[Any]:
# Initialize image_processing
lowercase__ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , numpify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , np.ndarray )
# Test not batched input
lowercase__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__ : List[str] = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ : Union[str, Any] = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
lowercase__ : str = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _lowerCAmelCase( self ) -> List[str]:
# Initialize image_processing
lowercase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowerCAmelCase , torchify=__lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(__lowerCAmelCase , torch.Tensor )
# Test not batched input
lowercase__ : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
lowercase__ : Dict = self.image_processor_tester.get_expected_values(__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ : Dict = image_processing(__lowerCAmelCase , return_tensors='''pt''' ).pixel_values
lowercase__ : List[Any] = self.image_processor_tester.get_expected_values(__lowerCAmelCase , batched=__lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _lowerCAmelCase( self ) -> Tuple:
# prepare image and target
lowercase__ : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
lowercase__ : Dict = json.loads(f.read() )
lowercase__ : int = {'image_id': 39769, 'annotations': target}
# encode them
lowercase__ : Dict = ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50''' )
lowercase__ : Optional[Any] = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
lowercase__ : Union[str, Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
lowercase__ : Union[str, Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
lowercase__ : List[str] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
lowercase__ : Dict = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
lowercase__ : Tuple = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
lowercase__ : List[Any] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
lowercase__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
lowercase__ : Union[str, Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify orig_size
lowercase__ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
lowercase__ : Any = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
@slow
def _lowerCAmelCase( self ) -> Tuple:
# prepare image, target and masks_path
lowercase__ : Any = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
lowercase__ : Optional[int] = json.loads(f.read() )
lowercase__ : Optional[Any] = {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}
lowercase__ : Tuple = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
lowercase__ : str = ConditionalDetrImageProcessor(format='''coco_panoptic''' )
lowercase__ : Optional[Any] = image_processing(images=__lowerCAmelCase , annotations=__lowerCAmelCase , masks_path=__lowerCAmelCase , return_tensors='''pt''' )
# verify pixel values
lowercase__ : List[Any] = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding['''pixel_values'''].shape , __lowerCAmelCase )
lowercase__ : List[Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , __lowerCAmelCase , atol=1E-4 ) )
# verify area
lowercase__ : List[str] = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , __lowerCAmelCase ) )
# verify boxes
lowercase__ : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , __lowerCAmelCase )
lowercase__ : Tuple = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , __lowerCAmelCase , atol=1E-3 ) )
# verify image_id
lowercase__ : Optional[int] = torch.tensor([39769] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , __lowerCAmelCase ) )
# verify is_crowd
lowercase__ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , __lowerCAmelCase ) )
# verify class_labels
lowercase__ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , __lowerCAmelCase ) )
# verify masks
lowercase__ : Optional[int] = 822873
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , __lowerCAmelCase )
# verify orig_size
lowercase__ : Union[str, Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , __lowerCAmelCase ) )
# verify size
lowercase__ : Tuple = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , __lowerCAmelCase ) )
| 198 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=13 , lowercase=30 , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.0_2 , ) -> int:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase_ = (image_size // patch_size) ** 2
lowerCamelCase_ = num_patches + 1
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , )
return config, pixel_values
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Optional[Any]:
lowerCamelCase_ = FlaxViTModel(config=lowercase )
lowerCamelCase_ = model(lowercase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase_ = (self.image_size, self.image_size)
lowerCamelCase_ = (self.patch_size, self.patch_size)
lowerCamelCase_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Any:
lowerCamelCase_ = self.type_sequence_label_size
lowerCamelCase_ = FlaxViTForImageClassification(config=lowercase )
lowerCamelCase_ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase_ = 1
lowerCamelCase_ = FlaxViTForImageClassification(lowercase )
lowerCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ = model(lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = self.prepare_config_and_inputs()
(
lowerCamelCase_
) = config_and_inputs
lowerCamelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class _SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
lowerCAmelCase__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE_( self ) -> None:
lowerCamelCase_ = FlaxViTModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
lowerCamelCase_ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase_ = self._prepare_for_class(lowercase , lowercase )
lowerCamelCase_ = model_class(lowercase )
@jax.jit
def model_jitted(lowercase , **lowercase ):
return model(pixel_values=lowercase , **lowercase )
with self.subTest("JIT Enabled" ):
lowerCamelCase_ = model_jitted(**lowercase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCamelCase_ = model_jitted(**lowercase ).to_tuple()
self.assertEqual(len(lowercase ) , len(lowercase ) )
for jitted_output, output in zip(lowercase , lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
for model_class_name in self.all_model_classes:
lowerCamelCase_ = model_class_name.from_pretrained("google/vit-base-patch16-224" )
lowerCamelCase_ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(lowercase )
| 19 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.environ['GITHUB_TOKEN'] )
__lowerCAmelCase: Tuple = g.get_repo('huggingface/accelerate' )
__lowerCAmelCase: str = repo.get_issues(state='open' )
for issue in open_issues:
__lowerCAmelCase: Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda SCREAMING_SNAKE_CASE : i.created_at , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = comments[0] if len(SCREAMING_SNAKE_CASE ) > 0 else None
__lowerCAmelCase: Tuple = dt.utcnow()
__lowerCAmelCase: Optional[int] = (current_time - issue.updated_at).days
__lowerCAmelCase: str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 322 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_lowerCAmelCase : List[str] = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
class _UpperCamelCase ( unittest.TestCase , snake_case__ ):
def UpperCAmelCase_ ( self :Optional[int] ) -> List[str]:
UpperCAmelCase__ = load_tool("text-question-answering" )
self.tool.setup()
UpperCAmelCase__ = load_tool("text-question-answering" , remote=lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] ) -> List[Any]:
UpperCAmelCase__ = self.tool(lowerCamelCase , "What did Hugging Face do in April 2021?" )
self.assertEqual(lowerCamelCase , "launched the BigScience Research Workshop" )
def UpperCAmelCase_ ( self :int ) -> Union[str, Any]:
UpperCAmelCase__ = self.remote_tool(lowerCamelCase , "What did Hugging Face do in April 2021?" )
self.assertEqual(lowerCamelCase , "launched the BigScience Research Workshop" )
def UpperCAmelCase_ ( self :List[Any] ) -> str:
UpperCAmelCase__ = self.tool(text=lowerCamelCase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(lowerCamelCase , "launched the BigScience Research Workshop" )
def UpperCAmelCase_ ( self :List[Any] ) -> Any:
UpperCAmelCase__ = self.remote_tool(text=lowerCamelCase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(lowerCamelCase , "launched the BigScience Research Workshop" )
| 169 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 322 | 0 |
from math import factorial
def A__ ( __lowerCamelCase, __lowerCamelCase ):
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(__lowerCamelCase ) // (factorial(__lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"The number of five-card hands possible from a standard",
F"""fifty-two card deck is: {combinations(52, 5)}\n""",
)
print(
"If a class of 40 students must be arranged into groups of",
F"""4 for group projects, there are {combinations(40, 4)} ways""",
"to arrange them.\n",
)
print(
"If 10 teams are competing in a Formula One race, there",
F"""are {combinations(10, 3)} ways that first, second and""",
"third place can be awarded.",
)
| 299 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Tuple , UpperCAmelCase : float , UpperCAmelCase : Callable , UpperCAmelCase : int , UpperCAmelCase : float = 1.0 , UpperCAmelCase : str = None , ) -> Union[str, Any]:
super().__init__()
__lowerCAmelCase: Optional[Any] = initial_learning_rate
__lowerCAmelCase: str = warmup_steps
__lowerCAmelCase: Optional[int] = power
__lowerCAmelCase: str = decay_schedule_fn
__lowerCAmelCase: Tuple = name
def __call__( self : int , UpperCAmelCase : Dict ) -> Optional[int]:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCAmelCase: List[str] = tf.cast(UpperCAmelCase , tf.floataa )
__lowerCAmelCase: Tuple = tf.cast(self.warmup_steps , tf.floataa )
__lowerCAmelCase: List[str] = global_step_float / warmup_steps_float
__lowerCAmelCase: List[str] = self.initial_learning_rate * tf.math.pow(UpperCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=UpperCAmelCase , )
def UpperCAmelCase ( self : Tuple ) -> int:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 0.9 , SCREAMING_SNAKE_CASE : float = 0.9_9_9 , SCREAMING_SNAKE_CASE : float = 1E-8 , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 , SCREAMING_SNAKE_CASE : Optional[List[str]] = None , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=SCREAMING_SNAKE_CASE , )
if num_warmup_steps:
__lowerCAmelCase: Optional[int] = WarmUp(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_schedule_fn=SCREAMING_SNAKE_CASE , warmup_steps=SCREAMING_SNAKE_CASE , )
if weight_decay_rate > 0.0:
__lowerCAmelCase: List[Any] = AdamWeightDecay(
learning_rate=SCREAMING_SNAKE_CASE , weight_decay_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase: Dict = tf.keras.optimizers.Adam(
learning_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , UpperCAmelCase : float = 0.9 , UpperCAmelCase : float = 0.999 , UpperCAmelCase : float = 1E-7 , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : str = "AdamWeightDecay" , **UpperCAmelCase : str , ) -> int:
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
__lowerCAmelCase: List[Any] = weight_decay_rate
__lowerCAmelCase: List[str] = include_in_weight_decay
__lowerCAmelCase: Optional[Any] = exclude_from_weight_decay
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Tuple ) -> Optional[int]:
__lowerCAmelCase: Union[str, Any] = {'WarmUp': WarmUp}
return super(UpperCAmelCase , cls ).from_config(UpperCAmelCase , custom_objects=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
super(UpperCAmelCase , self )._prepare_local(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> List[str]:
__lowerCAmelCase: Dict = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None , **UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase: Tuple = list(zip(*UpperCAmelCase ) )
return super(UpperCAmelCase , self ).apply_gradients(zip(UpperCAmelCase , UpperCAmelCase ) , name=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCAmelCase: Dict = apply_state or {}
__lowerCAmelCase: Union[str, Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCAmelCase: str = self._fallback_apply_state(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Tuple = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any]=None ) -> List[Any]:
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_dense(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[Any]=None ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase: Any = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: str = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_sparse(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase: List[str] = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return False
return True
class A_ ( snake_case__ ):
def __init__( self : int ) -> List[Any]:
__lowerCAmelCase: Tuple = []
__lowerCAmelCase: int = None
@property
def UpperCAmelCase ( self : Dict ) -> List[Any]:
if self._accum_steps is None:
__lowerCAmelCase: List[Any] = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Optional[Any] , UpperCAmelCase : Any ) -> Any:
if not self._gradients:
__lowerCAmelCase: Any = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(UpperCAmelCase ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(UpperCAmelCase ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(UpperCAmelCase )}''' )
for accum_gradient, gradient in zip(self._gradients , UpperCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(UpperCAmelCase )
self._accum_steps.assign_add(1 )
def UpperCAmelCase ( self : int ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(UpperCAmelCase ) )
| 322 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _a ( snake_case__ ):
__a : str = 'openai/whisper-base'
__a : Union[str, Any] = (
'This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '
'transcribed text.'
)
__a : Any = 'transcriber'
__a : Dict = WhisperProcessor
__a : List[Any] = WhisperForConditionalGeneration
__a : List[str] = ['audio']
__a : List[str] = ['text']
def A ( self : str , lowercase : Optional[int] ):
'''simple docstring'''
return self.pre_processor(lowercase , return_tensors='''pt''' ).input_features
def A ( self : Tuple , lowercase : List[str] ):
'''simple docstring'''
return self.model.generate(inputs=lowercase )
def A ( self : int , lowercase : Union[str, Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(lowercase , skip_special_tokens=lowercase )[0]
| 34 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any]=[] ) -> str:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = size[0] - overlap_pixels * 2
__lowerCAmelCase: str = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__lowerCAmelCase: Any = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
__lowerCAmelCase: int = np.pad(SCREAMING_SNAKE_CASE , mode='linear_ramp' , pad_width=SCREAMING_SNAKE_CASE , end_values=0 )
if "l" in remove_borders:
__lowerCAmelCase: Dict = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__lowerCAmelCase: Tuple = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__lowerCAmelCase: List[Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__lowerCAmelCase: List[str] = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
return max(SCREAMING_SNAKE_CASE , min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] ) -> int:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : [int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = list(SCREAMING_SNAKE_CASE )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__lowerCAmelCase: int = clamp_rect(SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase: List[Any] = Image.new('RGB' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(SCREAMING_SNAKE_CASE , (original_slice, 0) )
return result
def _a ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__lowerCAmelCase: List[Any] = tile.crop(SCREAMING_SNAKE_CASE )
return tile
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = n % d
return n - divisor
class A_ ( snake_case__ ):
def __init__( self : Optional[Any] , UpperCAmelCase : AutoencoderKL , UpperCAmelCase : CLIPTextModel , UpperCAmelCase : CLIPTokenizer , UpperCAmelCase : UNetaDConditionModel , UpperCAmelCase : DDPMScheduler , UpperCAmelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase : int = 3_5_0 , ) -> Optional[Any]:
super().__init__(
vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , low_res_scheduler=UpperCAmelCase , scheduler=UpperCAmelCase , max_noise_level=UpperCAmelCase , )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ) -> Optional[int]:
torch.manual_seed(0 )
__lowerCAmelCase: Optional[int] = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__lowerCAmelCase: Optional[Any] = add_overlap_rect(UpperCAmelCase , UpperCAmelCase , image.size )
__lowerCAmelCase: Any = image.crop(UpperCAmelCase )
__lowerCAmelCase: Any = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__lowerCAmelCase: Tuple = translated_slice_x - (original_image_slice / 2)
__lowerCAmelCase: Union[str, Any] = max(0 , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = squeeze_tile(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = to_input.size
__lowerCAmelCase: List[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__lowerCAmelCase: int = super(UpperCAmelCase , self ).__call__(image=UpperCAmelCase , **UpperCAmelCase ).images[0]
__lowerCAmelCase: Dict = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Union[str, Any] = unsqueeze_tile(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Optional[int] = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
__lowerCAmelCase: int = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=UpperCAmelCase ) , mode='L' , )
final_image.paste(
UpperCAmelCase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , UpperCAmelCase )
@torch.no_grad()
def __call__( self : Optional[Any] , UpperCAmelCase : Union[str, List[str]] , UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , UpperCAmelCase : int = 7_5 , UpperCAmelCase : float = 9.0 , UpperCAmelCase : int = 5_0 , UpperCAmelCase : Optional[Union[str, List[str]]] = None , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[torch.Generator] = None , UpperCAmelCase : Optional[torch.FloatTensor] = None , UpperCAmelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 1_2_8 , UpperCAmelCase : int = 3_2 , UpperCAmelCase : int = 3_2 , ) -> str:
__lowerCAmelCase: List[Any] = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
__lowerCAmelCase: str = math.ceil(image.size[0] / tile_size )
__lowerCAmelCase: List[Any] = math.ceil(image.size[1] / tile_size )
__lowerCAmelCase: Optional[Any] = tcx * tcy
__lowerCAmelCase: Tuple = 0
for y in range(UpperCAmelCase ):
for x in range(UpperCAmelCase ):
self._process_tile(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , prompt=UpperCAmelCase , num_inference_steps=UpperCAmelCase , guidance_scale=UpperCAmelCase , noise_level=UpperCAmelCase , negative_prompt=UpperCAmelCase , num_images_per_prompt=UpperCAmelCase , eta=UpperCAmelCase , generator=UpperCAmelCase , latents=UpperCAmelCase , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: Any = 'stabilityai/stable-diffusion-x4-upscaler'
__lowerCAmelCase: Dict = StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE , revision='fp16' , torch_dtype=torch.floataa )
__lowerCAmelCase: Optional[Any] = pipe.to('cuda' )
__lowerCAmelCase: Tuple = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(SCREAMING_SNAKE_CASE : Tuple ):
print(f'''progress: {obj['progress']:.4f}''' )
obj["image"].save('diffusers_library_progress.jpg' )
__lowerCAmelCase: str = pipe(image=SCREAMING_SNAKE_CASE , prompt='Black font, white background, vector' , noise_level=40 , callback=SCREAMING_SNAKE_CASE )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCamelCase = logging.get_logger(__name__)
class lowerCamelCase_ ( snake_case__ ):
"""simple docstring"""
a_ =['pixel_values']
def __init__( self : Optional[int] , _a : bool = True , _a : Dict[str, int] = None , _a : PILImageResampling = PIL.Image.BICUBIC , _a : bool = True , _a : Dict[str, int] = None , _a : Union[int, float] = 1 / 255 , _a : bool = True , _a : bool = True , _a : Optional[Union[float, List[float]]] = None , _a : Optional[Union[float, List[float]]] = None , **_a : List[Any] , ) -> None:
super().__init__(**_a )
__lowerCamelCase : Union[str, Any] = size if size is not None else {'height': 256, 'width': 256}
__lowerCamelCase : Any = get_size_dict(_a )
__lowerCamelCase : List[Any] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
__lowerCamelCase : Optional[int] = get_size_dict(_a , param_name='crop_size' )
__lowerCamelCase : str = do_resize
__lowerCamelCase : int = size
__lowerCamelCase : Any = resample
__lowerCamelCase : Union[str, Any] = do_center_crop
__lowerCamelCase : Any = crop_size
__lowerCamelCase : Optional[Any] = do_rescale
__lowerCamelCase : List[str] = rescale_factor
__lowerCamelCase : int = do_normalize
__lowerCamelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowerCamelCase : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _lowercase ( self : Any , _a : np.ndarray , _a : Dict[str, int] , _a : PILImageResampling = PIL.Image.BICUBIC , _a : Optional[Union[str, ChannelDimension]] = None , **_a : str , ) -> np.ndarray:
__lowerCamelCase : Dict = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
return resize(
_a , size=(size['height'], size['width']) , resample=_a , data_format=_a , **_a )
def _lowercase ( self : Union[str, Any] , _a : np.ndarray , _a : Dict[str, int] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : Tuple , ) -> np.ndarray:
__lowerCamelCase : List[Any] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
return center_crop(_a , size=(size['height'], size['width']) , data_format=_a , **_a )
def _lowercase ( self : Tuple , _a : np.ndarray , _a : Union[int, float] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : List[str] , ) -> Optional[Any]:
return rescale(_a , scale=_a , data_format=_a , **_a )
def _lowercase ( self : List[Any] , _a : np.ndarray , _a : Union[float, List[float]] , _a : Union[float, List[float]] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : Union[str, Any] , ) -> np.ndarray:
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def _lowercase ( self : Optional[int] , _a : ImageInput , _a : bool = None , _a : Dict[str, int] = None , _a : List[Any]=None , _a : bool = None , _a : Dict[str, int] = None , _a : bool = None , _a : float = None , _a : bool = None , _a : Optional[Union[float, List[float]]] = None , _a : Optional[Union[float, List[float]]] = None , _a : Optional[Union[str, TensorType]] = None , _a : ChannelDimension = ChannelDimension.FIRST , **_a : Any , ) -> PIL.Image.Image:
__lowerCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
__lowerCamelCase : Optional[int] = resample if resample is not None else self.resample
__lowerCamelCase : Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
__lowerCamelCase : int = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCamelCase : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
__lowerCamelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
__lowerCamelCase : Any = image_std if image_std is not None else self.image_std
__lowerCamelCase : Optional[Any] = size if size is not None else self.size
__lowerCamelCase : Union[str, Any] = get_size_dict(_a )
__lowerCamelCase : Any = crop_size if crop_size is not None else self.crop_size
__lowerCamelCase : int = get_size_dict(_a , param_name='crop_size' )
__lowerCamelCase : Dict = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__lowerCamelCase : Union[str, Any] = [to_numpy_array(_a ) for image in images]
if do_resize:
__lowerCamelCase : Dict = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
__lowerCamelCase : List[Any] = [self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
__lowerCamelCase : int = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
__lowerCamelCase : Tuple = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
__lowerCamelCase : Optional[Any] = [to_channel_dimension_format(_a , _a ) for image in images]
__lowerCamelCase : Optional[int] = {'pixel_values': images}
return BatchFeature(data=_a , tensor_type=_a )
| 208 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowerCAmelCase: Tuple = True
for i in range(1 , s + 1 ):
__lowerCAmelCase: Any = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowerCAmelCase: Optional[int] = dp[i][j - 1]
if arr[i - 1] <= j:
__lowerCAmelCase: Union[str, Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowerCAmelCase: Tuple = s - 2 * j
break
return diff
| 322 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
lowercase_ = """
Human: <<task>>
Assistant: """
lowercase_ = """huggingface-tools/default-prompts"""
lowercase_ = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def a__ ( snake_case , snake_case , snake_case="run" ):
"""simple docstring"""
if prompt_or_repo_id is None:
__SCREAMING_SNAKE_CASE : Optional[int] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , snake_case ) is not None:
return prompt_or_repo_id
__SCREAMING_SNAKE_CASE : Optional[Any] = cached_file(
snake_case , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(snake_case , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 303 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__lowerCAmelCase: Tuple = i + 1
else:
__lowerCAmelCase: List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 1_1, 1_5], 9) = }")
| 322 | 0 |
"""simple docstring"""
import math
def _lowerCAmelCase ( ):
UpperCAmelCase = input('Enter message: ' )
UpperCAmelCase = int(input(F"""Enter key [2-{len(lowercase_ ) - 1}]: """ ) )
UpperCAmelCase = input('Encryption/Decryption [e/d]: ' )
if mode.lower().startswith('e' ):
UpperCAmelCase = encrypt_message(lowercase_ , lowercase_ )
elif mode.lower().startswith('d' ):
UpperCAmelCase = decrypt_message(lowercase_ , lowercase_ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(F"""Output:\n{text + '|'}""" )
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = [''] * key
for col in range(lowercase_ ):
UpperCAmelCase = col
while pointer < len(lowercase_ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(lowercase_ )
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = math.ceil(len(lowercase_ ) / key )
UpperCAmelCase = key
UpperCAmelCase = (num_cols * num_rows) - len(lowercase_ )
UpperCAmelCase = [''] * num_cols
UpperCAmelCase = 0
UpperCAmelCase = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
UpperCAmelCase = 0
row += 1
return "".join(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 78 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_a = '''scheduler_config.json'''
class A_ ( snake_case__ ):
_lowercase : Optional[Any] = 1
_lowercase : Tuple = 2
_lowercase : Dict = 3
_lowercase : int = 4
_lowercase : Optional[Any] = 5
@dataclass
class A_ ( snake_case__ ):
_lowercase : jnp.ndarray
class A_ :
_lowercase : Optional[int] = SCHEDULER_CONFIG_NAME
_lowercase : Dict = ['dtype']
_lowercase : int = []
_lowercase : Union[str, Any] = True
@classmethod
def UpperCAmelCase ( cls : Union[str, Any] , UpperCAmelCase : Dict[str, Any] = None , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : List[str]=False , **UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCAmelCase , subfolder=UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase , )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.from_config(UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase )
if hasattr(UpperCAmelCase , 'create_state' ) and getattr(UpperCAmelCase , 'has_state' , UpperCAmelCase ):
__lowerCAmelCase: Dict = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, os.PathLike] , UpperCAmelCase : bool = False , **UpperCAmelCase : Any ) -> List[str]:
self.save_config(save_directory=UpperCAmelCase , push_to_hub=UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : str ) -> Dict:
return self._get_compatibles()
@classmethod
def UpperCAmelCase ( cls : Optional[int] ) -> Any:
__lowerCAmelCase: Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
__lowerCAmelCase: Dict = importlib.import_module(__name__.split('.' )[0] )
__lowerCAmelCase: Dict = [
getattr(UpperCAmelCase , UpperCAmelCase ) for c in compatible_classes_str if hasattr(UpperCAmelCase , UpperCAmelCase )
]
return compatible_classes
def _a ( SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : Tuple[int] ) -> jnp.ndarray:
"""simple docstring"""
assert len(SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE ) - x.ndim) ) , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any=0.9_9_9 , SCREAMING_SNAKE_CASE : List[Any]=jnp.floataa ) -> jnp.ndarray:
"""simple docstring"""
def alpha_bar(SCREAMING_SNAKE_CASE : str ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowerCAmelCase: str = []
for i in range(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Union[str, Any] = i / num_diffusion_timesteps
__lowerCAmelCase: List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE ) / alpha_bar(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) )
return jnp.array(SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class A_ :
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Optional[int] ) -> Any:
__lowerCAmelCase: str = scheduler.config
if config.trained_betas is not None:
__lowerCAmelCase: Tuple = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowerCAmelCase: Any = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCAmelCase: List[Any] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCAmelCase: str = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
__lowerCAmelCase: Optional[Any] = 1.0 - betas
__lowerCAmelCase: Optional[Any] = jnp.cumprod(UpperCAmelCase , axis=0 )
return cls(
alphas=UpperCAmelCase , betas=UpperCAmelCase , alphas_cumprod=UpperCAmelCase , )
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = state.alphas_cumprod
__lowerCAmelCase: str = alphas_cumprod[timesteps] ** 0.5
__lowerCAmelCase: Any = sqrt_alpha_prod.flatten()
__lowerCAmelCase: Any = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
__lowerCAmelCase: Any = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCAmelCase: str = sqrt_one_minus_alpha_prod.flatten()
__lowerCAmelCase: str = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Tuple = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 322 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : str=13 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Any=99 , UpperCamelCase__ : Union[str, Any]=32 , UpperCamelCase__ : Optional[int]=5 , UpperCamelCase__ : List[Any]=4 , UpperCamelCase__ : Optional[int]=37 , UpperCamelCase__ : Union[str, Any]="gelu" , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Optional[int]=512 , UpperCamelCase__ : Any=16 , UpperCamelCase__ : List[str]=2 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Union[str, Any]=4 , UpperCamelCase__ : List[str]=None , ) -> int:
"""simple docstring"""
__magic_name__ = parent
__magic_name__ = batch_size
__magic_name__ = seq_length
__magic_name__ = is_training
__magic_name__ = use_token_type_ids
__magic_name__ = use_labels
__magic_name__ = vocab_size
__magic_name__ = hidden_size
__magic_name__ = num_hidden_layers
__magic_name__ = num_attention_heads
__magic_name__ = intermediate_size
__magic_name__ = hidden_act
__magic_name__ = hidden_dropout_prob
__magic_name__ = attention_probs_dropout_prob
__magic_name__ = max_position_embeddings
__magic_name__ = type_vocab_size
__magic_name__ = type_sequence_label_size
__magic_name__ = initializer_range
__magic_name__ = num_labels
__magic_name__ = num_choices
__magic_name__ = scope
__magic_name__ = self.vocab_size - 1
def _lowercase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ = None
if self.use_token_type_ids:
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ = None
__magic_name__ = None
__magic_name__ = None
if self.use_labels:
__magic_name__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__magic_name__ = ids_tensor([self.batch_size] , self.num_choices )
__magic_name__ = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
__magic_name__ = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _lowercase ( self : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any , *UpperCamelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
__magic_name__ = OpenAIGPTModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ , head_mask=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
__magic_name__ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self : Any , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , *UpperCamelCase__ : List[Any] ) -> Any:
"""simple docstring"""
__magic_name__ = OpenAIGPTLMHeadModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] , *UpperCamelCase__ : List[str] ) -> Tuple:
"""simple docstring"""
__magic_name__ = OpenAIGPTDoubleHeadsModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , *UpperCamelCase__ : int ) -> int:
"""simple docstring"""
__magic_name__ = self.num_labels
__magic_name__ = OpenAIGPTForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__magic_name__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ = model(UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self : int ) -> str:
"""simple docstring"""
__magic_name__ = self.prepare_config_and_inputs()
(
__magic_name__
) = config_and_inputs
__magic_name__ = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
a__ = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
a__ = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
a__ = (
{
'feature-extraction': OpenAIGPTModel,
'text-classification': OpenAIGPTForSequenceClassification,
'text-generation': OpenAIGPTLMHeadModel,
'zero-shot': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def _lowercase ( self : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] ) -> Any:
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def _lowercase ( self : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : str=False ) -> Any:
"""simple docstring"""
__magic_name__ = super()._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
__magic_name__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=UpperCamelCase__ , )
__magic_name__ = inputs_dict['labels']
__magic_name__ = inputs_dict['labels']
__magic_name__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=UpperCamelCase__ , )
__magic_name__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase__ )
return inputs_dict
def _lowercase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__magic_name__ = OpenAIGPTModelTester(self )
__magic_name__ = ConfigTester(self , config_class=UpperCamelCase__ , n_embd=37 )
def _lowercase ( self : Tuple ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self : List[Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase__ )
def _lowercase ( self : str ) -> int:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*UpperCamelCase__ )
def _lowercase ( self : List[str] ) -> Dict:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*UpperCamelCase__ )
def _lowercase ( self : Optional[Any] ) -> str:
"""simple docstring"""
__magic_name__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase__ )
@slow
def _lowercase ( self : Dict ) -> int:
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ = OpenAIGPTModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__magic_name__ = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(UpperCamelCase__ )
__magic_name__ = torch.tensor([[481, 4735, 544]] , dtype=torch.long , device=UpperCamelCase__ ) # the president is
__magic_name__ = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
4_0477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
__magic_name__ = model.generate(UpperCamelCase__ , do_sample=UpperCamelCase__ )
self.assertListEqual(output_ids[0].tolist() , UpperCamelCase__ )
| 88 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ) -> list[str]:
"""simple docstring"""
__lowerCAmelCase: int = set()
# keep track of all the paths to be checked
__lowerCAmelCase: str = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: Union[str, Any] = path[-1]
if node not in explored:
__lowerCAmelCase: Dict = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
new_path.append(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Optional[int] = [start]
__lowerCAmelCase: Dict = set(SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: Optional[int] = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Any = queue.pop(0 )
if node == target:
__lowerCAmelCase: Optional[int] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 322 | 0 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
A__: Any = '''src/transformers'''
# Matches is_xxx_available()
A__: int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
A__: Union[str, Any] = re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A__: Union[str, Any] = re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
A__: List[str] = re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
A__: Any = re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A__: Optional[Any] = re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
A__: Tuple = re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
A__: Dict = re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
A__: Tuple = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
A__: int = re.compile(R'''^\s*try:''')
# Catches a line with else:
A__: Dict = re.compile(R'''^\s*else:''')
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
if _re_test_backend.search(_UpperCAmelCase ) is None:
return None
_a : Union[str, Any] =[b[0] for b in _re_backend.findall(_UpperCAmelCase )]
backends.sort()
return "_and_".join(_UpperCAmelCase )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> Optional[Any]:
with open(_UpperCAmelCase ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
_a : str =f.readlines()
_a : Optional[Any] =0
while line_index < len(_UpperCAmelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_UpperCAmelCase ):
return None
# First grab the objects without a specific backend in _import_structure
_a : Union[str, Any] =[]
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
_a : Any =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_UpperCAmelCase ):
_a : Tuple =_re_one_line_import_struct.search(_UpperCAmelCase ).groups()[0]
_a : Any =re.findall(R"""\[([^\]]+)\]""" ,_UpperCAmelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
_a : Any =_re_import_struct_key_value.search(_UpperCAmelCase )
if single_line_import_search is not None:
_a : List[Any] =[obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(_UpperCAmelCase ) > 0]
objects.extend(_UpperCAmelCase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
_a : Optional[int] ={'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
_a : List[str] =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_a : Optional[Any] =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_a : int =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
_a : Optional[int] =lines[line_index]
if _re_import_struct_add_one.search(_UpperCAmelCase ) is not None:
objects.append(_re_import_struct_add_one.search(_UpperCAmelCase ).groups()[0] )
elif _re_import_struct_add_many.search(_UpperCAmelCase ) is not None:
_a : List[str] =_re_import_struct_add_many.search(_UpperCAmelCase ).groups()[0].split(""", """ )
_a : Any =[obj[1:-1] for obj in imports if len(_UpperCAmelCase ) > 0]
objects.extend(_UpperCAmelCase )
elif _re_between_brackets.search(_UpperCAmelCase ) is not None:
_a : str =_re_between_brackets.search(_UpperCAmelCase ).groups()[0].split(""", """ )
_a : Union[str, Any] =[obj[1:-1] for obj in imports if len(_UpperCAmelCase ) > 0]
objects.extend(_UpperCAmelCase )
elif _re_quote_object.search(_UpperCAmelCase ) is not None:
objects.append(_re_quote_object.search(_UpperCAmelCase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
_a : Any =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_a : List[str] =[]
while (
line_index < len(_UpperCAmelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
_a : List[str] =lines[line_index]
_a : List[str] =_re_import.search(_UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
_a : Dict ={'none': objects}
# Let's continue with backend-specific objects
while line_index < len(_UpperCAmelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
_a : Tuple =find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_a : List[Any] =None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_a : List[Any] =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
_a : Tuple =lines[line_index]
_a : List[str] =_re_import.search(_UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
_a : Optional[int] =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ,_UpperCAmelCase : Optional[int] ) -> Optional[int]:
def find_duplicates(_UpperCAmelCase : int ):
return [k for k, v in collections.Counter(_UpperCAmelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_a : Optional[int] =[]
for key in import_dict_objects.keys():
_a : int =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}" )
_a : List[Any] =find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_a : int ='base imports' if key == 'none' else F"{key} backend"
errors.append(F"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F" {a} in _import_structure but not in TYPE_HINT." )
return errors
def SCREAMING_SNAKE_CASE_ ( ) -> List[Any]:
_a : str =[]
for root, _, files in os.walk(_UpperCAmelCase ):
if "__init__.py" in files:
_a : Union[str, Any] =os.path.join(_UpperCAmelCase ,"""__init__.py""" )
_a : List[str] =parse_init(_UpperCAmelCase )
if objects is not None:
_a : str =analyze_results(*_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
_a : Dict =F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append("""\n""".join(_UpperCAmelCase ) )
if len(_UpperCAmelCase ) > 0:
raise ValueError("""\n\n""".join(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE_ ( ) -> str:
_a : Tuple =[]
for path, directories, files in os.walk(_UpperCAmelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(_UpperCAmelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_UpperCAmelCase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
_a : Dict =str((Path(_UpperCAmelCase ) / folder).relative_to(_UpperCAmelCase ) )
_a : Tuple =short_path.replace(os.path.sep ,""".""" )
submodules.append(_UpperCAmelCase )
for fname in files:
if fname == "__init__.py":
continue
_a : List[str] =str((Path(_UpperCAmelCase ) / fname).relative_to(_UpperCAmelCase ) )
_a : Union[str, Any] =short_path.replace(""".py""" ,"""""" ).replace(os.path.sep ,""".""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(_UpperCAmelCase )
return submodules
A__: int = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def SCREAMING_SNAKE_CASE_ ( ) -> Union[str, Any]:
from transformers.utils import direct_transformers_import
_a : List[Any] =direct_transformers_import(_UpperCAmelCase )
_a : int =set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_UpperCAmelCase ,"""__init__.py""" ) ,"""r""" ) as f:
_a : int =f.read()
import_structure_keys.update(set(re.findall(R"""import_structure\[\"([^\"]*)\"\]""" ,_UpperCAmelCase ) ) )
_a : str =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_UpperCAmelCase ) > 0:
_a : List[Any] ='\n'.join(F"- {module}" for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registed in the main init of Transformers:\n"""
F"{list_of_modules}\n"
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 276 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
_lowercase : int = ['image_processor', 'tokenizer']
_lowercase : Union[str, Any] = 'LayoutLMv3ImageProcessor'
_lowercase : List[str] = ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Any , UpperCAmelCase : Dict=None , UpperCAmelCase : Tuple=None , **UpperCAmelCase : Optional[Any] ) -> str:
__lowerCAmelCase: str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase , )
__lowerCAmelCase: List[Any] = kwargs.pop('feature_extractor' )
__lowerCAmelCase: Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCAmelCase , UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__lowerCAmelCase: str = self.image_processor(images=UpperCAmelCase , return_tensors=UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCAmelCase: List[str] = features['words']
__lowerCAmelCase: List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel values
__lowerCAmelCase: Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__lowerCAmelCase: int = self.get_overflowing_images(UpperCAmelCase , encoded_inputs['overflow_to_sample_mapping'] )
__lowerCAmelCase: str = images
return encoded_inputs
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCAmelCase: str = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
return images_with_overflow
def UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Any , *UpperCAmelCase : Dict , **UpperCAmelCase : Any ) -> List[str]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase , )
return self.image_processor
| 322 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class __SCREAMING_SNAKE_CASE :
def __init__( self : Any , __lowercase : Union[str, Any] , __lowercase : str=13 , __lowercase : Optional[Any]=7 , __lowercase : str=True , __lowercase : Any=True , __lowercase : Tuple=True , __lowercase : Any=True , __lowercase : Optional[int]=True , __lowercase : List[str]=False , __lowercase : Tuple=False , __lowercase : int=False , __lowercase : Optional[int]=2 , __lowercase : Any=99 , __lowercase : str=0 , __lowercase : Dict=32 , __lowercase : int=5 , __lowercase : Optional[int]=4 , __lowercase : Any=0.1 , __lowercase : str=0.1 , __lowercase : int=5_12 , __lowercase : str=2 , __lowercase : Optional[int]=0.02 , __lowercase : Optional[Any]=2 , __lowercase : List[str]=4 , __lowercase : Dict="last" , __lowercase : int=True , __lowercase : Dict=None , __lowercase : Union[str, Any]=0 , ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Dict =batch_size
SCREAMING_SNAKE_CASE__ : Tuple =seq_length
SCREAMING_SNAKE_CASE__ : Tuple =is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] =use_input_lengths
SCREAMING_SNAKE_CASE__ : List[str] =use_token_type_ids
SCREAMING_SNAKE_CASE__ : Dict =use_labels
SCREAMING_SNAKE_CASE__ : int =gelu_activation
SCREAMING_SNAKE_CASE__ : Optional[int] =sinusoidal_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =causal
SCREAMING_SNAKE_CASE__ : Optional[Any] =asm
SCREAMING_SNAKE_CASE__ : int =n_langs
SCREAMING_SNAKE_CASE__ : Tuple =vocab_size
SCREAMING_SNAKE_CASE__ : List[Any] =n_special
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict =num_attention_heads
SCREAMING_SNAKE_CASE__ : int =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict =max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[str] =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str =initializer_range
SCREAMING_SNAKE_CASE__ : List[str] =num_labels
SCREAMING_SNAKE_CASE__ : List[str] =num_choices
SCREAMING_SNAKE_CASE__ : Optional[int] =summary_type
SCREAMING_SNAKE_CASE__ : Any =use_proj
SCREAMING_SNAKE_CASE__ : Optional[Any] =scope
SCREAMING_SNAKE_CASE__ : Dict =bos_token_id
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : str =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Any =None
if self.use_input_lengths:
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
SCREAMING_SNAKE_CASE__ : str =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
SCREAMING_SNAKE_CASE__ : int =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size] , 2 ).float()
SCREAMING_SNAKE_CASE__ : str =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Dict =self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __magic_name__ ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : int , __lowercase : int , __lowercase : List[str] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[str] =XLMModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Any =model(__lowercase , lengths=__lowercase , langs=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , langs=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[Any] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Any , __lowercase : List[Any] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Dict , ) -> int:
SCREAMING_SNAKE_CASE__ : str =XLMWithLMHeadModel(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Optional[int] , __lowercase : Any , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : List[str] , __lowercase : str , __lowercase : Dict , ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Dict =XLMForQuestionAnsweringSimple(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , start_positions=__lowercase , end_positions=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __magic_name__ ( self : List[str] , __lowercase : Dict , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : List[str] , __lowercase : List[Any] , __lowercase : Any , __lowercase : Optional[int] , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =XLMForQuestionAnswering(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(
__lowercase , start_positions=__lowercase , end_positions=__lowercase , cls_index=__lowercase , is_impossible=__lowercase , p_mask=__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =model(
__lowercase , start_positions=__lowercase , end_positions=__lowercase , cls_index=__lowercase , is_impossible=__lowercase , )
(SCREAMING_SNAKE_CASE__ ) : List[str] =result_with_labels.to_tuple()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase , start_positions=__lowercase , end_positions=__lowercase )
(SCREAMING_SNAKE_CASE__ ) : List[Any] =result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __magic_name__ ( self : Dict , __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : List[str] , __lowercase : Any , __lowercase : Union[str, Any] , __lowercase : str , __lowercase : List[str] , ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =XLMForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : Optional[Any] , __lowercase : str , __lowercase : int , __lowercase : str , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Union[str, Any] , ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.num_labels
SCREAMING_SNAKE_CASE__ : Tuple =XLMForTokenClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self : str , __lowercase : Tuple , __lowercase : str , __lowercase : Any , __lowercase : str , __lowercase : str , __lowercase : str , __lowercase : str , __lowercase : List[str] , __lowercase : List[Any] , ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_choices
SCREAMING_SNAKE_CASE__ : Optional[Any] =XLMForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : List[str] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Dict =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.prepare_config_and_inputs()
(
SCREAMING_SNAKE_CASE__
) : Union[str, Any] =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
snake_case_ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
snake_case_ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
snake_case_ = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __magic_name__ ( self : Any , __lowercase : List[Any] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : str , __lowercase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('''Fast''' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __magic_name__ ( self : Any , __lowercase : Optional[Any] , __lowercase : Tuple , __lowercase : Tuple=False ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] =super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
SCREAMING_SNAKE_CASE__ : str =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : int =XLMModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[int] =ConfigTester(self , config_class=__lowercase , emb_dim=37 )
def __magic_name__ ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*__lowercase )
def __magic_name__ ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*__lowercase )
def __magic_name__ ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*__lowercase )
def __magic_name__ ( self : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*__lowercase )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*__lowercase )
def __magic_name__ ( self : Optional[Any] , __lowercase : int , __lowercase : Tuple , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Optional[Any] , __lowercase : Optional[int]=False , __lowercase : Dict=1 ) -> Dict:
self.assertIsInstance(__lowercase , __lowercase )
self.assertListEqual(
[isinstance(__lowercase , __lowercase ) for iter_attentions in attentions] , [True] * len(__lowercase ) )
self.assertEqual(len(__lowercase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(__lowercase ):
# adds PAD dummy token
SCREAMING_SNAKE_CASE__ : int =min_length + idx + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] =min_length + idx + 1
SCREAMING_SNAKE_CASE__ : Any =(
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(__lowercase ) )
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : Any , __lowercase : Optional[Any] , __lowercase : str=False , __lowercase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(__lowercase , __lowercase )
self.assertListEqual(
[isinstance(__lowercase , __lowercase ) for iter_hidden_states in hidden_states] , [True] * len(__lowercase ) , )
self.assertEqual(len(__lowercase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(__lowercase ):
# adds PAD dummy token
SCREAMING_SNAKE_CASE__ : Any =min_length + idx + 1
SCREAMING_SNAKE_CASE__ : str =(batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(__lowercase ) , )
pass
@slow
def __magic_name__ ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : List[Any] =XLMModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __magic_name__ ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048''' )
model.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor([[14, 4_47]] , dtype=torch.long , device=__lowercase ) # the president
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
14,
4_47,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
SCREAMING_SNAKE_CASE__ : str =model.generate(__lowercase , do_sample=__lowercase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , __lowercase ) | 152 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : tuple , SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int]=False , ) -> str:
"""simple docstring"""
output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , use_external_data_format=SCREAMING_SNAKE_CASE , enable_onnx_checker=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
else:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool = False ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__lowerCAmelCase: str = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__lowerCAmelCase: Dict = 'cpu'
__lowerCAmelCase: Optional[int] = Path(SCREAMING_SNAKE_CASE )
# VAE DECODER
__lowerCAmelCase: Optional[Any] = AutoencoderKL.from_pretrained(model_path + '/vae' )
__lowerCAmelCase: Union[str, Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
__lowerCAmelCase: Any = vae_decoder.decode
onnx_export(
SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 322 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase ( snake_case__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
SCREAMING_SNAKE_CASE = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
SCREAMING_SNAKE_CASE = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
SCREAMING_SNAKE_CASE = False
@property
def _lowerCAmelCase( self ) -> Tuple:
return 32
@property
def _lowerCAmelCase( self ) -> Union[str, Any]:
return 32
@property
def _lowerCAmelCase( self ) -> Union[str, Any]:
return self.time_input_dim
@property
def _lowerCAmelCase( self ) -> List[Any]:
return self.time_input_dim * 4
@property
def _lowerCAmelCase( self ) -> Dict:
return 100
@property
def _lowerCAmelCase( self ) -> int:
torch.manual_seed(0 )
lowercase__ : Union[str, Any] = {
'in_channels': 8,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image_hint',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowercase__ : str = UNetaDConditionModel(**__lowerCAmelCase )
return model
@property
def _lowerCAmelCase( self ) -> Tuple:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def _lowerCAmelCase( self ) -> Optional[Any]:
torch.manual_seed(0 )
lowercase__ : Optional[int] = VQModel(**self.dummy_movq_kwargs )
return model
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : int = self.dummy_unet
lowercase__ : Optional[Any] = self.dummy_movq
lowercase__ : str = {
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.0_0_0_8_5,
'beta_end': 0.0_1_2,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowercase__ : Optional[int] = DDIMScheduler(**__lowerCAmelCase )
lowercase__ : int = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> List[str]:
lowercase__ : str = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
lowercase__ : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowerCAmelCase )
# create init_image
lowercase__ : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
lowercase__ : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : Dict = Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert('''RGB''' ).resize((256, 256) )
# create hint
lowercase__ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
if str(__lowerCAmelCase ).startswith('''mps''' ):
lowercase__ : List[Any] = torch.manual_seed(__lowerCAmelCase )
else:
lowercase__ : str = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
lowercase__ : List[str] = {
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'hint': hint,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def _lowerCAmelCase( self ) -> Dict:
lowercase__ : int = 'cpu'
lowercase__ : List[str] = self.get_dummy_components()
lowercase__ : Optional[Any] = self.pipeline_class(**__lowerCAmelCase )
lowercase__ : str = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Union[str, Any] = pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
lowercase__ : Dict = output.images
lowercase__ : str = pipe(
**self.get_dummy_inputs(__lowerCAmelCase ) , return_dict=__lowerCAmelCase , )[0]
lowercase__ : Optional[int] = image[0, -3:, -3:, -1]
lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : int = np.array(
[0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
lowercase__ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
lowercase__ : Tuple = init_image.resize((512, 512) )
lowercase__ : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
lowercase__ : Dict = torch.from_numpy(np.array(__lowerCAmelCase ) ).float() / 2_5_5.0
lowercase__ : Optional[Any] = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase__ : Dict = 'A robot, 4k photo'
lowercase__ : Union[str, Any] = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCAmelCase )
lowercase__ : List[Any] = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
lowercase__ : int = pipeline.to(__lowerCAmelCase )
pipeline.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Optional[int] = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase__ : Union[str, Any] = pipe_prior(
__lowerCAmelCase , image=__lowerCAmelCase , strength=0.8_5 , generator=__lowerCAmelCase , negative_prompt='''''' , ).to_tuple()
lowercase__ : Any = pipeline(
image=__lowerCAmelCase , image_embeds=__lowerCAmelCase , negative_image_embeds=__lowerCAmelCase , hint=__lowerCAmelCase , generator=__lowerCAmelCase , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type='''np''' , )
lowercase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
| 198 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square(SCREAMING_SNAKE_CASE , col + 1 )
__lowerCAmelCase: Tuple = update_area_of_max_square(row + 1 , col + 1 )
__lowerCAmelCase: int = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: List[str] = 1 + min([right, diagonal, down] )
__lowerCAmelCase: List[str] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
return sub_problem_sol
else:
return 0
__lowerCAmelCase: List[str] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__lowerCAmelCase: List[Any] = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: int = 1 + min([right, diagonal, down] )
__lowerCAmelCase: Union[str, Any] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sub_problem_sol
return sub_problem_sol
else:
return 0
__lowerCAmelCase: int = [0]
__lowerCAmelCase: int = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: int = [[0] * (cols + 1) for _ in range(rows + 1 )]
__lowerCAmelCase: Optional[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: Union[str, Any] = dp_array[row][col + 1]
__lowerCAmelCase: str = dp_array[row + 1][col + 1]
__lowerCAmelCase: Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
__lowerCAmelCase: Optional[Any] = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(dp_array[row][col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Dict = 0
return largest_square_area
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: Tuple = [0] * (cols + 1)
__lowerCAmelCase: Optional[int] = [0] * (cols + 1)
__lowerCAmelCase: str = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: int = current_row[col + 1]
__lowerCAmelCase: Union[str, Any] = next_row[col + 1]
__lowerCAmelCase: Any = next_row[col]
if mat[row][col] == 1:
__lowerCAmelCase: str = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(current_row[col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: int = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 322 | 0 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with open(lowerCamelCase__ ) as metadata_file:
lowerCamelCase_ = json.load(lowerCamelCase__ )
lowerCamelCase_ = LukeConfig(use_entity_aware_attention=lowerCamelCase__ , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
lowerCamelCase_ = torch.load(lowerCamelCase__ , map_location="cpu" )
# Load the entity vocab file
lowerCamelCase_ = load_entity_vocab(lowerCamelCase__ )
lowerCamelCase_ = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
lowerCamelCase_ = AddedToken("<ent>" , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ )
lowerCamelCase_ = AddedToken("<ent2>" , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = LukeTokenizer.from_pretrained(lowerCamelCase__ )
# Initialize the embeddings of the special tokens
lowerCamelCase_ = state_dict['embeddings.word_embeddings.weight']
lowerCamelCase_ = word_emb[tokenizer.convert_tokens_to_ids(["@"] )[0]].unsqueeze(0 )
lowerCamelCase_ = word_emb[tokenizer.convert_tokens_to_ids(["#"] )[0]].unsqueeze(0 )
lowerCamelCase_ = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
lowerCamelCase_ = F'encoder.layer.{layer_index}.attention.self.'
lowerCamelCase_ = state_dict[prefix + matrix_name]
lowerCamelCase_ = state_dict[prefix + matrix_name]
lowerCamelCase_ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
lowerCamelCase_ = state_dict['entity_embeddings.entity_embeddings.weight']
lowerCamelCase_ = entity_emb[entity_vocab['[MASK]']]
lowerCamelCase_ = LukeModel(config=lowerCamelCase__ ).eval()
lowerCamelCase_ = model.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
if not (len(lowerCamelCase__ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(F'Missing keys {", ".join(lowerCamelCase__ )}. Expected only missing embeddings.position_ids' )
if not (all(key.startswith("entity_predictions" ) or key.startswith("lm_head" ) for key in unexpected_keys )):
raise ValueError(
"Unexpected keys"
F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' )
# Check outputs
lowerCamelCase_ = LukeTokenizer.from_pretrained(lowerCamelCase__ , task="entity_classification" )
lowerCamelCase_ = (
'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
' new world number one avoid a humiliating second- round exit at Wimbledon .'
)
lowerCamelCase_ = (3_9, 4_2)
lowerCamelCase_ = tokenizer(lowerCamelCase__ , entity_spans=[span] , add_prefix_space=lowerCamelCase__ , return_tensors="pt" )
lowerCamelCase_ = model(**lowerCamelCase__ )
# Verify word hidden states
if model_size == "large":
lowerCamelCase_ = torch.Size((1, 4_2, 1_0_2_4) )
lowerCamelCase_ = torch.tensor(
[[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]] )
else: # base
lowerCamelCase_ = torch.Size((1, 4_2, 7_6_8) )
lowerCamelCase_ = torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
lowerCamelCase_ = torch.Size((1, 1, 1_0_2_4) )
lowerCamelCase_ = torch.tensor([[0.04_66, -0.01_06, -0.01_79]] )
else: # base
lowerCamelCase_ = torch.Size((1, 1, 7_6_8) )
lowerCamelCase_ = torch.tensor([[0.14_57, 0.10_44, 0.01_74]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , lowerCamelCase__ , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(lowerCamelCase__ ) )
model.save_pretrained(lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = {}
with open(lowerCamelCase__ , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(lowerCamelCase__ ):
lowerCamelCase_ = line.rstrip().split("\t" )
lowerCamelCase_ = index
return entity_vocab
if __name__ == "__main__":
__A =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
__A =parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 19 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_a = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = SavedModel()
__lowerCAmelCase: str = []
with open(os.path.join(SCREAMING_SNAKE_CASE , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__lowerCAmelCase: List[str] = json.load(SCREAMING_SNAKE_CASE )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(SCREAMING_SNAKE_CASE )] )
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__lowerCAmelCase: Optional[int] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowerCAmelCase: List[str] = sorted(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(SCREAMING_SNAKE_CASE )
if strict and len(SCREAMING_SNAKE_CASE ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(SCREAMING_SNAKE_CASE ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*SCREAMING_SNAKE_CASE , sep='\n' )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_a = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 322 | 0 |
from __future__ import annotations
_lowerCAmelCase : Optional[int] = "Muhammad Umer Farooq"
_lowerCAmelCase : List[Any] = "MIT"
_lowerCAmelCase : str = "1.0.0"
_lowerCAmelCase : List[Any] = "Muhammad Umer Farooq"
_lowerCAmelCase : Optional[int] = "[email protected]"
_lowerCAmelCase : List[Any] = "Alpha"
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class _UpperCamelCase ( snake_case__ ):
def __init__( self :List[Any] , lowerCamelCase :str ) -> None:
super().__init__()
UpperCAmelCase__ = []
UpperCAmelCase__ = domain
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :str , lowerCamelCase :list[tuple[str, str | None]] ) -> None:
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
UpperCAmelCase__ = parse.urljoin(self.domain , lowerCamelCase )
self.urls.append(lowerCamelCase )
def lowerCAmelCase ( _lowerCAmelCase : str ):
"""simple docstring"""
return ".".join(get_sub_domain_name(_lowerCAmelCase ).split("." )[-2:] )
def lowerCAmelCase ( _lowerCAmelCase : str ):
"""simple docstring"""
return parse.urlparse(_lowerCAmelCase ).netloc
def lowerCAmelCase ( _lowerCAmelCase : str = "https://github.com" ):
"""simple docstring"""
UpperCAmelCase__ = get_domain_name(_lowerCAmelCase )
# Initialize the parser
UpperCAmelCase__ = Parser(_lowerCAmelCase )
try:
# Open URL
UpperCAmelCase__ = requests.get(_lowerCAmelCase )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
UpperCAmelCase__ = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
UpperCAmelCase__ = requests.get(_lowerCAmelCase )
# Get the valid email.
UpperCAmelCase__ = re.findall("[a-zA-Z0-9]+@" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_lowerCAmelCase )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_lowerCAmelCase )
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = emails_from_url("https://github.com")
print(F'''{len(emails)} emails found:''')
print("\n".join(sorted(emails)))
| 169 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase: Union[str, Any] = qiskit.QuantumRegister(4 , 'qr' )
__lowerCAmelCase: List[Any] = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCAmelCase: Any = [input_a, input_a, carry_in]
__lowerCAmelCase: List[str] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(SCREAMING_SNAKE_CASE ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(SCREAMING_SNAKE_CASE ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(SCREAMING_SNAKE_CASE ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , SCREAMING_SNAKE_CASE ) # measure the last two qbits
__lowerCAmelCase: List[str] = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase: List[Any] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 322 | 0 |
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
def update_area_of_max_square(__lowerCamelCase, __lowerCamelCase ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
SCREAMING_SNAKE_CASE_ = update_area_of_max_square(__lowerCamelCase, col + 1 )
SCREAMING_SNAKE_CASE_ = update_area_of_max_square(row + 1, col + 1 )
SCREAMING_SNAKE_CASE_ = update_area_of_max_square(row + 1, __lowerCamelCase )
if mat[row][col]:
SCREAMING_SNAKE_CASE_ = 1 + min([right, diagonal, down] )
SCREAMING_SNAKE_CASE_ = max(largest_square_area[0], __lowerCamelCase )
return sub_problem_sol
else:
return 0
SCREAMING_SNAKE_CASE_ = [0]
update_area_of_max_square(0, 0 )
return largest_square_area[0]
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
def update_area_of_max_square_using_dp_array(
__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
SCREAMING_SNAKE_CASE_ = update_area_of_max_square_using_dp_array(__lowerCamelCase, col + 1, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = update_area_of_max_square_using_dp_array(row + 1, col + 1, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = update_area_of_max_square_using_dp_array(row + 1, __lowerCamelCase, __lowerCamelCase )
if mat[row][col]:
SCREAMING_SNAKE_CASE_ = 1 + min([right, diagonal, down] )
SCREAMING_SNAKE_CASE_ = max(largest_square_area[0], __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = sub_problem_sol
return sub_problem_sol
else:
return 0
SCREAMING_SNAKE_CASE_ = [0]
SCREAMING_SNAKE_CASE_ = [[-1] * cols for _ in range(__lowerCamelCase )]
update_area_of_max_square_using_dp_array(0, 0, __lowerCamelCase )
return largest_square_area[0]
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = [[0] * (cols + 1) for _ in range(rows + 1 )]
SCREAMING_SNAKE_CASE_ = 0
for row in range(rows - 1, -1, -1 ):
for col in range(cols - 1, -1, -1 ):
SCREAMING_SNAKE_CASE_ = dp_array[row][col + 1]
SCREAMING_SNAKE_CASE_ = dp_array[row + 1][col + 1]
SCREAMING_SNAKE_CASE_ = dp_array[row + 1][col]
if mat[row][col] == 1:
SCREAMING_SNAKE_CASE_ = 1 + min(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = max(dp_array[row][col], __lowerCamelCase )
else:
SCREAMING_SNAKE_CASE_ = 0
return largest_square_area
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * (cols + 1)
SCREAMING_SNAKE_CASE_ = [0] * (cols + 1)
SCREAMING_SNAKE_CASE_ = 0
for row in range(rows - 1, -1, -1 ):
for col in range(cols - 1, -1, -1 ):
SCREAMING_SNAKE_CASE_ = current_row[col + 1]
SCREAMING_SNAKE_CASE_ = next_row[col + 1]
SCREAMING_SNAKE_CASE_ = next_row[col]
if mat[row][col] == 1:
SCREAMING_SNAKE_CASE_ = 1 + min(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = max(current_row[col], __lowerCamelCase )
else:
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 299 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
def __init__( self : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : int=3 , UpperCAmelCase : int=4 , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=9_9 , UpperCAmelCase : Tuple=3_6 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]=3_7 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[str]=5_1_2 , UpperCAmelCase : int=1_6 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[Any]=6 , UpperCAmelCase : int=6 , UpperCAmelCase : str=3 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[str]=1_0_0_0 , ) -> int:
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: List[str] = batch_size
__lowerCAmelCase: Optional[Any] = num_channels
__lowerCAmelCase: Tuple = image_size
__lowerCAmelCase: str = patch_size
__lowerCAmelCase: List[str] = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Union[str, Any] = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: Dict = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: str = hidden_dropout_prob
__lowerCAmelCase: str = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: str = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: List[str] = coordinate_size
__lowerCAmelCase: Tuple = shape_size
__lowerCAmelCase: List[Any] = num_labels
__lowerCAmelCase: Any = num_choices
__lowerCAmelCase: List[str] = scope
__lowerCAmelCase: Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase: Optional[Any] = text_seq_length
__lowerCAmelCase: List[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase: int = self.text_seq_length + self.image_seq_length
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase: str = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: Optional[Any] = bbox[i, j, 3]
__lowerCAmelCase: Tuple = bbox[i, j, 1]
__lowerCAmelCase: Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Any = bbox[i, j, 2]
__lowerCAmelCase: int = bbox[i, j, 0]
__lowerCAmelCase: int = tmp_coordinate
__lowerCAmelCase: List[Any] = tf.constant(UpperCAmelCase )
__lowerCAmelCase: Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase: Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase: List[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase: int = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase: str = None
__lowerCAmelCase: Dict = None
if self.use_labels:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase: Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> int:
__lowerCAmelCase: Tuple = TFLayoutLMvaModel(config=UpperCAmelCase )
# text + image
__lowerCAmelCase: Dict = model(UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , training=UpperCAmelCase , )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase: str = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase: List[str] = model({'pixel_values': pixel_values} , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] ) -> int:
__lowerCAmelCase: List[str] = self.num_labels
__lowerCAmelCase: Tuple = TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int ) -> Any:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: List[str] = TFLayoutLMvaForTokenClassification(config=UpperCAmelCase )
__lowerCAmelCase: Any = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> Any:
__lowerCAmelCase: str = 2
__lowerCAmelCase: Dict = TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)): List[str] = config_and_inputs
__lowerCAmelCase: List[str] = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : Tuple = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Dict = False
_lowercase : Tuple = False
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> List[str]:
return True
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=False ) -> dict:
__lowerCAmelCase: Optional[Any] = copy.deepcopy(UpperCAmelCase )
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: int = {
k: tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase: Tuple = TFLayoutLMvaModelTester(self )
__lowerCAmelCase: str = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase: List[Any] = model_class(UpperCAmelCase )
if getattr(UpperCAmelCase , 'hf_compute_loss' , UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase: Optional[int] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: List[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=UpperCAmelCase )[0]
]
__lowerCAmelCase: Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase: Optional[Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Tuple = prepared_for_class.pop('input_ids' )
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase: Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCAmelCase: str = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase: Tuple = -1_0_0
__lowerCAmelCase: Union[str, Any] = tf.convert_to_tensor(UpperCAmelCase )
__lowerCAmelCase: Dict = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase: str = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase: Any = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase: Tuple = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase: Dict = inspect.signature(model.call ).parameters
__lowerCAmelCase: Dict = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase: str = {0: 'input_ids'}
for label_key in label_keys:
__lowerCAmelCase: Optional[Any] = signature_names.index(UpperCAmelCase )
__lowerCAmelCase: Tuple = label_key
__lowerCAmelCase: Tuple = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase: List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase: Optional[Any] = prepared_for_class[value]
__lowerCAmelCase: Union[str, Any] = tuple(UpperCAmelCase )
# Send to model
__lowerCAmelCase: Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCAmelCase ( self : Dict ) -> Tuple:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> int:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: Tuple = type
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : int ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Optional[int] = TFLayoutLMvaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : int ) -> Dict:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Any ) -> List[str]:
__lowerCAmelCase: Any = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCAmelCase: Tuple = self.default_image_processor
__lowerCAmelCase: str = prepare_img()
__lowerCAmelCase: Optional[int] = image_processor(images=UpperCAmelCase , return_tensors='tf' ).pixel_values
__lowerCAmelCase: Dict = tf.constant([[1, 2]] )
__lowerCAmelCase: str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase: List[str] = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
__lowerCAmelCase: Tuple = (1, 1_9_9, 7_6_8)
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase )
__lowerCAmelCase: str = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def snake_case_ (_a : List[str] ):
UpperCAmelCase = []
for line in lines:
UpperCAmelCase = re.sub(R'''#.*''' , '''''' , _a ) # remove comments
if line:
filtered_lines.append(_a )
UpperCAmelCase = '\n'.join(_a )
# Make a hash from all this code
UpperCAmelCase = full_str.encode('''utf-8''' )
return shaaaa(_a ).hexdigest()
# get importable module names and hash for caching
A ={
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
A ={
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A ={'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
A ={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 34 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : str=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=9_9 , UpperCAmelCase : Optional[int]=3_2 , UpperCAmelCase : Dict=5 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : Dict=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : int=0.02 , UpperCAmelCase : List[Any]=4 , ) -> Optional[Any]:
__lowerCAmelCase: str = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Optional[int] = seq_length
__lowerCAmelCase: Dict = is_training
__lowerCAmelCase: Optional[Any] = use_attention_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Optional[int] = use_labels
__lowerCAmelCase: Optional[Any] = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: List[str] = num_attention_heads
__lowerCAmelCase: int = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Optional[int] = max_position_embeddings
__lowerCAmelCase: Union[str, Any] = type_vocab_size
__lowerCAmelCase: int = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Optional[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Optional[int] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : Dict ) -> Any:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: Optional[Any] = config_and_inputs
__lowerCAmelCase: Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( snake_case__ , unittest.TestCase ):
_lowercase : Dict = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase: List[Any] = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
__lowerCAmelCase: Optional[Any] = model_class_name.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
@require_flax
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: List[Any] = FlaxAlbertModel.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
__lowerCAmelCase: str = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase_ :
"""simple docstring"""
def __init__( self : List[Any] ) -> List[str]:
__lowerCamelCase : Dict = {}
def _lowercase ( self : Tuple , _a : str ) -> None:
__lowerCamelCase : Optional[Any] = {}
def _lowercase ( self : Tuple , _a : str , _a : str , _a : float ) -> None:
if nodea not in self.connections:
self.add_node(_a )
if nodea not in self.connections:
self.add_node(_a )
__lowerCamelCase : List[Any] = probability
def _lowercase ( self : List[str] ) -> list[str]:
return list(self.connections )
def _lowercase ( self : Optional[int] , _a : str ) -> str:
__lowerCamelCase : int = 0
__lowerCamelCase : Tuple = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> dict[str, int]:
__lowerCamelCase : Tuple = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
__lowerCamelCase : int = Counter(graph.get_nodes() )
__lowerCamelCase : Any = start
for _ in range(_lowerCAmelCase ):
__lowerCamelCase : Tuple = graph.transition(_lowerCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 208 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class __UpperCamelCase ( snake_case__ ):
"""simple docstring"""
lowerCAmelCase_ = 'new-model'
if is_tf_available():
class __UpperCamelCase ( snake_case__ ):
"""simple docstring"""
lowerCAmelCase_ = NewModelConfig
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = 'bert-base-cased'
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = 'bert-base-cased'
__SCREAMING_SNAKE_CASE : int = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Dict = TFAutoModelForPreTraining.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : str ):
"""simple docstring"""
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelForCausalLM.from_pretrained(_A )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForCausalLM.from_pretrained(_A , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : int = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelWithLMHead.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForMaskedLM.from_pretrained(_A )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForMaskedLM.from_pretrained(_A , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(_A )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSeqaSeqLM.from_pretrained(_A , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelForSequenceClassification.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModelForQuestionAnswering.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
@slow
@require_tensorflow_probability
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
__SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelForTableQuestionAnswering.from_pretrained(_A )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelForTableQuestionAnswering.from_pretrained(
_A , output_loading_info=_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , _A )
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = TFAutoModelWithLMHead.from_pretrained(_A )
self.assertIsInstance(_A , _A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=_A ) , 1_4410 )
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = TFAutoModelWithLMHead.from_pretrained(_A )
self.assertIsInstance(_A , _A )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=_A ) , 1_4410 )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny''' )
self.assertIsInstance(_A , _A )
__SCREAMING_SNAKE_CASE : str = copy.deepcopy(model.config )
__SCREAMING_SNAKE_CASE : int = ['FunnelBaseModel']
__SCREAMING_SNAKE_CASE : Union[str, Any] = TFAutoModel.from_config(_A )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A )
__SCREAMING_SNAKE_CASE : Any = TFAutoModel.from_pretrained(_A )
self.assertIsInstance(_A , _A )
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
try:
AutoConfig.register('''new-model''' , _A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__ ):
# Wrong config class will raise an error
with self.assertRaises(_A ):
auto_class.register(_A , _A )
auto_class.register(_A , _A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
auto_class.register(_A , _A )
# Now that the config is registered, it can be used as any other config with the auto-API
__SCREAMING_SNAKE_CASE : Optional[int] = BertModelTester(self ).get_config()
__SCREAMING_SNAKE_CASE : Union[str, Any] = NewModelConfig(**tiny_config.to_dict() )
__SCREAMING_SNAKE_CASE : int = auto_class.from_config(_A )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_A )
__SCREAMING_SNAKE_CASE : str = auto_class.from_pretrained(_A )
self.assertIsInstance(_A , _A )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
with self.assertRaisesRegex(
_A , '''bert-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModel.from_pretrained('''bert-base''' )
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
with self.assertRaisesRegex(
_A , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Any = TFAutoModel.from_pretrained(_A , revision='''aaaaaa''' )
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
with self.assertRaisesRegex(
_A , '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin''' , ):
__SCREAMING_SNAKE_CASE : Dict = TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
with self.assertRaisesRegex(_A , '''Use `from_pt=True` to load this model''' ):
__SCREAMING_SNAKE_CASE : int = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
# With a sharded checkpoint
__SCREAMING_SNAKE_CASE : str = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
with RequestCounter() as counter:
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 303 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_a = [None] * 1_0_0_0_0_0_0_0
_a = True
_a = False
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCAmelCase: int = chain(next_number(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = number_chain
while number < 10_00_00_00:
__lowerCAmelCase: Dict = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE : int = 10_00_00_00 ) -> int:
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| 322 | 0 |
"""simple docstring"""
import pytest
snake_case_ = """__dummy_dataset1__"""
snake_case_ = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
\"tokens\": datasets.Sequence(datasets.Value(\"string\")),
\"ner_tags\": datasets.Sequence(
datasets.features.ClassLabel(
names=[
\"O\",
\"B-PER\",
\"I-PER\",
\"B-ORG\",
\"I-ORG\",
\"B-LOC\",
\"I-LOC\",
]
)
),
\"langs\": datasets.Sequence(datasets.Value(\"string\")),
\"spans\": datasets.Sequence(datasets.Value(\"string\")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
]
def _generate_examples(self, filepath):
with open(filepath, \"r\", encoding=\"utf-8\") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
"""
@pytest.fixture
def _lowerCAmelCase ( ):
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def _lowerCAmelCase ( ):
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = dataset_loading_script_name
UpperCAmelCase = tmp_path / 'datasets' / script_name
script_dir.mkdir(parents=lowercase_ )
UpperCAmelCase = script_dir / F"""{script_name}.py"""
with open(lowercase_ , 'w' ) as f:
f.write(lowercase_ )
return str(lowercase_ )
| 78 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
import re
def a__ ( A_ ):
'''simple docstring'''
return [char.split() for char in re.split(R"""[^ a-z A-Z 0-9 \s]""", str_ )]
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = split_input(str_ )
return "".join(
["""""".join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def a__ ( A_, A_, A_ ):
'''simple docstring'''
try:
__magic_name__ = split_input(A_ )
if upper:
__magic_name__ = ''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__magic_name__ = ''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def a__ ( A_ ):
'''simple docstring'''
return to_simple_case(A_ )
def a__ ( A_ ):
'''simple docstring'''
try:
__magic_name__ = to_simple_case(A_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def a__ ( A_, A_ ):
'''simple docstring'''
return to_complex_case(A_, A_, """_""" )
def a__ ( A_, A_ ):
'''simple docstring'''
return to_complex_case(A_, A_, """-""" )
if __name__ == "__main__":
__import__('doctest').testmod()
| 88 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str=1_3 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : str=True , UpperCAmelCase : Any=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : int=False , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Any=9_9 , UpperCAmelCase : str=0 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : int=5 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : int=5_1_2 , UpperCAmelCase : str=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict="last" , UpperCAmelCase : int=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Union[str, Any]=0 , ) -> Dict:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Tuple = seq_length
__lowerCAmelCase: Tuple = is_training
__lowerCAmelCase: Optional[Any] = use_input_lengths
__lowerCAmelCase: List[str] = use_token_type_ids
__lowerCAmelCase: Dict = use_labels
__lowerCAmelCase: int = gelu_activation
__lowerCAmelCase: Optional[int] = sinusoidal_embeddings
__lowerCAmelCase: Tuple = causal
__lowerCAmelCase: Optional[Any] = asm
__lowerCAmelCase: int = n_langs
__lowerCAmelCase: Tuple = vocab_size
__lowerCAmelCase: List[Any] = n_special
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: int = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Dict = max_position_embeddings
__lowerCAmelCase: List[str] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: List[str] = num_labels
__lowerCAmelCase: List[str] = num_choices
__lowerCAmelCase: Optional[int] = summary_type
__lowerCAmelCase: Any = use_proj
__lowerCAmelCase: Optional[Any] = scope
__lowerCAmelCase: Dict = bos_token_id
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Any = None
if self.use_input_lengths:
__lowerCAmelCase: Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowerCAmelCase: str = None
if self.use_token_type_ids:
__lowerCAmelCase: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: Optional[int] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
__lowerCAmelCase: str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase: Dict = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: List[str] = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , ) -> int:
__lowerCAmelCase: str = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Dict , ) -> List[str]:
__lowerCAmelCase: Dict = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: str = model(UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
__lowerCAmelCase: Any = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((__lowerCAmelCase) , ): List[str] = result_with_labels.to_tuple()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((__lowerCAmelCase) , ): List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , ) -> List[Any]:
__lowerCAmelCase: Optional[Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = model(UpperCAmelCase )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
__lowerCAmelCase: List[Any] = self.num_choices
__lowerCAmelCase: Optional[Any] = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Tuple ) -> int:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCAmelCase: Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple=False ) -> Dict:
__lowerCAmelCase: Optional[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__lowerCAmelCase: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: int = XLMModelTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=3_7 )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__lowerCAmelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] ) -> int:
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : Dict=1 ) -> Dict:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: int = min_length + idx + 1
__lowerCAmelCase: Union[str, Any] = min_length + idx + 1
__lowerCAmelCase: Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: Any = min_length + idx + 1
__lowerCAmelCase: str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def UpperCAmelCase ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: List[Any] = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=UpperCAmelCase ) # the president
__lowerCAmelCase: Union[str, Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__lowerCAmelCase: str = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 322 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Sequence[float] ,_UpperCAmelCase : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Sequence[float] ,_UpperCAmelCase : float ) -> float:
_a : str =0.0
for coeff in reversed(_UpperCAmelCase ):
_a : Dict =result * x + coeff
return result
if __name__ == "__main__":
A__: List[Any] = (0.0, 0.0, 5.0, 9.3, 7.0)
A__: Union[str, Any] = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 276 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) <= 1:
return arr, 0
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE ) // 2
__lowerCAmelCase: str = arr[0:mid]
__lowerCAmelCase: int = arr[mid:]
__lowerCAmelCase , __lowerCAmelCase: List[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: int = _count_cross_inversions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: List[str] = 0
while i < len(SCREAMING_SNAKE_CASE ) and j < len(SCREAMING_SNAKE_CASE ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(SCREAMING_SNAKE_CASE ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: str = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# an empty list should also have zero inversions
__lowerCAmelCase: int = []
__lowerCAmelCase: Any = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all MVP models at https://huggingface.co/models?filter=mvp
a_ = {
'vocab_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
},
'added_tokens.json': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
},
'merges_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
},
'tokenizer_file': {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
},
}
a_ = {
'RUCAIBox/mvp': 1_0_2_4,
}
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ['input_ids', 'attention_mask']
snake_case_ = MvpTokenizer
def __init__( self : Optional[Any] , __lowercase : List[str]=None , __lowercase : Optional[int]=None , __lowercase : List[Any]=None , __lowercase : Any="replace" , __lowercase : List[Any]="<s>" , __lowercase : Dict="</s>" , __lowercase : Optional[Any]="</s>" , __lowercase : Tuple="<s>" , __lowercase : List[Any]="<unk>" , __lowercase : Union[str, Any]="<pad>" , __lowercase : List[Any]="<mask>" , __lowercase : Dict=False , __lowercase : Tuple=True , **__lowercase : Tuple , ) -> List[Any]:
super().__init__(
__lowercase , __lowercase , tokenizer_file=__lowercase , errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , trim_offsets=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , __lowercase ) != add_prefix_space:
SCREAMING_SNAKE_CASE__ : str =getattr(__lowercase , pre_tok_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : int =add_prefix_space
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pre_tok_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
SCREAMING_SNAKE_CASE__ : Dict ='post_processor'
SCREAMING_SNAKE_CASE__ : List[Any] =getattr(self.backend_tokenizer , __lowercase , __lowercase )
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE__ : int =tuple(state['''sep'''] )
if "cls" in state:
SCREAMING_SNAKE_CASE__ : str =tuple(state['''cls'''] )
SCREAMING_SNAKE_CASE__ : List[Any] =False
if state.get('''add_prefix_space''' , __lowercase ) != add_prefix_space:
SCREAMING_SNAKE_CASE__ : str =add_prefix_space
SCREAMING_SNAKE_CASE__ : Tuple =True
if state.get('''trim_offsets''' , __lowercase ) != trim_offsets:
SCREAMING_SNAKE_CASE__ : Optional[Any] =trim_offsets
SCREAMING_SNAKE_CASE__ : List[str] =True
if changes_to_apply:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =getattr(__lowercase , state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Optional[int] =component_class(**__lowercase )
setattr(self.backend_tokenizer , __lowercase , __lowercase )
@property
def __magic_name__ ( self : int ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def __magic_name__ ( self : Dict , __lowercase : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : List[str] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else value
SCREAMING_SNAKE_CASE__ : str =value
def __magic_name__ ( self : Optional[Any] , *__lowercase : Any , **__lowercase : int ) -> BatchEncoding:
SCREAMING_SNAKE_CASE__ : List[Any] =kwargs.get('''is_split_into_words''' , __lowercase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*__lowercase , **__lowercase )
def __magic_name__ ( self : Dict , *__lowercase : int , **__lowercase : Any ) -> BatchEncoding:
SCREAMING_SNAKE_CASE__ : Any =kwargs.get('''is_split_into_words''' , __lowercase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*__lowercase , **__lowercase )
def __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : str =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase )
def __magic_name__ ( self : Dict , __lowercase : List[str] , __lowercase : Optional[int]=None ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __magic_name__ ( self : Optional[int] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : Tuple =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] | 152 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 0 |
'''simple docstring'''
import math
import qiskit
def __UpperCamelCase ( UpperCAmelCase = 1 , UpperCAmelCase = 1 , UpperCAmelCase = 1 ):
if (
isinstance(UpperCAmelCase , UpperCAmelCase )
or isinstance(UpperCAmelCase , UpperCAmelCase )
or isinstance(UpperCAmelCase , UpperCAmelCase )
):
raise TypeError('''inputs must be integers.''' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''' )
if (
(math.floor(UpperCAmelCase ) != input_a)
or (math.floor(UpperCAmelCase ) != input_a)
or (math.floor(UpperCAmelCase ) != carry_in)
):
raise ValueError('''inputs must be exact integers.''' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''' )
# build registers
lowercase__ : Union[str, Any] = qiskit.QuantumRegister(4 , '''qr''' )
lowercase__ : List[Any] = qiskit.ClassicalRegister(2 , '''cr''' )
# list the entries
lowercase__ : Any = [input_a, input_a, carry_in]
lowercase__ : List[str] = qiskit.QuantumCircuit(UpperCAmelCase , UpperCAmelCase )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(UpperCAmelCase ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(UpperCAmelCase ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(UpperCAmelCase ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , UpperCAmelCase ) # measure the last two qbits
lowercase__ : List[str] = qiskit.Aer.get_backend('''aer_simulator''' )
lowercase__ : List[Any] = qiskit.execute(UpperCAmelCase , UpperCAmelCase , shots=1000 )
return job.result().get_counts(UpperCAmelCase )
if __name__ == "__main__":
print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')
| 198 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 0 |
import gc
import threading
import time
import psutil
import torch
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> Tuple:
lowerCamelCase_ = psutil.Process()
lowerCamelCase_ = False
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = -1
while True:
lowerCamelCase_ = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = True
lowerCamelCase_ = threading.Thread(target=self.peak_monitor )
lowerCamelCase_ = True
self.thread.start()
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = False
self.thread.join()
return self.cpu_memory_peak
__A =PeakCPUMemory()
def lowerCamelCase_ ( ):
lowerCamelCase_ = {'time': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCamelCase_ = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
lowerCamelCase_ = torch.cuda.memory_allocated(lowerCamelCase__ )
torch.cuda.reset_peak_memory_stats()
return measures
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = {'time': time.time() - start_measures['time']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCamelCase_ = (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
lowerCamelCase_ = (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0
# GPU mem
for i in range(torch.cuda.device_count() ):
lowerCamelCase_ = (torch.cuda.memory_allocated(lowerCamelCase__ ) - start_measures[str(lowerCamelCase__ )]) / 2**2_0
lowerCamelCase_ = (torch.cuda.max_memory_allocated(lowerCamelCase__ ) - start_measures[str(lowerCamelCase__ )]) / 2**2_0
return measures
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
print(F'{description}:' )
print(F'- Time: {measures["time"]:.2f}s' )
for i in range(torch.cuda.device_count() ):
print(F'- GPU {i} allocated: {measures[str(lowerCamelCase__ )]:.2f}MiB' )
lowerCamelCase_ = measures[F'{i}-peak']
print(F'- GPU {i} peak: {peak:.2f}MiB' )
print(F'- CPU RAM allocated: {measures["cpu"]:.2f}MiB' )
print(F'- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB' )
| 19 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.environ['GITHUB_TOKEN'] )
__lowerCAmelCase: Tuple = g.get_repo('huggingface/accelerate' )
__lowerCAmelCase: str = repo.get_issues(state='open' )
for issue in open_issues:
__lowerCAmelCase: Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda SCREAMING_SNAKE_CASE : i.created_at , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = comments[0] if len(SCREAMING_SNAKE_CASE ) > 0 else None
__lowerCAmelCase: Tuple = dt.utcnow()
__lowerCAmelCase: Optional[int] = (current_time - issue.updated_at).days
__lowerCAmelCase: str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 322 | 0 |
def lowerCAmelCase ( _lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowerCAmelCase )
if number < 0:
return False
UpperCAmelCase__ = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 169 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 322 | 0 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__lowerCamelCase ) ):
if indegree[i] == 0:
queue.append(__lowerCamelCase )
while queue:
SCREAMING_SNAKE_CASE_ = queue.pop(0 )
cnt += 1
topo.append(__lowerCamelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(__lowerCamelCase )
if cnt != len(__lowerCamelCase ):
print('''Cycle exists''' )
else:
print(__lowerCamelCase )
# Adjacency List of Graph
__UpperCAmelCase = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 299 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class A_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Tuple , UpperCAmelCase : float , UpperCAmelCase : Callable , UpperCAmelCase : int , UpperCAmelCase : float = 1.0 , UpperCAmelCase : str = None , ) -> Union[str, Any]:
super().__init__()
__lowerCAmelCase: Optional[Any] = initial_learning_rate
__lowerCAmelCase: str = warmup_steps
__lowerCAmelCase: Optional[int] = power
__lowerCAmelCase: str = decay_schedule_fn
__lowerCAmelCase: Tuple = name
def __call__( self : int , UpperCAmelCase : Dict ) -> Optional[int]:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
__lowerCAmelCase: List[str] = tf.cast(UpperCAmelCase , tf.floataa )
__lowerCAmelCase: Tuple = tf.cast(self.warmup_steps , tf.floataa )
__lowerCAmelCase: List[str] = global_step_float / warmup_steps_float
__lowerCAmelCase: List[str] = self.initial_learning_rate * tf.math.pow(UpperCAmelCase , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=UpperCAmelCase , )
def UpperCAmelCase ( self : Tuple ) -> int:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def _a ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 0.9 , SCREAMING_SNAKE_CASE : float = 0.9_9_9 , SCREAMING_SNAKE_CASE : float = 1E-8 , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : Optional[float] = None , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 , SCREAMING_SNAKE_CASE : Optional[List[str]] = None , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=SCREAMING_SNAKE_CASE , )
if num_warmup_steps:
__lowerCAmelCase: Optional[int] = WarmUp(
initial_learning_rate=SCREAMING_SNAKE_CASE , decay_schedule_fn=SCREAMING_SNAKE_CASE , warmup_steps=SCREAMING_SNAKE_CASE , )
if weight_decay_rate > 0.0:
__lowerCAmelCase: List[Any] = AdamWeightDecay(
learning_rate=SCREAMING_SNAKE_CASE , weight_decay_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase: Dict = tf.keras.optimizers.Adam(
learning_rate=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , beta_a=SCREAMING_SNAKE_CASE , epsilon=SCREAMING_SNAKE_CASE , clipnorm=SCREAMING_SNAKE_CASE , global_clipnorm=SCREAMING_SNAKE_CASE , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , UpperCAmelCase : float = 0.9 , UpperCAmelCase : float = 0.999 , UpperCAmelCase : float = 1E-7 , UpperCAmelCase : bool = False , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : Optional[List[str]] = None , UpperCAmelCase : str = "AdamWeightDecay" , **UpperCAmelCase : str , ) -> int:
super().__init__(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
__lowerCAmelCase: List[Any] = weight_decay_rate
__lowerCAmelCase: List[str] = include_in_weight_decay
__lowerCAmelCase: Optional[Any] = exclude_from_weight_decay
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Tuple ) -> Optional[int]:
__lowerCAmelCase: Union[str, Any] = {'WarmUp': WarmUp}
return super(UpperCAmelCase , cls ).from_config(UpperCAmelCase , custom_objects=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : int , UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
super(UpperCAmelCase , self )._prepare_local(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> List[str]:
__lowerCAmelCase: Dict = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None , **UpperCAmelCase : Optional[Any] ) -> Union[str, Any]:
__lowerCAmelCase , __lowerCAmelCase: Tuple = list(zip(*UpperCAmelCase ) )
return super(UpperCAmelCase , self ).apply_gradients(zip(UpperCAmelCase , UpperCAmelCase ) , name=UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : str , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any ) -> str:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
__lowerCAmelCase: Dict = apply_state or {}
__lowerCAmelCase: Union[str, Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
__lowerCAmelCase: str = self._fallback_apply_state(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Tuple = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any]=None ) -> List[Any]:
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_dense(UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[Any]=None ) -> List[str]:
__lowerCAmelCase , __lowerCAmelCase: Any = self._get_lr(var.device , var.dtype.base_dtype , UpperCAmelCase )
__lowerCAmelCase: str = self._decay_weights_op(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
with tf.control_dependencies([decay] ):
return super(UpperCAmelCase , self )._resource_apply_sparse(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase: List[str] = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(UpperCAmelCase , UpperCAmelCase ) is not None:
return False
return True
class A_ ( snake_case__ ):
def __init__( self : int ) -> List[Any]:
__lowerCAmelCase: Tuple = []
__lowerCAmelCase: int = None
@property
def UpperCAmelCase ( self : Dict ) -> List[Any]:
if self._accum_steps is None:
__lowerCAmelCase: List[Any] = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Optional[Any] , UpperCAmelCase : Any ) -> Any:
if not self._gradients:
__lowerCAmelCase: Any = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(UpperCAmelCase ) , trainable=UpperCAmelCase , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(UpperCAmelCase ) != len(self._gradients ):
raise ValueError(F'''Expected {len(self._gradients )} gradients, but got {len(UpperCAmelCase )}''' )
for accum_gradient, gradient in zip(self._gradients , UpperCAmelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(UpperCAmelCase )
self._accum_steps.assign_add(1 )
def UpperCAmelCase ( self : int ) -> int:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(UpperCAmelCase ) )
| 322 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case_ (_a : dict , _a : str , _a : set , _a : set , _a : dict , _a : dict , _a : PriorityQueue , _a : dict , _a : float | int , ):
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
UpperCAmelCase = cst_fwd.get(_a , np.inf )
UpperCAmelCase = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
UpperCAmelCase = new_cost_f
UpperCAmelCase = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
UpperCAmelCase = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def snake_case_ (_a : str , _a : str , _a : dict , _a : dict ):
UpperCAmelCase = -1
UpperCAmelCase = set()
UpperCAmelCase = set()
UpperCAmelCase = {source: 0}
UpperCAmelCase = {destination: 0}
UpperCAmelCase = {source: None}
UpperCAmelCase = {destination: None}
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = PriorityQueue()
UpperCAmelCase = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
UpperCAmelCase = queue_forward.get()
visited_forward.add(_a )
UpperCAmelCase = queue_backward.get()
visited_backward.add(_a )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
UpperCAmelCase = pass_and_relaxation(
_a , _a , _a , _a , _a , _a , _a , _a , _a , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
UpperCAmelCase = shortest_distance
return shortest_path_distance
A ={
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
A ={
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 34 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any]=[] ) -> str:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = size[0] - overlap_pixels * 2
__lowerCAmelCase: str = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__lowerCAmelCase: Any = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_55
__lowerCAmelCase: int = np.pad(SCREAMING_SNAKE_CASE , mode='linear_ramp' , pad_width=SCREAMING_SNAKE_CASE , end_values=0 )
if "l" in remove_borders:
__lowerCAmelCase: Dict = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__lowerCAmelCase: Tuple = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__lowerCAmelCase: List[Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__lowerCAmelCase: List[str] = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : List[str] ) -> List[Any]:
"""simple docstring"""
return max(SCREAMING_SNAKE_CASE , min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : [int] ) -> int:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( SCREAMING_SNAKE_CASE : [int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : [int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Tuple = list(SCREAMING_SNAKE_CASE )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__lowerCAmelCase: int = clamp_rect(SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCAmelCase: List[Any] = Image.new('RGB' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(SCREAMING_SNAKE_CASE , (original_slice, 0) )
return result
def _a ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__lowerCAmelCase: List[Any] = tile.crop(SCREAMING_SNAKE_CASE )
return tile
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = n % d
return n - divisor
class A_ ( snake_case__ ):
def __init__( self : Optional[Any] , UpperCAmelCase : AutoencoderKL , UpperCAmelCase : CLIPTextModel , UpperCAmelCase : CLIPTokenizer , UpperCAmelCase : UNetaDConditionModel , UpperCAmelCase : DDPMScheduler , UpperCAmelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , UpperCAmelCase : int = 3_5_0 , ) -> Optional[Any]:
super().__init__(
vae=UpperCAmelCase , text_encoder=UpperCAmelCase , tokenizer=UpperCAmelCase , unet=UpperCAmelCase , low_res_scheduler=UpperCAmelCase , scheduler=UpperCAmelCase , max_noise_level=UpperCAmelCase , )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , **UpperCAmelCase : List[Any] ) -> Optional[int]:
torch.manual_seed(0 )
__lowerCAmelCase: Optional[int] = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__lowerCAmelCase: Optional[Any] = add_overlap_rect(UpperCAmelCase , UpperCAmelCase , image.size )
__lowerCAmelCase: Any = image.crop(UpperCAmelCase )
__lowerCAmelCase: Any = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__lowerCAmelCase: Tuple = translated_slice_x - (original_image_slice / 2)
__lowerCAmelCase: Union[str, Any] = max(0 , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = squeeze_tile(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = to_input.size
__lowerCAmelCase: List[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__lowerCAmelCase: int = super(UpperCAmelCase , self ).__call__(image=UpperCAmelCase , **UpperCAmelCase ).images[0]
__lowerCAmelCase: Dict = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Union[str, Any] = unsqueeze_tile(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Optional[int] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__lowerCAmelCase: Optional[int] = []
if x == 0:
remove_borders.append('l' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('r' )
if y == 0:
remove_borders.append('t' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('b' )
__lowerCAmelCase: int = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=UpperCAmelCase ) , mode='L' , )
final_image.paste(
UpperCAmelCase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , UpperCAmelCase )
@torch.no_grad()
def __call__( self : Optional[Any] , UpperCAmelCase : Union[str, List[str]] , UpperCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , UpperCAmelCase : int = 7_5 , UpperCAmelCase : float = 9.0 , UpperCAmelCase : int = 5_0 , UpperCAmelCase : Optional[Union[str, List[str]]] = None , UpperCAmelCase : Optional[int] = 1 , UpperCAmelCase : float = 0.0 , UpperCAmelCase : Optional[torch.Generator] = None , UpperCAmelCase : Optional[torch.FloatTensor] = None , UpperCAmelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 1_2_8 , UpperCAmelCase : int = 3_2 , UpperCAmelCase : int = 3_2 , ) -> str:
__lowerCAmelCase: List[Any] = Image.new('RGB' , (image.size[0] * 4, image.size[1] * 4) )
__lowerCAmelCase: str = math.ceil(image.size[0] / tile_size )
__lowerCAmelCase: List[Any] = math.ceil(image.size[1] / tile_size )
__lowerCAmelCase: Optional[Any] = tcx * tcy
__lowerCAmelCase: Tuple = 0
for y in range(UpperCAmelCase ):
for x in range(UpperCAmelCase ):
self._process_tile(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , prompt=UpperCAmelCase , num_inference_steps=UpperCAmelCase , guidance_scale=UpperCAmelCase , noise_level=UpperCAmelCase , negative_prompt=UpperCAmelCase , num_images_per_prompt=UpperCAmelCase , eta=UpperCAmelCase , generator=UpperCAmelCase , latents=UpperCAmelCase , )
current_count += 1
if callback is not None:
callback({'progress': current_count / total_tile_count, 'image': final_image} )
return final_image
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: Any = 'stabilityai/stable-diffusion-x4-upscaler'
__lowerCAmelCase: Dict = StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE , revision='fp16' , torch_dtype=torch.floataa )
__lowerCAmelCase: Optional[Any] = pipe.to('cuda' )
__lowerCAmelCase: Tuple = Image.open('../../docs/source/imgs/diffusers_library.jpg' )
def callback(SCREAMING_SNAKE_CASE : Tuple ):
print(f'''progress: {obj['progress']:.4f}''' )
obj["image"].save('diffusers_library_progress.jpg' )
__lowerCAmelCase: str = pipe(image=SCREAMING_SNAKE_CASE , prompt='Black font, white background, vector' , noise_level=40 , callback=SCREAMING_SNAKE_CASE )
final_image.save('diffusers_library.jpg' )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> Dict:
if index == r:
for j in range(_lowerCAmelCase ):
print(data[j] ,end=' ' )
print(' ' )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__lowerCamelCase : Tuple = arr[i]
combination_util(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,index + 1 ,_lowerCAmelCase ,i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> Optional[Any]:
__lowerCamelCase : Union[str, Any] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,0 ,_lowerCAmelCase ,0 )
if __name__ == "__main__":
# Driver code to check the function above
_UpperCamelCase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 208 |
def _a ( SCREAMING_SNAKE_CASE : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: List[Any] = sum(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
__lowerCAmelCase: Tuple = True
for i in range(1 , s + 1 ):
__lowerCAmelCase: Any = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
__lowerCAmelCase: Optional[int] = dp[i][j - 1]
if arr[i - 1] <= j:
__lowerCAmelCase: Union[str, Any] = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
__lowerCAmelCase: Tuple = s - 2 * j
break
return diff
| 322 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
lowercase_ = 8
def a__ ( snake_case , snake_case=BITS ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = x.device
__SCREAMING_SNAKE_CASE : List[Any] = (x * 255).int().clamp(0 , 255 )
__SCREAMING_SNAKE_CASE : Dict = 2 ** torch.arange(bits - 1 , -1 , -1 , device=snake_case )
__SCREAMING_SNAKE_CASE : Union[str, Any] = rearrange(snake_case , '''d -> d 1 1''' )
__SCREAMING_SNAKE_CASE : Any = rearrange(snake_case , '''b c h w -> b c 1 h w''' )
__SCREAMING_SNAKE_CASE : List[Any] = ((x & mask) != 0).float()
__SCREAMING_SNAKE_CASE : Any = rearrange(snake_case , '''b c d h w -> b (c d) h w''' )
__SCREAMING_SNAKE_CASE : str = bits * 2 - 1
return bits
def a__ ( snake_case , snake_case=BITS ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = x.device
__SCREAMING_SNAKE_CASE : List[str] = (x > 0).int()
__SCREAMING_SNAKE_CASE : List[str] = 2 ** torch.arange(bits - 1 , -1 , -1 , device=snake_case , dtype=torch.intaa )
__SCREAMING_SNAKE_CASE : Union[str, Any] = rearrange(snake_case , '''d -> d 1 1''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = rearrange(snake_case , '''b (c d) h w -> b c d h w''' , d=8 )
__SCREAMING_SNAKE_CASE : Optional[Any] = reduce(x * mask , '''b c d h w -> b c h w''' , '''sum''' )
return (dec / 255).clamp(0.0 , 1.0 )
def a__ ( self , snake_case , snake_case , snake_case , snake_case = 0.0 , snake_case = True , snake_case=None , snake_case = True , ):
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
__SCREAMING_SNAKE_CASE : List[Any] = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
__SCREAMING_SNAKE_CASE : Optional[Any] = self.alphas_cumprod[timestep]
__SCREAMING_SNAKE_CASE : int = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__SCREAMING_SNAKE_CASE : int = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
__SCREAMING_SNAKE_CASE : List[str] = self.bit_scale
if self.config.clip_sample:
__SCREAMING_SNAKE_CASE : Tuple = torch.clamp(snake_case , -scale , snake_case )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._get_variance(snake_case , snake_case )
__SCREAMING_SNAKE_CASE : str = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
__SCREAMING_SNAKE_CASE : str = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__SCREAMING_SNAKE_CASE : Optional[Any] = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__SCREAMING_SNAKE_CASE : Tuple = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
__SCREAMING_SNAKE_CASE : Optional[int] = model_output.device if torch.is_tensor(snake_case ) else 'cpu'
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.randn(model_output.shape , dtype=model_output.dtype , generator=snake_case ).to(snake_case )
__SCREAMING_SNAKE_CASE : Dict = self._get_variance(snake_case , snake_case ) ** 0.5 * eta * noise
__SCREAMING_SNAKE_CASE : Optional[Any] = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=snake_case , pred_original_sample=snake_case )
def a__ ( self , snake_case , snake_case , snake_case , snake_case="epsilon" , snake_case=None , snake_case = True , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.split(snake_case , sample.shape[1] , dim=1 )
else:
__SCREAMING_SNAKE_CASE : Any = None
# 1. compute alphas, betas
__SCREAMING_SNAKE_CASE : List[str] = self.alphas_cumprod[t]
__SCREAMING_SNAKE_CASE : Optional[int] = self.alphas_cumprod[t - 1] if t > 0 else self.one
__SCREAMING_SNAKE_CASE : Optional[Any] = 1 - alpha_prod_t
__SCREAMING_SNAKE_CASE : List[Any] = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
__SCREAMING_SNAKE_CASE : Tuple = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
__SCREAMING_SNAKE_CASE : Optional[Any] = model_output
else:
raise ValueError(F'''Unsupported prediction_type {prediction_type}.''' )
# 3. Clip "predicted x_0"
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.bit_scale
if self.config.clip_sample:
__SCREAMING_SNAKE_CASE : int = torch.clamp(snake_case , -scale , snake_case )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE : Optional[int] = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
__SCREAMING_SNAKE_CASE : Optional[Any] = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE : int = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
if t > 0:
__SCREAMING_SNAKE_CASE : List[Any] = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=snake_case ).to(model_output.device )
__SCREAMING_SNAKE_CASE : Optional[Any] = (self._get_variance(snake_case , predicted_variance=snake_case ) ** 0.5) * noise
__SCREAMING_SNAKE_CASE : Optional[int] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=snake_case , pred_original_sample=snake_case )
class __UpperCamelCase ( snake_case__ ):
"""simple docstring"""
def __init__( self : Optional[int] , _A : UNetaDConditionModel , _A : Union[DDIMScheduler, DDPMScheduler] , _A : Optional[float] = 1.0 , ):
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE : List[str] = bit_scale
__SCREAMING_SNAKE_CASE : str = (
ddim_bit_scheduler_step if isinstance(_A , _A ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=_A , scheduler=_A )
@torch.no_grad()
def __call__( self : int , _A : Optional[int] = 256 , _A : Optional[int] = 256 , _A : Optional[int] = 50 , _A : Optional[torch.Generator] = None , _A : Optional[int] = 1 , _A : Optional[str] = "pil" , _A : bool = True , **_A : str , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=_A , )
__SCREAMING_SNAKE_CASE : str = decimal_to_bits(_A ) * self.bit_scale
__SCREAMING_SNAKE_CASE : str = latents.to(self.device )
self.scheduler.set_timesteps(_A )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
__SCREAMING_SNAKE_CASE : Any = self.unet(_A , _A ).sample
# compute the previous noisy sample x_t -> x_t-1
__SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.step(_A , _A , _A ).prev_sample
__SCREAMING_SNAKE_CASE : Tuple = bits_to_decimal(_A )
if output_type == "pil":
__SCREAMING_SNAKE_CASE : Optional[int] = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A )
| 303 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
__lowerCAmelCase: int = 0
__lowerCAmelCase: Tuple = len(SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__lowerCAmelCase: Tuple = i + 1
else:
__lowerCAmelCase: List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{two_pointer([2, 7, 1_1, 1_5], 9) = }")
| 322 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A_ ( snake_case__ ):
"""simple docstring"""
__UpperCamelCase = 'Wav2Vec2FeatureExtractor'
__UpperCamelCase = 'AutoTokenizer'
def __init__( self :Tuple , lowercase_ :Any , lowercase_ :List[Any] ) -> Optional[int]:
super().__init__(lowercase_ , lowercase_ )
UpperCAmelCase = self.feature_extractor
UpperCAmelCase = False
@classmethod
def UpperCAmelCase__ ( cls :Optional[Any] , lowercase_ :Optional[Any] , **lowercase_ :Optional[int] ) -> List[Any]:
try:
return super().from_pretrained(lowercase_ , **lowercase_ )
except OSError:
warnings.warn(
f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ' , lowercase_ , )
UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase = WavaVecaCTCTokenizer.from_pretrained(lowercase_ , **lowercase_ )
return cls(feature_extractor=lowercase_ , tokenizer=lowercase_ )
def __call__( self :Union[str, Any] , *lowercase_ :Tuple , **lowercase_ :str ) -> Tuple:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*lowercase_ , **lowercase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
UpperCAmelCase = kwargs.pop('raw_speech' )
else:
UpperCAmelCase = kwargs.pop('audio' , lowercase_ )
UpperCAmelCase = kwargs.pop('sampling_rate' , lowercase_ )
UpperCAmelCase = kwargs.pop('text' , lowercase_ )
if len(lowercase_ ) > 0:
UpperCAmelCase = args[0]
UpperCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
UpperCAmelCase = self.feature_extractor(lowercase_ , *lowercase_ , sampling_rate=lowercase_ , **lowercase_ )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase_ , **lowercase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
UpperCAmelCase = encodings['input_ids']
return inputs
def UpperCAmelCase__ ( self :Any , *lowercase_ :Tuple , **lowercase_ :Dict ) -> str:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*lowercase_ , **lowercase_ )
UpperCAmelCase = kwargs.pop('input_features' , lowercase_ )
UpperCAmelCase = kwargs.pop('labels' , lowercase_ )
if len(lowercase_ ) > 0:
UpperCAmelCase = args[0]
UpperCAmelCase = args[1:]
if input_features is not None:
UpperCAmelCase = self.feature_extractor.pad(lowercase_ , *lowercase_ , **lowercase_ )
if labels is not None:
UpperCAmelCase = self.tokenizer.pad(lowercase_ , **lowercase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
UpperCAmelCase = labels['input_ids']
return input_features
def UpperCAmelCase__ ( self :int , *lowercase_ :int , **lowercase_ :Union[str, Any] ) -> str:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def UpperCAmelCase__ ( self :List[Any] , *lowercase_ :str , **lowercase_ :str ) -> Any:
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@contextmanager
def UpperCAmelCase__ ( self :Dict ) -> Union[str, Any]:
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
UpperCAmelCase = True
UpperCAmelCase = self.tokenizer
yield
UpperCAmelCase = self.feature_extractor
UpperCAmelCase = False
| 78 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_a = '''scheduler_config.json'''
class A_ ( snake_case__ ):
_lowercase : Optional[Any] = 1
_lowercase : Tuple = 2
_lowercase : Dict = 3
_lowercase : int = 4
_lowercase : Optional[Any] = 5
@dataclass
class A_ ( snake_case__ ):
_lowercase : jnp.ndarray
class A_ :
_lowercase : Optional[int] = SCHEDULER_CONFIG_NAME
_lowercase : Dict = ['dtype']
_lowercase : int = []
_lowercase : Union[str, Any] = True
@classmethod
def UpperCAmelCase ( cls : Union[str, Any] , UpperCAmelCase : Dict[str, Any] = None , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : List[str]=False , **UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.load_config(
pretrained_model_name_or_path=UpperCAmelCase , subfolder=UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase , )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = cls.from_config(UpperCAmelCase , return_unused_kwargs=UpperCAmelCase , **UpperCAmelCase )
if hasattr(UpperCAmelCase , 'create_state' ) and getattr(UpperCAmelCase , 'has_state' , UpperCAmelCase ):
__lowerCAmelCase: Dict = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, os.PathLike] , UpperCAmelCase : bool = False , **UpperCAmelCase : Any ) -> List[str]:
self.save_config(save_directory=UpperCAmelCase , push_to_hub=UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : str ) -> Dict:
return self._get_compatibles()
@classmethod
def UpperCAmelCase ( cls : Optional[int] ) -> Any:
__lowerCAmelCase: Optional[int] = list(set([cls.__name__] + cls._compatibles ) )
__lowerCAmelCase: Dict = importlib.import_module(__name__.split('.' )[0] )
__lowerCAmelCase: Dict = [
getattr(UpperCAmelCase , UpperCAmelCase ) for c in compatible_classes_str if hasattr(UpperCAmelCase , UpperCAmelCase )
]
return compatible_classes
def _a ( SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : Tuple[int] ) -> jnp.ndarray:
"""simple docstring"""
assert len(SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE ) - x.ndim) ) , SCREAMING_SNAKE_CASE )
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any=0.9_9_9 , SCREAMING_SNAKE_CASE : List[Any]=jnp.floataa ) -> jnp.ndarray:
"""simple docstring"""
def alpha_bar(SCREAMING_SNAKE_CASE : str ):
return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
__lowerCAmelCase: str = []
for i in range(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: Union[str, Any] = i / num_diffusion_timesteps
__lowerCAmelCase: List[str] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE ) / alpha_bar(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE ) )
return jnp.array(SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class A_ :
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
_lowercase : jnp.ndarray
@classmethod
def UpperCAmelCase ( cls : str , UpperCAmelCase : Optional[int] ) -> Any:
__lowerCAmelCase: str = scheduler.config
if config.trained_betas is not None:
__lowerCAmelCase: Tuple = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
__lowerCAmelCase: Any = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCAmelCase: List[Any] = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCAmelCase: str = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )
__lowerCAmelCase: Optional[Any] = 1.0 - betas
__lowerCAmelCase: Optional[Any] = jnp.cumprod(UpperCAmelCase , axis=0 )
return cls(
alphas=UpperCAmelCase , betas=UpperCAmelCase , alphas_cumprod=UpperCAmelCase , )
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = state.alphas_cumprod
__lowerCAmelCase: str = alphas_cumprod[timesteps] ** 0.5
__lowerCAmelCase: Any = sqrt_alpha_prod.flatten()
__lowerCAmelCase: Any = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
__lowerCAmelCase: Any = (1 - alphas_cumprod[timesteps]) ** 0.5
__lowerCAmelCase: str = sqrt_one_minus_alpha_prod.flatten()
__lowerCAmelCase: str = broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> str:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Union[str, Any] = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def _a ( SCREAMING_SNAKE_CASE : CommonSchedulerState , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray , SCREAMING_SNAKE_CASE : jnp.ndarray ) -> Any:
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase: Tuple = get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 322 | 0 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
__lowerCAmelCase : Any = logging.get_logger(__name__)
class UpperCAmelCase_ ( snake_case__ ):
'''simple docstring'''
a__ = ['input_features', 'attention_mask']
def __init__( self : str , UpperCamelCase__ : List[Any]=80 , UpperCamelCase__ : List[Any]=1_6000 , UpperCamelCase__ : Dict=0.0 , UpperCamelCase__ : Optional[int]=10 , UpperCamelCase__ : str=25 , UpperCamelCase__ : Dict="hamming_window" , UpperCamelCase__ : Optional[int]=32768.0 , UpperCamelCase__ : Tuple=0.97 , UpperCamelCase__ : Any=1.0 , UpperCamelCase__ : Any=True , UpperCamelCase__ : int=True , UpperCamelCase__ : List[str]=False , **UpperCamelCase__ : List[str] , ) -> int:
"""simple docstring"""
super().__init__(feature_size=UpperCamelCase__ , sampling_rate=UpperCamelCase__ , padding_value=UpperCamelCase__ , **UpperCamelCase__ )
__magic_name__ = feature_size
__magic_name__ = sampling_rate
__magic_name__ = padding_value
__magic_name__ = hop_length
__magic_name__ = win_length
__magic_name__ = frame_signal_scale
__magic_name__ = preemphasis_coeff
__magic_name__ = mel_floor
__magic_name__ = normalize_means
__magic_name__ = normalize_vars
__magic_name__ = win_function
__magic_name__ = return_attention_mask
__magic_name__ = win_length * sampling_rate // 1000
__magic_name__ = hop_length * sampling_rate // 1000
__magic_name__ = optimal_fft_length(self.sample_size )
__magic_name__ = (self.n_fft // 2) + 1
def _lowercase ( self : int , UpperCamelCase__ : np.array ) -> np.ndarray:
"""simple docstring"""
if self.win_function == "hamming_window":
__magic_name__ = window_function(window_length=self.sample_size , name=self.win_function , periodic=UpperCamelCase__ )
else:
__magic_name__ = window_function(window_length=self.sample_size , name=self.win_function )
__magic_name__ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
__magic_name__ = spectrogram(
one_waveform * self.frame_signal_scale , window=UpperCamelCase__ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=UpperCamelCase__ , preemphasis=self.preemphasis_coeff , mel_filters=UpperCamelCase__ , mel_floor=self.mel_floor , log_mel="""log""" , )
return msfc_features.T
def _lowercase ( self : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict ) -> int:
"""simple docstring"""
if self.normalize_means:
__magic_name__ = x[:input_length].mean(axis=0 )
__magic_name__ = np.subtract(UpperCamelCase__ , UpperCamelCase__ )
if self.normalize_vars:
__magic_name__ = x[:input_length].std(axis=0 )
__magic_name__ = np.divide(UpperCamelCase__ , UpperCamelCase__ )
if input_length < x.shape[0]:
__magic_name__ = padding_value
# make sure array is in float32
__magic_name__ = x.astype(np.floataa )
return x
def _lowercase ( self : List[str] , UpperCamelCase__ : List[np.ndarray] , UpperCamelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
"""simple docstring"""
__magic_name__ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(UpperCamelCase__ , UpperCamelCase__ , self.padding_value ) for x, n in zip(UpperCamelCase__ , UpperCamelCase__ )]
def __call__( self : str , UpperCamelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase__ : Union[bool, str, PaddingStrategy] = False , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : bool = False , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : Optional[int] = None , **UpperCamelCase__ : Any , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
F''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"""It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
__magic_name__ = isinstance(UpperCamelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
__magic_name__ = is_batched_numpy or (
isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__magic_name__ = [np.asarray(UpperCamelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase__ , np.ndarray ):
__magic_name__ = np.asarray(UpperCamelCase__ , dtype=np.floataa )
elif isinstance(UpperCamelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__magic_name__ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__magic_name__ = [raw_speech]
# extract fbank features
__magic_name__ = [self._extract_mfsc_features(UpperCamelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
__magic_name__ = BatchFeature({"""input_features""": features} )
__magic_name__ = self.pad(
UpperCamelCase__ , padding=UpperCamelCase__ , max_length=UpperCamelCase__ , truncation=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , **UpperCamelCase__ , )
# make sure list is in array format
__magic_name__ = padded_inputs.get("""input_features""" )
if isinstance(input_features[0] , UpperCamelCase__ ):
__magic_name__ = [np.asarray(UpperCamelCase__ , dtype=np.floataa ) for feature in input_features]
__magic_name__ = padded_inputs.get("""attention_mask""" )
if attention_mask is not None:
__magic_name__ = [np.asarray(UpperCamelCase__ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
__magic_name__ = (
np.array(UpperCamelCase__ , dtype=np.intaa )
if self._get_padding_strategies(UpperCamelCase__ , max_length=UpperCamelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
__magic_name__ = self.normalize(
padded_inputs["""input_features"""] , attention_mask=UpperCamelCase__ )
if return_tensors is not None:
__magic_name__ = padded_inputs.convert_to_tensors(UpperCamelCase__ )
return padded_inputs
| 88 |
_a = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ) -> list[str]:
"""simple docstring"""
__lowerCAmelCase: int = set()
# keep track of all the paths to be checked
__lowerCAmelCase: str = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowerCAmelCase: str = queue.pop(0 )
# get the last node from the path
__lowerCAmelCase: Union[str, Any] = path[-1]
if node not in explored:
__lowerCAmelCase: Dict = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowerCAmelCase: Dict = list(SCREAMING_SNAKE_CASE )
new_path.append(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(SCREAMING_SNAKE_CASE )
# in case there's no path between the 2 nodes
return []
def _a ( SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Any ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowerCAmelCase: Optional[int] = [start]
__lowerCAmelCase: Dict = set(SCREAMING_SNAKE_CASE )
# Keep tab on distances from `start` node.
__lowerCAmelCase: Optional[int] = {start: 0, target: -1}
while queue:
__lowerCAmelCase: Any = queue.pop(0 )
if node == target:
__lowerCAmelCase: Optional[int] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(SCREAMING_SNAKE_CASE )
queue.append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 322 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
A__: str = logging.get_logger(__name__)
# General docstring
A__: Any = '''RegNetConfig'''
# Base docstring
A__: int = '''facebook/regnet-y-040'''
A__: int = [1, 1088, 7, 7]
# Image classification docstring
A__: str = '''facebook/regnet-y-040'''
A__: Union[str, Any] = '''tabby, tabby cat'''
A__: List[str] = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class A__ ( tf.keras.layers.Layer ):
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int = 3 , SCREAMING_SNAKE_CASE :int = 1 , SCREAMING_SNAKE_CASE :int = 1 , SCREAMING_SNAKE_CASE :Optional[str] = "relu" , **SCREAMING_SNAKE_CASE :Tuple , ) -> List[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
_a : List[str] =tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
_a : List[str] =tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE , kernel_size=SCREAMING_SNAKE_CASE , strides=SCREAMING_SNAKE_CASE , padding="""VALID""" , groups=SCREAMING_SNAKE_CASE , use_bias=SCREAMING_SNAKE_CASE , name="""convolution""" , )
_a : Tuple =tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
_a : str =ACTaFN[activation] if activation is not None else tf.identity
def __UpperCAmelCase ( self :Any , SCREAMING_SNAKE_CASE :List[Any] ) -> List[str]:
'''simple docstring'''
_a : Dict =self.convolution(self.padding(SCREAMING_SNAKE_CASE ) )
_a : List[str] =self.normalization(SCREAMING_SNAKE_CASE )
_a : Tuple =self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :List[str] , SCREAMING_SNAKE_CASE :RegNetConfig , **SCREAMING_SNAKE_CASE :Any ) -> str:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Any =config.num_channels
_a : Tuple =TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , )
def __UpperCAmelCase ( self :Dict , SCREAMING_SNAKE_CASE :List[str] ) -> List[str]:
'''simple docstring'''
_a : int =shape_list(SCREAMING_SNAKE_CASE )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
_a : str =tf.transpose(SCREAMING_SNAKE_CASE , perm=(0, 2, 3, 1) )
_a : Tuple =self.embedder(SCREAMING_SNAKE_CASE )
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :Any , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int = 2 , **SCREAMING_SNAKE_CASE :Dict ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =tf.keras.layers.ConvaD(
filters=SCREAMING_SNAKE_CASE , kernel_size=1 , strides=SCREAMING_SNAKE_CASE , use_bias=SCREAMING_SNAKE_CASE , name="""convolution""" )
_a : Optional[int] =tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="""normalization""" )
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :tf.Tensor , SCREAMING_SNAKE_CASE :bool = False ) -> tf.Tensor:
'''simple docstring'''
return self.normalization(self.convolution(SCREAMING_SNAKE_CASE ) , training=SCREAMING_SNAKE_CASE )
class A__ ( tf.keras.layers.Layer ):
def __init__( self :List[Any] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int , **SCREAMING_SNAKE_CASE :Any ) -> Tuple:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Tuple =tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE , name="""pooler""" )
_a : Any =[
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE , kernel_size=1 , activation="""relu""" , name="""attention.0""" ),
tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ),
]
def __UpperCAmelCase ( self :Optional[Any] , SCREAMING_SNAKE_CASE :Optional[Any] ) -> Any:
'''simple docstring'''
# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
_a : Optional[int] =self.pooler(SCREAMING_SNAKE_CASE )
for layer_module in self.attention:
_a : Tuple =layer_module(SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =hidden_state * pooled
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :Optional[int] , SCREAMING_SNAKE_CASE :RegNetConfig , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int = 1 , **SCREAMING_SNAKE_CASE :int ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Any =in_channels != out_channels or stride != 1
_a : Any =max(1 , out_channels // config.groups_width )
_a : int =(
TFRegNetShortCut(SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
_a : Dict =[
TFRegNetConvLayer(SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , groups=SCREAMING_SNAKE_CASE , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE , kernel_size=1 , activation=SCREAMING_SNAKE_CASE , name="""layer.2""" ),
]
_a : Union[str, Any] =ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self :Any , SCREAMING_SNAKE_CASE :Any ) -> str:
'''simple docstring'''
_a : Any =hidden_state
for layer_module in self.layers:
_a : str =layer_module(SCREAMING_SNAKE_CASE )
_a : List[str] =self.shortcut(SCREAMING_SNAKE_CASE )
hidden_state += residual
_a : Tuple =self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :str , SCREAMING_SNAKE_CASE :RegNetConfig , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int = 1 , **SCREAMING_SNAKE_CASE :Any ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Tuple =in_channels != out_channels or stride != 1
_a : Any =max(1 , out_channels // config.groups_width )
_a : str =(
TFRegNetShortCut(SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , name="""shortcut""" )
if should_apply_shortcut
else tf.keras.layers.Activation("""linear""" , name="""shortcut""" )
)
_a : List[str] =[
TFRegNetConvLayer(SCREAMING_SNAKE_CASE , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ),
TFRegNetConvLayer(
SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , groups=SCREAMING_SNAKE_CASE , activation=config.hidden_act , name="""layer.1""" ),
TFRegNetSELayer(SCREAMING_SNAKE_CASE , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ),
TFRegNetConvLayer(SCREAMING_SNAKE_CASE , kernel_size=1 , activation=SCREAMING_SNAKE_CASE , name="""layer.3""" ),
]
_a : int =ACTaFN[config.hidden_act]
def __UpperCAmelCase ( self :str , SCREAMING_SNAKE_CASE :List[str] ) -> Tuple:
'''simple docstring'''
_a : List[Any] =hidden_state
for layer_module in self.layers:
_a : Dict =layer_module(SCREAMING_SNAKE_CASE )
_a : int =self.shortcut(SCREAMING_SNAKE_CASE )
hidden_state += residual
_a : Union[str, Any] =self.activation(SCREAMING_SNAKE_CASE )
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :RegNetConfig , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int = 2 , SCREAMING_SNAKE_CASE :int = 2 , **SCREAMING_SNAKE_CASE :str ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : int =TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
_a : List[Any] =[
# downsampling is done in the first layer with stride of 2
layer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , stride=SCREAMING_SNAKE_CASE , name="""layers.0""" ),
*[layer(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , name=f"layers.{i+1}" ) for i in range(depth - 1 )],
]
def __UpperCAmelCase ( self :Optional[Any] , SCREAMING_SNAKE_CASE :Optional[int] ) -> List[str]:
'''simple docstring'''
for layer_module in self.layers:
_a : Any =layer_module(SCREAMING_SNAKE_CASE )
return hidden_state
class A__ ( tf.keras.layers.Layer ):
def __init__( self :List[Any] , SCREAMING_SNAKE_CASE :RegNetConfig , **SCREAMING_SNAKE_CASE :Optional[int] ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =[]
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
SCREAMING_SNAKE_CASE , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) )
_a : int =zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , depth=SCREAMING_SNAKE_CASE , name=f"stages.{i+1}" ) )
def __UpperCAmelCase ( self :Any , SCREAMING_SNAKE_CASE :tf.Tensor , SCREAMING_SNAKE_CASE :bool = False , SCREAMING_SNAKE_CASE :bool = True ) -> TFBaseModelOutputWithNoAttention:
'''simple docstring'''
_a : Dict =() if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_a : Optional[Any] =hidden_states + (hidden_state,)
_a : str =stage_module(SCREAMING_SNAKE_CASE )
if output_hidden_states:
_a : List[Any] =hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE , hidden_states=SCREAMING_SNAKE_CASE )
@keras_serializable
class A__ ( tf.keras.layers.Layer ):
__UpperCamelCase : Dict = RegNetConfig
def __init__( self :Optional[Any] , SCREAMING_SNAKE_CASE :int , **SCREAMING_SNAKE_CASE :List[Any] ) -> Dict:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE )
_a : Optional[Any] =config
_a : Union[str, Any] =TFRegNetEmbeddings(SCREAMING_SNAKE_CASE , name="""embedder""" )
_a : Tuple =TFRegNetEncoder(SCREAMING_SNAKE_CASE , name="""encoder""" )
_a : Tuple =tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE , name="""pooler""" )
@unpack_inputs
def __UpperCAmelCase ( self :List[str] , SCREAMING_SNAKE_CASE :tf.Tensor , SCREAMING_SNAKE_CASE :Optional[bool] = None , SCREAMING_SNAKE_CASE :Optional[bool] = None , SCREAMING_SNAKE_CASE :bool = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
_a : Dict =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a : Optional[Any] =return_dict if return_dict is not None else self.config.use_return_dict
_a : Dict =self.embedder(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
_a : List[str] =self.encoder(
SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , return_dict=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
_a : List[Any] =encoder_outputs[0]
_a : List[Any] =self.pooler(SCREAMING_SNAKE_CASE )
# Change to NCHW output format have uniformity in the modules
_a : int =tf.transpose(SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) )
_a : Optional[int] =tf.transpose(SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
_a : int =tuple([tf.transpose(SCREAMING_SNAKE_CASE , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE , pooler_output=SCREAMING_SNAKE_CASE , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class A__ ( snake_case__ ):
__UpperCamelCase : Optional[int] = RegNetConfig
__UpperCamelCase : Any = 'regnet'
__UpperCamelCase : Any = 'pixel_values'
@property
def __UpperCAmelCase ( self :int ) -> List[Any]:
'''simple docstring'''
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
A__: int = R'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
A__: str = R'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"The bare RegNet model outputting raw features without any specific head on top." , snake_case__ , )
class A__ ( snake_case__ ):
def __init__( self :Any , SCREAMING_SNAKE_CASE :RegNetConfig , *SCREAMING_SNAKE_CASE :Any , **SCREAMING_SNAKE_CASE :Any ) -> Dict:
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
_a : Dict =TFRegNetMainLayer(SCREAMING_SNAKE_CASE , name="""regnet""" )
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __UpperCAmelCase ( self :Any , SCREAMING_SNAKE_CASE :tf.Tensor , SCREAMING_SNAKE_CASE :Optional[bool] = None , SCREAMING_SNAKE_CASE :Optional[bool] = None , SCREAMING_SNAKE_CASE :Optional[int]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
'''simple docstring'''
_a : Optional[int] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a : List[str] =return_dict if return_dict is not None else self.config.use_return_dict
_a : Dict =self.regnet(
pixel_values=SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , return_dict=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , snake_case__ , )
class A__ ( snake_case__ , snake_case__ ):
def __init__( self :List[str] , SCREAMING_SNAKE_CASE :RegNetConfig , *SCREAMING_SNAKE_CASE :List[str] , **SCREAMING_SNAKE_CASE :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(SCREAMING_SNAKE_CASE , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
_a : Dict =config.num_labels
_a : Any =TFRegNetMainLayer(SCREAMING_SNAKE_CASE , name="""regnet""" )
# classification head
_a : List[Any] =[
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __UpperCAmelCase ( self :Optional[Any] , SCREAMING_SNAKE_CASE :tf.Tensor = None , SCREAMING_SNAKE_CASE :tf.Tensor = None , SCREAMING_SNAKE_CASE :bool = None , SCREAMING_SNAKE_CASE :bool = None , SCREAMING_SNAKE_CASE :List[str]=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
'''simple docstring'''
_a : Optional[int] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_a : Optional[int] =return_dict if return_dict is not None else self.config.use_return_dict
_a : int =self.regnet(
SCREAMING_SNAKE_CASE , output_hidden_states=SCREAMING_SNAKE_CASE , return_dict=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
_a : List[Any] =outputs.pooler_output if return_dict else outputs[1]
_a : Dict =self.classifier[0](SCREAMING_SNAKE_CASE )
_a : Optional[Any] =self.classifier[1](SCREAMING_SNAKE_CASE )
_a : Optional[int] =None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE , logits=SCREAMING_SNAKE_CASE )
if not return_dict:
_a : Any =(logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE , logits=SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states )
| 276 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( snake_case__ ):
_lowercase : int = ['image_processor', 'tokenizer']
_lowercase : Union[str, Any] = 'LayoutLMv3ImageProcessor'
_lowercase : List[str] = ('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Any , UpperCAmelCase : Dict=None , UpperCAmelCase : Tuple=None , **UpperCAmelCase : Optional[Any] ) -> str:
__lowerCAmelCase: str = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , UpperCAmelCase , )
__lowerCAmelCase: List[Any] = kwargs.pop('feature_extractor' )
__lowerCAmelCase: Tuple = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(UpperCAmelCase , UpperCAmelCase )
def __call__( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , UpperCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , UpperCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , UpperCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase : Union[bool, str, TruncationStrategy] = None , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : int = 0 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : Optional[bool] = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : bool = True , UpperCAmelCase : Optional[Union[str, TensorType]] = None , **UpperCAmelCase : Tuple , ) -> BatchEncoding:
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.' )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' )
# first, apply the image processor
__lowerCAmelCase: str = self.image_processor(images=UpperCAmelCase , return_tensors=UpperCAmelCase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Tuple = [text] # add batch dimension (as the image processor always adds a batch dimension)
__lowerCAmelCase: List[str] = features['words']
__lowerCAmelCase: List[Any] = self.tokenizer(
text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=UpperCAmelCase , add_special_tokens=UpperCAmelCase , padding=UpperCAmelCase , truncation=UpperCAmelCase , max_length=UpperCAmelCase , stride=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_token_type_ids=UpperCAmelCase , return_attention_mask=UpperCAmelCase , return_overflowing_tokens=UpperCAmelCase , return_special_tokens_mask=UpperCAmelCase , return_offsets_mapping=UpperCAmelCase , return_length=UpperCAmelCase , verbose=UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase , )
# add pixel values
__lowerCAmelCase: Tuple = features.pop('pixel_values' )
if return_overflowing_tokens is True:
__lowerCAmelCase: int = self.get_overflowing_images(UpperCAmelCase , encoded_inputs['overflow_to_sample_mapping'] )
__lowerCAmelCase: str = images
return encoded_inputs
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] ) -> List[str]:
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
__lowerCAmelCase: str = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError(
'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
F''' {len(UpperCAmelCase )} and {len(UpperCAmelCase )}''' )
return images_with_overflow
def UpperCAmelCase ( self : Optional[int] , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*UpperCAmelCase , **UpperCAmelCase )
def UpperCAmelCase ( self : Any , *UpperCAmelCase : Dict , **UpperCAmelCase : Any ) -> List[str]:
return self.tokenizer.decode(*UpperCAmelCase , **UpperCAmelCase )
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> str:
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCAmelCase ( self : str ) -> Union[str, Any]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , UpperCAmelCase , )
return self.image_processor
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[str] ): # noqa: E741
'''simple docstring'''
while r - l > 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_CASE__ : List[Any] =m
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =m # noqa: E741
return r
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if len(UpperCamelCase__ ) == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =1
SCREAMING_SNAKE_CASE__ : Optional[int] =v[0]
for i in range(1, len(UpperCamelCase__ ) ):
if v[i] < tail[0]:
SCREAMING_SNAKE_CASE__ : Tuple =v[i]
elif v[i] > tail[length - 1]:
SCREAMING_SNAKE_CASE__ : Tuple =v[i]
length += 1
else:
SCREAMING_SNAKE_CASE__ : List[Any] =v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod() | 152 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
_a = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : tuple , SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int]=False , ) -> str:
"""simple docstring"""
output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , use_external_data_format=SCREAMING_SNAKE_CASE , enable_onnx_checker=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
else:
export(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=SCREAMING_SNAKE_CASE , output_names=SCREAMING_SNAKE_CASE , dynamic_axes=SCREAMING_SNAKE_CASE , do_constant_folding=SCREAMING_SNAKE_CASE , opset_version=SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : bool = False ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
__lowerCAmelCase: str = 'cuda'
elif fpaa and not torch.cuda.is_available():
raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
else:
__lowerCAmelCase: Dict = 'cpu'
__lowerCAmelCase: Optional[int] = Path(SCREAMING_SNAKE_CASE )
# VAE DECODER
__lowerCAmelCase: Optional[Any] = AutoencoderKL.from_pretrained(model_path + '/vae' )
__lowerCAmelCase: Union[str, Any] = vae_decoder.config.latent_channels
# forward only through the decoder part
__lowerCAmelCase: Any = vae_decoder.decode
onnx_export(
SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=SCREAMING_SNAKE_CASE , dtype=SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / 'vae_decoder' / 'model.onnx' , ordered_input_names=['latent_sample', 'return_dict'] , output_names=['sample'] , dynamic_axes={
'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
} , opset=SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=1_4,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
_a = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('''SD: Done: ONNX''')
| 322 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__a: Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default="cifar10" , metadata={"help": "Name of a dataset from the datasets package"} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={"help": "The column name of the images in the files."} )
SCREAMING_SNAKE_CASE = field(default=snake_case__ , metadata={"help": "A folder containing the training data."} )
SCREAMING_SNAKE_CASE = field(default=snake_case__ , metadata={"help": "A folder containing the validation data."} )
SCREAMING_SNAKE_CASE = field(
default=0.15 , metadata={"help": "Percent to split off of train for validation."} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
def _lowerCAmelCase( self ) -> str:
lowercase__ : int = {}
if self.train_dir is not None:
lowercase__ : str = self.train_dir
if self.validation_dir is not None:
lowercase__ : Dict = self.validation_dir
lowercase__ : Any = data_files if data_files else None
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={
"help": (
"The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
SCREAMING_SNAKE_CASE = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
SCREAMING_SNAKE_CASE = field(default=snake_case__ , metadata={"help": "Name or path of preprocessor config."} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=0.75 , metadata={"help": "The ratio of the number of masked tokens in the input sequence."} )
SCREAMING_SNAKE_CASE = field(
default=snake_case__ , metadata={"help": "Whether or not to train with normalized pixel values as target."} )
@dataclass
class UpperCAmelCase ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default=1e-3 , metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."} )
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def __UpperCamelCase ( ):
lowercase__ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ : Optional[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , UpperCAmelCase , UpperCAmelCase )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase__ : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(UpperCAmelCase )
transformers.utils.logging.set_verbosity(UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowercase__ : Union[str, Any] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase__ : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
lowercase__ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase__ : Any = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , UpperCAmelCase ) and data_args.train_val_split > 0.0:
lowercase__ : Optional[int] = ds['train'].train_test_split(data_args.train_val_split )
lowercase__ : Optional[Any] = split['train']
lowercase__ : Dict = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : Any = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowercase__ : Any = ViTMAEConfig.from_pretrained(model_args.config_name , **UpperCAmelCase )
elif model_args.model_name_or_path:
lowercase__ : Tuple = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase )
else:
lowercase__ : Any = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowercase__ : Optional[Any] = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **UpperCAmelCase )
elif model_args.model_name_or_path:
lowercase__ : str = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **UpperCAmelCase )
else:
lowercase__ : Any = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase__ : List[str] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
lowercase__ : Tuple = ViTMAEForPreTraining(UpperCAmelCase )
if training_args.do_train:
lowercase__ : Tuple = ds['train'].column_names
else:
lowercase__ : Dict = ds['validation'].column_names
if data_args.image_column_name is not None:
lowercase__ : Union[str, Any] = data_args.image_column_name
elif "image" in column_names:
lowercase__ : Optional[int] = 'image'
elif "img" in column_names:
lowercase__ : List[str] = 'img'
else:
lowercase__ : Optional[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowercase__ : Any = image_processor.size['shortest_edge']
else:
lowercase__ : Optional[int] = (image_processor.size['height'], image_processor.size['width'])
lowercase__ : int = Compose(
[
Lambda(lambda UpperCAmelCase : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(UpperCAmelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(UpperCAmelCase ):
lowercase__ : str = [transforms(UpperCAmelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
lowercase__ : List[str] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(UpperCAmelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
lowercase__ : Dict = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(UpperCAmelCase )
# Compute absolute learning rate
lowercase__ : Any = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase__ : str = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
lowercase__ : List[str] = Trainer(
model=UpperCAmelCase , args=UpperCAmelCase , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=UpperCAmelCase , data_collator=UpperCAmelCase , )
# Training
if training_args.do_train:
lowercase__ : Dict = None
if training_args.resume_from_checkpoint is not None:
lowercase__ : Tuple = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase__ : Union[str, Any] = last_checkpoint
lowercase__ : List[Any] = trainer.train(resume_from_checkpoint=UpperCAmelCase )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase__ : int = trainer.evaluate()
trainer.log_metrics('''eval''' , UpperCAmelCase )
trainer.save_metrics('''eval''' , UpperCAmelCase )
# Write model card and (optionally) push to hub
lowercase__ : str = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCAmelCase )
else:
trainer.create_model_card(**UpperCAmelCase )
def __UpperCamelCase ( UpperCAmelCase ):
main()
if __name__ == "__main__":
main()
| 198 |
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square(SCREAMING_SNAKE_CASE , col + 1 )
__lowerCAmelCase: Tuple = update_area_of_max_square(row + 1 , col + 1 )
__lowerCAmelCase: int = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: List[str] = 1 + min([right, diagonal, down] )
__lowerCAmelCase: List[str] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
return sub_problem_sol
else:
return 0
__lowerCAmelCase: List[str] = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
__lowerCAmelCase: List[Any] = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Any = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if mat[row][col]:
__lowerCAmelCase: int = 1 + min([right, diagonal, down] )
__lowerCAmelCase: Union[str, Any] = max(largest_square_area[0] , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = sub_problem_sol
return sub_problem_sol
else:
return 0
__lowerCAmelCase: int = [0]
__lowerCAmelCase: int = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE )
return largest_square_area[0]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: int = [[0] * (cols + 1) for _ in range(rows + 1 )]
__lowerCAmelCase: Optional[Any] = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: Union[str, Any] = dp_array[row][col + 1]
__lowerCAmelCase: str = dp_array[row + 1][col + 1]
__lowerCAmelCase: Optional[int] = dp_array[row + 1][col]
if mat[row][col] == 1:
__lowerCAmelCase: Optional[Any] = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(dp_array[row][col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Dict = 0
return largest_square_area
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[list[int]] ) -> int:
"""simple docstring"""
__lowerCAmelCase: Tuple = [0] * (cols + 1)
__lowerCAmelCase: Optional[int] = [0] * (cols + 1)
__lowerCAmelCase: str = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
__lowerCAmelCase: int = current_row[col + 1]
__lowerCAmelCase: Union[str, Any] = next_row[col + 1]
__lowerCAmelCase: Any = next_row[col]
if mat[row][col] == 1:
__lowerCAmelCase: str = 1 + min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: str = max(current_row[col] , SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase: Optional[Any] = 0
__lowerCAmelCase: int = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 322 | 0 |
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return x if y == 0 else greatest_common_divisor(lowerCamelCase__ , x % y )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return (x * y) // greatest_common_divisor(lowerCamelCase__ , lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ = 2_0 ):
lowerCamelCase_ = 1
for i in range(1 , n + 1 ):
lowerCamelCase_ = lcm(lowerCamelCase__ , lowerCamelCase__ )
return g
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_a = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_a = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = SavedModel()
__lowerCAmelCase: str = []
with open(os.path.join(SCREAMING_SNAKE_CASE , 'utils' , 'tf_ops' , 'onnx.json' ) ) as f:
__lowerCAmelCase: List[str] = json.load(SCREAMING_SNAKE_CASE )['opsets']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(SCREAMING_SNAKE_CASE )] )
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
saved_model.ParseFromString(f.read() )
__lowerCAmelCase: Optional[int] = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__lowerCAmelCase: List[str] = sorted(SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(SCREAMING_SNAKE_CASE )
if strict and len(SCREAMING_SNAKE_CASE ) > 0:
raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(SCREAMING_SNAKE_CASE ) > 0:
print(f'''Found the following incompatible ops for the opset {opset}:''' )
print(*SCREAMING_SNAKE_CASE , sep='\n' )
else:
print(f'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
parser.add_argument(
'''--opset''', default=1_2, type=int, help='''The ONNX opset against which the model has to be tested.'''
)
parser.add_argument(
'''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
)
parser.add_argument(
'''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
)
_a = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 322 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_lowerCAmelCase : str = logging.get_logger(__name__)
if is_vision_available():
import PIL
class _UpperCamelCase ( snake_case__ ):
UpperCAmelCase_ = ['pixel_values']
def __init__( self :int , lowerCamelCase :bool = True , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase :bool = True , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :bool = True , lowerCamelCase :Union[int, float] = 1 / 255 , lowerCamelCase :bool = True , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :bool = True , **lowerCamelCase :Any , ) -> None:
super().__init__(**lowerCamelCase )
UpperCAmelCase__ = size if size is not None else {'shortest_edge': 224}
UpperCAmelCase__ = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
UpperCAmelCase__ = crop_size if crop_size is not None else {'height': 224, 'width': 224}
UpperCAmelCase__ = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase , param_name="crop_size" )
UpperCAmelCase__ = do_resize
UpperCAmelCase__ = size
UpperCAmelCase__ = resample
UpperCAmelCase__ = do_center_crop
UpperCAmelCase__ = crop_size
UpperCAmelCase__ = do_rescale
UpperCAmelCase__ = rescale_factor
UpperCAmelCase__ = do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase__ = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase__ = do_convert_rgb
def UpperCAmelCase_ ( self :Optional[int] , lowerCamelCase :np.ndarray , lowerCamelCase :Dict[str, int] , lowerCamelCase :PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Optional[int] , ) -> np.ndarray:
UpperCAmelCase__ = get_size_dict(lowerCamelCase , default_to_square=lowerCamelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
UpperCAmelCase__ = get_resize_output_image_size(lowerCamelCase , size=size["shortest_edge"] , default_to_square=lowerCamelCase )
return resize(lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :np.ndarray , lowerCamelCase :Dict[str, int] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Any , ) -> np.ndarray:
UpperCAmelCase__ = get_size_dict(lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowerCamelCase , size=(size["height"], size["width"]) , data_format=lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :np.ndarray , lowerCamelCase :Union[int, float] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Optional[Any] , ) -> str:
return rescale(lowerCamelCase , scale=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :np.ndarray , lowerCamelCase :Union[float, List[float]] , lowerCamelCase :Union[float, List[float]] , lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase :Union[str, Any] , ) -> np.ndarray:
return normalize(lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase , data_format=lowerCamelCase , **lowerCamelCase )
def UpperCAmelCase_ ( self :Optional[Any] , lowerCamelCase :ImageInput , lowerCamelCase :bool = None , lowerCamelCase :Dict[str, int] = None , lowerCamelCase :PILImageResampling = None , lowerCamelCase :bool = None , lowerCamelCase :int = None , lowerCamelCase :bool = None , lowerCamelCase :float = None , lowerCamelCase :bool = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :Optional[Union[float, List[float]]] = None , lowerCamelCase :bool = None , lowerCamelCase :Optional[Union[str, TensorType]] = None , lowerCamelCase :Optional[ChannelDimension] = ChannelDimension.FIRST , **lowerCamelCase :int , ) -> PIL.Image.Image:
UpperCAmelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ = size if size is not None else self.size
UpperCAmelCase__ = get_size_dict(lowerCamelCase , param_name="size" , default_to_square=lowerCamelCase )
UpperCAmelCase__ = resample if resample is not None else self.resample
UpperCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ = get_size_dict(lowerCamelCase , param_name="crop_size" , default_to_square=lowerCamelCase )
UpperCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ = image_std if image_std is not None else self.image_std
UpperCAmelCase__ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase__ = make_list_of_images(lowerCamelCase )
if not valid_images(lowerCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase__ = [convert_to_rgb(lowerCamelCase ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase__ = [to_numpy_array(lowerCamelCase ) for image in images]
if do_resize:
UpperCAmelCase__ = [self.resize(image=lowerCamelCase , size=lowerCamelCase , resample=lowerCamelCase ) for image in images]
if do_center_crop:
UpperCAmelCase__ = [self.center_crop(image=lowerCamelCase , size=lowerCamelCase ) for image in images]
if do_rescale:
UpperCAmelCase__ = [self.rescale(image=lowerCamelCase , scale=lowerCamelCase ) for image in images]
if do_normalize:
UpperCAmelCase__ = [self.normalize(image=lowerCamelCase , mean=lowerCamelCase , std=lowerCamelCase ) for image in images]
UpperCAmelCase__ = [to_channel_dimension_format(lowerCamelCase , lowerCamelCase ) for image in images]
UpperCAmelCase__ = {'pixel_values': images}
return BatchFeature(data=lowerCamelCase , tensor_type=lowerCamelCase )
| 169 |
import math
import qiskit
def _a ( SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 , SCREAMING_SNAKE_CASE : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
):
raise TypeError('inputs must be integers.' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('inputs must be positive.' )
if (
(math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != input_a)
or (math.floor(SCREAMING_SNAKE_CASE ) != carry_in)
):
raise ValueError('inputs must be exact integers.' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('inputs must be less or equal to 2.' )
# build registers
__lowerCAmelCase: Union[str, Any] = qiskit.QuantumRegister(4 , 'qr' )
__lowerCAmelCase: List[Any] = qiskit.ClassicalRegister(2 , 'cr' )
# list the entries
__lowerCAmelCase: Any = [input_a, input_a, carry_in]
__lowerCAmelCase: List[str] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(SCREAMING_SNAKE_CASE ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(SCREAMING_SNAKE_CASE ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(SCREAMING_SNAKE_CASE ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , SCREAMING_SNAKE_CASE ) # measure the last two qbits
__lowerCAmelCase: List[str] = qiskit.Aer.get_backend('aer_simulator' )
__lowerCAmelCase: List[Any] = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"Total sum count for state is: {quantum_full_adder(1, 1, 1)}")
| 322 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
"facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}
class UpperCamelCase__ ( snake_case__ ):
"""simple docstring"""
UpperCAmelCase_ ='encodec'
def __init__( self , _A=[1.5, 3.0, 6.0, 12.0, 24.0] , _A=24000 , _A=1 , _A=False , _A=None , _A=None , _A=128 , _A=32 , _A=1 , _A=[8, 5, 4, 2] , _A="weight_norm" , _A=7 , _A=7 , _A=3 , _A=2 , _A=True , _A="reflect" , _A=2 , _A=2 , _A=1.0 , _A=1024 , _A=None , _A=True , **_A , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = target_bandwidths
SCREAMING_SNAKE_CASE_ = sampling_rate
SCREAMING_SNAKE_CASE_ = audio_channels
SCREAMING_SNAKE_CASE_ = normalize
SCREAMING_SNAKE_CASE_ = chunk_length_s
SCREAMING_SNAKE_CASE_ = overlap
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_filters
SCREAMING_SNAKE_CASE_ = num_residual_layers
SCREAMING_SNAKE_CASE_ = upsampling_ratios
SCREAMING_SNAKE_CASE_ = norm_type
SCREAMING_SNAKE_CASE_ = kernel_size
SCREAMING_SNAKE_CASE_ = last_kernel_size
SCREAMING_SNAKE_CASE_ = residual_kernel_size
SCREAMING_SNAKE_CASE_ = dilation_growth_rate
SCREAMING_SNAKE_CASE_ = use_causal_conv
SCREAMING_SNAKE_CASE_ = pad_mode
SCREAMING_SNAKE_CASE_ = compress
SCREAMING_SNAKE_CASE_ = num_lstm_layers
SCREAMING_SNAKE_CASE_ = trim_right_ratio
SCREAMING_SNAKE_CASE_ = codebook_size
SCREAMING_SNAKE_CASE_ = codebook_dim if codebook_dim is not None else hidden_size
SCREAMING_SNAKE_CASE_ = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''' )
super().__init__(**_A )
@property
def _UpperCamelCase ( self ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _UpperCamelCase ( self ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _UpperCamelCase ( self ) -> int:
SCREAMING_SNAKE_CASE_ = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _UpperCamelCase ( self ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 299 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
def __init__( self : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : int=3 , UpperCAmelCase : int=4 , UpperCAmelCase : str=2 , UpperCAmelCase : Union[str, Any]=7 , UpperCAmelCase : List[str]=True , UpperCAmelCase : Dict=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Optional[Any]=9_9 , UpperCAmelCase : Tuple=3_6 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Union[str, Any]=3_7 , UpperCAmelCase : Any="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : List[str]=5_1_2 , UpperCAmelCase : int=1_6 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Optional[Any]=0.02 , UpperCAmelCase : Optional[Any]=6 , UpperCAmelCase : int=6 , UpperCAmelCase : str=3 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=None , UpperCAmelCase : List[str]=1_0_0_0 , ) -> int:
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: List[str] = batch_size
__lowerCAmelCase: Optional[Any] = num_channels
__lowerCAmelCase: Tuple = image_size
__lowerCAmelCase: str = patch_size
__lowerCAmelCase: List[str] = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Union[str, Any] = use_token_type_ids
__lowerCAmelCase: Tuple = use_labels
__lowerCAmelCase: Optional[int] = vocab_size
__lowerCAmelCase: Any = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: Optional[int] = num_attention_heads
__lowerCAmelCase: Dict = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: str = hidden_dropout_prob
__lowerCAmelCase: str = attention_probs_dropout_prob
__lowerCAmelCase: str = max_position_embeddings
__lowerCAmelCase: str = type_vocab_size
__lowerCAmelCase: Optional[Any] = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: List[str] = coordinate_size
__lowerCAmelCase: Tuple = shape_size
__lowerCAmelCase: List[Any] = num_labels
__lowerCAmelCase: Any = num_choices
__lowerCAmelCase: List[str] = scope
__lowerCAmelCase: Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__lowerCAmelCase: Optional[Any] = text_seq_length
__lowerCAmelCase: List[Any] = (image_size // patch_size) ** 2 + 1
__lowerCAmelCase: int = self.text_seq_length + self.image_seq_length
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
__lowerCAmelCase: str = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
__lowerCAmelCase: Optional[Any] = bbox[i, j, 3]
__lowerCAmelCase: Tuple = bbox[i, j, 1]
__lowerCAmelCase: Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
__lowerCAmelCase: Any = bbox[i, j, 2]
__lowerCAmelCase: int = bbox[i, j, 0]
__lowerCAmelCase: int = tmp_coordinate
__lowerCAmelCase: List[Any] = tf.constant(UpperCAmelCase )
__lowerCAmelCase: Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase: Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase: List[Any] = random_attention_mask([self.batch_size, self.text_seq_length] )
__lowerCAmelCase: int = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__lowerCAmelCase: str = None
__lowerCAmelCase: Dict = None
if self.use_labels:
__lowerCAmelCase: Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__lowerCAmelCase: Dict = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ) -> int:
__lowerCAmelCase: Tuple = TFLayoutLMvaModel(config=UpperCAmelCase )
# text + image
__lowerCAmelCase: Dict = model(UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , training=UpperCAmelCase , )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__lowerCAmelCase: str = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__lowerCAmelCase: List[str] = model({'pixel_values': pixel_values} , training=UpperCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] ) -> int:
__lowerCAmelCase: List[str] = self.num_labels
__lowerCAmelCase: Tuple = TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int ) -> Any:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: List[str] = TFLayoutLMvaForTokenClassification(config=UpperCAmelCase )
__lowerCAmelCase: Any = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] ) -> Any:
__lowerCAmelCase: str = 2
__lowerCAmelCase: Dict = TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase )
__lowerCAmelCase: int = model(
UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , training=UpperCAmelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)): List[str] = config_and_inputs
__lowerCAmelCase: List[str] = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class A_ ( snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowercase : Tuple = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
_lowercase : Union[str, Any] = False
_lowercase : Dict = False
_lowercase : Tuple = False
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] ) -> List[str]:
return True
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Dict=False ) -> dict:
__lowerCAmelCase: Optional[Any] = copy.deepcopy(UpperCAmelCase )
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: int = {
k: tf.tile(tf.expand_dims(UpperCAmelCase , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(UpperCAmelCase , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(UpperCAmelCase ):
__lowerCAmelCase: str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase: Tuple = TFLayoutLMvaModelTester(self )
__lowerCAmelCase: str = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Tuple ) -> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase: List[Any] = model_class(UpperCAmelCase )
if getattr(UpperCAmelCase , 'hf_compute_loss' , UpperCAmelCase ):
# The number of elements in the loss should be the same as the number of elements in the label
__lowerCAmelCase: Optional[int] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: List[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=UpperCAmelCase )[0]
]
__lowerCAmelCase: Tuple = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
__lowerCAmelCase: Optional[Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Tuple = prepared_for_class.pop('input_ids' )
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
__lowerCAmelCase: Union[str, Any] = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[int] = prepared_for_class.pop('input_ids' )
if "labels" in prepared_for_class:
__lowerCAmelCase: str = prepared_for_class['labels'].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
__lowerCAmelCase: Tuple = -1_0_0
__lowerCAmelCase: Union[str, Any] = tf.convert_to_tensor(UpperCAmelCase )
__lowerCAmelCase: Dict = model(UpperCAmelCase , **UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
__lowerCAmelCase: str = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
__lowerCAmelCase: Any = self._prepare_for_class(inputs_dict.copy() , UpperCAmelCase , return_labels=UpperCAmelCase )
# Get keys that were added with the _prepare_for_class function
__lowerCAmelCase: Tuple = prepared_for_class.keys() - inputs_dict.keys()
__lowerCAmelCase: Dict = inspect.signature(model.call ).parameters
__lowerCAmelCase: Dict = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
__lowerCAmelCase: str = {0: 'input_ids'}
for label_key in label_keys:
__lowerCAmelCase: Optional[Any] = signature_names.index(UpperCAmelCase )
__lowerCAmelCase: Tuple = label_key
__lowerCAmelCase: Tuple = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
__lowerCAmelCase: List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
__lowerCAmelCase: Optional[Any] = prepared_for_class[value]
__lowerCAmelCase: Union[str, Any] = tuple(UpperCAmelCase )
# Send to model
__lowerCAmelCase: Any = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCAmelCase ( self : Dict ) -> Tuple:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> int:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): str = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowerCAmelCase: Tuple = type
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : int ) -> List[str]:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: Optional[int] = TFLayoutLMvaModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
__lowerCAmelCase: Dict = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
class A_ ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : int ) -> Dict:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self : Any ) -> List[str]:
__lowerCAmelCase: Any = TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base' )
__lowerCAmelCase: Tuple = self.default_image_processor
__lowerCAmelCase: str = prepare_img()
__lowerCAmelCase: Optional[int] = image_processor(images=UpperCAmelCase , return_tensors='tf' ).pixel_values
__lowerCAmelCase: Dict = tf.constant([[1, 2]] )
__lowerCAmelCase: str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
__lowerCAmelCase: List[str] = model(input_ids=UpperCAmelCase , bbox=UpperCAmelCase , pixel_values=UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
__lowerCAmelCase: Tuple = (1, 1_9_9, 7_6_8)
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase )
__lowerCAmelCase: str = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class _a ( snake_case__ ):
def __init__( self : List[Any] , lowercase : str=0.01 , lowercase : Tuple=1_000 ):
'''simple docstring'''
UpperCAmelCase = p_stop
UpperCAmelCase = max_length
def __iter__( self : Any ):
'''simple docstring'''
UpperCAmelCase = 0
UpperCAmelCase = False
while not stop and count < self.max_length:
yield count
count += 1
UpperCAmelCase = random.random() < self.p_stop
class _a ( unittest.TestCase ):
def A ( self : Optional[Any] , lowercase : List[str] , lowercase : str , lowercase : Any=False , lowercase : Union[str, Any]=True ):
'''simple docstring'''
UpperCAmelCase = [
BatchSamplerShard(lowercase , 2 , lowercase , split_batches=lowercase , even_batches=lowercase )
for i in range(2 )
]
UpperCAmelCase = [list(lowercase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(lowercase ) for shard in batch_sampler_shards] , [len(lowercase ) for e in expected] )
self.assertListEqual(lowercase , lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase )
# Check the shards when the dataset is very small.
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(lowercase , lowercase )
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
# Check the shards when the dataset is very small.
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=3 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(20 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
# Check the shards when the dataset is very small.
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=3 , drop_last=lowercase )
UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , even_batches=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(24 ) , batch_size=4 , drop_last=lowercase )
# Expected shouldn't change
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size.
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(22 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(21 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
# Check the shards when the dataset is very small.
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [[[0, 1]], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
UpperCAmelCase = BatchSampler(range(2 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = [[], []]
self.check_batch_sampler_shards(lowercase , lowercase , split_batches=lowercase , even_batches=lowercase )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
UpperCAmelCase = [BatchSamplerShard(lowercase , 2 , lowercase , even_batches=lowercase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def A ( self : List[str] , lowercase : Tuple , lowercase : int , lowercase : List[str] , lowercase : Dict=False , lowercase : Union[str, Any]=2 , lowercase : Union[str, Any]=False ):
'''simple docstring'''
random.seed(lowercase )
UpperCAmelCase = list(lowercase )
UpperCAmelCase = [
IterableDatasetShard(
lowercase , batch_size=lowercase , drop_last=lowercase , num_processes=lowercase , process_index=lowercase , split_batches=lowercase , )
for i in range(lowercase )
]
UpperCAmelCase = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(lowercase )
iterable_dataset_lists.append(list(lowercase ) )
UpperCAmelCase = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
UpperCAmelCase = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(lowercase ) , len(lowercase ) )
self.assertTrue(len(lowercase ) % shard_batch_size == 0 )
UpperCAmelCase = []
for idx in range(0 , len(lowercase ) , lowercase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(lowercase ) < len(lowercase ):
reference += reference
self.assertListEqual(lowercase , reference[: len(lowercase )] )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = 42
UpperCAmelCase = RandomIterableDataset()
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
# Edge case with a very small dataset
UpperCAmelCase = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
self.check_iterable_dataset_shards(lowercase , lowercase , batch_size=4 , drop_last=lowercase , split_batches=lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = BatchSampler(range(16 ) , batch_size=4 , drop_last=lowercase )
UpperCAmelCase = SkipBatchSampler(lowercase , 2 )
self.assertListEqual(list(lowercase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def A ( self : Any ):
'''simple docstring'''
UpperCAmelCase = DataLoader(list(range(16 ) ) , batch_size=4 )
UpperCAmelCase = skip_first_batches(lowercase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def A ( self : Optional[int] ):
'''simple docstring'''
Accelerator()
UpperCAmelCase = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(lowercase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 34 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any]=1_3 , UpperCAmelCase : Optional[int]=7 , UpperCAmelCase : Tuple=True , UpperCAmelCase : str=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Union[str, Any]=True , UpperCAmelCase : List[str]=9_9 , UpperCAmelCase : Optional[int]=3_2 , UpperCAmelCase : Dict=5 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : Tuple=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : str=5_1_2 , UpperCAmelCase : Dict=1_6 , UpperCAmelCase : Union[str, Any]=2 , UpperCAmelCase : int=0.02 , UpperCAmelCase : List[Any]=4 , ) -> Optional[Any]:
__lowerCAmelCase: str = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Optional[int] = seq_length
__lowerCAmelCase: Dict = is_training
__lowerCAmelCase: Optional[Any] = use_attention_mask
__lowerCAmelCase: List[Any] = use_token_type_ids
__lowerCAmelCase: Optional[int] = use_labels
__lowerCAmelCase: Optional[Any] = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Tuple = num_hidden_layers
__lowerCAmelCase: List[str] = num_attention_heads
__lowerCAmelCase: int = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[Any] = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Optional[int] = max_position_embeddings
__lowerCAmelCase: Union[str, Any] = type_vocab_size
__lowerCAmelCase: int = type_sequence_label_size
__lowerCAmelCase: Union[str, Any] = initializer_range
__lowerCAmelCase: Any = num_choices
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: List[Any] = None
if self.use_attention_mask:
__lowerCAmelCase: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Optional[Any] = None
if self.use_token_type_ids:
__lowerCAmelCase: List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase: Optional[int] = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self : Dict ) -> Any:
__lowerCAmelCase: Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: Optional[Any] = config_and_inputs
__lowerCAmelCase: Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( snake_case__ , unittest.TestCase ):
_lowercase : Dict = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self : List[str] ) -> Optional[int]:
__lowerCAmelCase: List[Any] = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase ( self : Tuple ) -> Dict:
for model_class_name in self.all_model_classes:
__lowerCAmelCase: Optional[Any] = model_class_name.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
@require_flax
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: List[Any] = FlaxAlbertModel.from_pretrained('albert-base-v2' )
__lowerCAmelCase: Optional[int] = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase: Tuple = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
__lowerCAmelCase: str = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[str] = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , UpperCAmelCase , atol=1E-4 ) )
| 322 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def a_ ( _lowerCAmelCase ) -> list[int]:
if num <= 0:
__lowerCamelCase : Any = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(_lowerCAmelCase )
__lowerCamelCase : Optional[Any] = [True] * (num + 1)
__lowerCamelCase : List[str] = []
__lowerCamelCase : Tuple = 2
__lowerCamelCase : str = int(math.sqrt(_lowerCAmelCase ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(_lowerCAmelCase )
# Set multiples of start be False
for i in range(start * start ,num + 1 ,_lowerCAmelCase ):
if sieve[i] is True:
__lowerCamelCase : List[str] = False
start += 1
for j in range(end + 1 ,num + 1 ):
if sieve[j] is True:
prime.append(_lowerCAmelCase )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 208 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
from timeit import timeit
lowercase_ = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = 0
__SCREAMING_SNAKE_CASE : Any = len(snake_case ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = len(snake_case ) // 2
__SCREAMING_SNAKE_CASE : Optional[Any] = len(snake_case )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(snake_case ) )
def a__ ( snake_case ):
"""simple docstring"""
if len(snake_case ) <= 2:
return True
if s[0] == s[len(snake_case ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def a__ ( snake_case ):
"""simple docstring"""
return s == s[::-1]
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = F'''all({name}(key) is value for key, value in test_data.items())'''
__SCREAMING_SNAKE_CASE : List[Any] = F'''from __main__ import test_data, {name}'''
__SCREAMING_SNAKE_CASE : List[str] = 500_000
__SCREAMING_SNAKE_CASE : Union[str, Any] = timeit(stmt=snake_case , setup=snake_case , number=snake_case )
print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(f'''{key:21} {value}''')
print("""a man a plan a canal panama""")
# finished 500,000 runs in 0.46793 seconds
benchmark_function("""is_palindrome_slice""")
# finished 500,000 runs in 0.85234 seconds
benchmark_function("""is_palindrome""")
# finished 500,000 runs in 1.32028 seconds
benchmark_function("""is_palindrome_recursive""")
# finished 500,000 runs in 2.08679 seconds
benchmark_function("""is_palindrome_traversal""")
| 303 |
_a = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def _a ( SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
__lowerCAmelCase: Optional[int] = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
number //= 10_00_00
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
_a = [None] * 1_0_0_0_0_0_0_0
_a = True
_a = False
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
__lowerCAmelCase: int = chain(next_number(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = number_chain
while number < 10_00_00_00:
__lowerCAmelCase: Dict = number_chain
number *= 10
return number_chain
def _a ( SCREAMING_SNAKE_CASE : int = 10_00_00_00 ) -> int:
"""simple docstring"""
for i in range(1 , SCREAMING_SNAKE_CASE ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{solution() = }")
| 322 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerConfig""",
"""InstructBlipVisionConfig""",
],
"""processing_instructblip""": ["""InstructBlipProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InstructBlipQFormerModel""",
"""InstructBlipPreTrainedModel""",
"""InstructBlipForConditionalGeneration""",
"""InstructBlipVisionModel""",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78 |
def _a ( SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowerCAmelCase: List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number < 0:
return False
__lowerCAmelCase: str = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 322 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Optional[Any] = get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
__lowerCAmelCase : Tuple = json.load(f)
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self : str , UpperCamelCase__ : Union[str, Any] ) -> str:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def _lowercase ( self : Union[str, Any] , UpperCamelCase__ : Optional[int] ) -> int:
"""simple docstring"""
__magic_name__ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _lowercase ( self : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] ) -> str:
"""simple docstring"""
__magic_name__ = F'''facebook/wmt19-{pair}'''
__magic_name__ = self.get_tokenizer(UpperCamelCase__ )
__magic_name__ = self.get_model(UpperCamelCase__ )
__magic_name__ = bleu_data[pair]['src']
__magic_name__ = bleu_data[pair]['tgt']
__magic_name__ = tokenizer(UpperCamelCase__ , return_tensors="""pt""" , truncation=UpperCamelCase__ , padding="""longest""" ).to(UpperCamelCase__ )
__magic_name__ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__magic_name__ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
__magic_name__ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["""bleu"""] , UpperCamelCase__ )
| 88 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A_ :
def __init__( self : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str=1_3 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : str=True , UpperCAmelCase : Any=True , UpperCAmelCase : Tuple=True , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[int]=True , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=False , UpperCAmelCase : int=False , UpperCAmelCase : Optional[int]=2 , UpperCAmelCase : Any=9_9 , UpperCAmelCase : str=0 , UpperCAmelCase : Dict=3_2 , UpperCAmelCase : int=5 , UpperCAmelCase : Optional[int]=4 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : str=0.1 , UpperCAmelCase : int=5_1_2 , UpperCAmelCase : str=2 , UpperCAmelCase : Optional[int]=0.02 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : List[str]=4 , UpperCAmelCase : Dict="last" , UpperCAmelCase : int=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Union[str, Any]=0 , ) -> Dict:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: Dict = batch_size
__lowerCAmelCase: Tuple = seq_length
__lowerCAmelCase: Tuple = is_training
__lowerCAmelCase: Optional[Any] = use_input_lengths
__lowerCAmelCase: List[str] = use_token_type_ids
__lowerCAmelCase: Dict = use_labels
__lowerCAmelCase: int = gelu_activation
__lowerCAmelCase: Optional[int] = sinusoidal_embeddings
__lowerCAmelCase: Tuple = causal
__lowerCAmelCase: Optional[Any] = asm
__lowerCAmelCase: int = n_langs
__lowerCAmelCase: Tuple = vocab_size
__lowerCAmelCase: List[Any] = n_special
__lowerCAmelCase: List[Any] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: int = hidden_dropout_prob
__lowerCAmelCase: List[str] = attention_probs_dropout_prob
__lowerCAmelCase: Dict = max_position_embeddings
__lowerCAmelCase: List[str] = type_sequence_label_size
__lowerCAmelCase: str = initializer_range
__lowerCAmelCase: List[str] = num_labels
__lowerCAmelCase: List[str] = num_choices
__lowerCAmelCase: Optional[int] = summary_type
__lowerCAmelCase: Any = use_proj
__lowerCAmelCase: Optional[Any] = scope
__lowerCAmelCase: Dict = bos_token_id
def UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase: Any = None
if self.use_input_lengths:
__lowerCAmelCase: Optional[Any] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
__lowerCAmelCase: str = None
if self.use_token_type_ids:
__lowerCAmelCase: Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: Optional[int] = None
if self.use_labels:
__lowerCAmelCase: Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase: Optional[int] = ids_tensor([self.batch_size] , 2 ).float()
__lowerCAmelCase: str = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase: Dict = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def UpperCAmelCase ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: List[str] = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , ) -> int:
__lowerCAmelCase: str = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str , UpperCAmelCase : Dict , ) -> List[str]:
__lowerCAmelCase: Dict = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: str = model(UpperCAmelCase )
__lowerCAmelCase: List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[str] = model(UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
__lowerCAmelCase: Any = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((__lowerCAmelCase) , ): List[str] = result_with_labels.to_tuple()
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((__lowerCAmelCase) , ): List[Any] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , ) -> List[Any]:
__lowerCAmelCase: Optional[Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = model(UpperCAmelCase )
__lowerCAmelCase: Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = self.num_labels
__lowerCAmelCase: Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[int] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
__lowerCAmelCase: List[Any] = self.num_choices
__lowerCAmelCase: Optional[Any] = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self : Tuple ) -> int:
__lowerCAmelCase: Optional[Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = config_and_inputs
__lowerCAmelCase: Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Any = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
_lowercase : Any = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
_lowercase : Optional[int] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : str , UpperCAmelCase : str ) -> int:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Tuple=False ) -> Dict:
__lowerCAmelCase: Optional[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
__lowerCAmelCase: str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: int = XLMModelTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=3_7 )
def UpperCAmelCase ( self : List[str] ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Dict ) -> List[Any]:
__lowerCAmelCase: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def UpperCAmelCase ( self : List[Any] ) -> int:
__lowerCAmelCase: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
__lowerCAmelCase: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> Tuple:
__lowerCAmelCase: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def UpperCAmelCase ( self : Any ) -> Any:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=False , UpperCAmelCase : Dict=1 ) -> Dict:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: int = min_length + idx + 1
__lowerCAmelCase: Union[str, Any] = min_length + idx + 1
__lowerCAmelCase: Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=False , UpperCAmelCase : Optional[int]=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
__lowerCAmelCase: Any = min_length + idx + 1
__lowerCAmelCase: str = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def UpperCAmelCase ( self : int ) -> Tuple:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase: List[Any] = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: Union[str, Any] = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
__lowerCAmelCase: Optional[int] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=UpperCAmelCase ) # the president
__lowerCAmelCase: Union[str, Any] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
__lowerCAmelCase: str = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 322 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class A__ ( unittest.TestCase ):
__UpperCamelCase : Union[str, Any] = MODEL_FOR_MASKED_LM_MAPPING
__UpperCamelCase : Optional[int] = TF_MODEL_FOR_MASKED_LM_MAPPING
def __UpperCAmelCase ( self :Dict ) -> List[Any]:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def __UpperCAmelCase ( self :str ) -> List[str]:
'''simple docstring'''
_a : Union[str, Any] =pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""tf""" )
_a : str =unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8_0_1_5, """token_str""": """ grouped"""},
{"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5_5_0_6, """token_str""": """ accuser"""},
] , )
_a : str =unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{
"""sequence""": """The largest city in France is grouped""",
"""score""": 2.1e-05,
"""token""": 3_8_0_1_5,
"""token_str""": """ grouped""",
},
{
"""sequence""": """The largest city in France is accuser""",
"""score""": 2.1e-05,
"""token""": 2_5_5_0_6,
"""token_str""": """ accuser""",
},
] , )
_a : Optional[int] =unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
{"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
] , )
@require_torch
def __UpperCAmelCase ( self :int ) -> List[str]:
'''simple docstring'''
_a : Tuple =pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""pt""" )
_a : Optional[int] =unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5_6_7_6, """token_str""": """ Maul"""},
{"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
] , )
_a : int =unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{
"""sequence""": """The largest city in France is Maul""",
"""score""": 2.2e-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
},
{"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
] , )
_a : Optional[Any] =unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
{"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
{"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
] , )
_a : Any =unmasker("""My name is <mask> <mask>""" , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE , decimals=6 ) , [
[
{
"""score""": 2.2e-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is Maul<mask></s>""",
},
{"""score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
],
[
{
"""score""": 2.2e-05,
"""token""": 3_5_6_7_6,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is<mask> Maul</s>""",
},
{"""score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
],
] , )
@require_torch_gpu
def __UpperCAmelCase ( self :List[Any] ) -> Dict:
'''simple docstring'''
_a : int =pipeline("""fill-mask""" , model="""hf-internal-testing/tiny-random-distilbert""" , device=0 , framework="""pt""" )
# convert model to fp16
pipe.model.half()
_a : Any =pipe("""Paris is the [MASK] of France.""" )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
@require_torch
def __UpperCAmelCase ( self :Dict ) -> Optional[int]:
'''simple docstring'''
_a : Union[str, Any] =pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""pt""" )
self.run_large_test(SCREAMING_SNAKE_CASE )
@slow
@require_tf
def __UpperCAmelCase ( self :List[str] ) -> int:
'''simple docstring'''
_a : List[str] =pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""tf""" )
self.run_large_test(SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :str , SCREAMING_SNAKE_CASE :Any ) -> str:
'''simple docstring'''
_a : Dict =unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE ) , [
{"""sequence""": """My name is John""", """score""": 0.008, """token""": 6_1_0, """token_str""": """ John"""},
{"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1_5_7_3, """token_str""": """ Chris"""},
] , )
_a : Optional[int] =unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE ) , [
{
"""sequence""": """The largest city in France is Paris""",
"""score""": 0.251,
"""token""": 2_2_0_1,
"""token_str""": """ Paris""",
},
{
"""sequence""": """The largest city in France is Lyon""",
"""score""": 0.214,
"""token""": 1_2_7_9_0,
"""token_str""": """ Lyon""",
},
] , )
_a : Optional[int] =unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE ) , [
{"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
{"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_9_4_1, """token_str""": """ Te"""},
] , )
@require_torch
def __UpperCAmelCase ( self :Dict ) -> str:
'''simple docstring'''
_a : Dict =pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""pt""" )
_a : List[str] =None
_a : List[str] =None
self.run_pipeline_test(SCREAMING_SNAKE_CASE , [] )
@require_tf
def __UpperCAmelCase ( self :str ) -> Tuple:
'''simple docstring'''
_a : List[str] =pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""tf""" )
_a : Optional[int] =None
_a : Optional[Any] =None
self.run_pipeline_test(SCREAMING_SNAKE_CASE , [] )
def __UpperCAmelCase ( self :int , SCREAMING_SNAKE_CASE :Optional[Any] , SCREAMING_SNAKE_CASE :Dict , SCREAMING_SNAKE_CASE :Optional[int] ) -> List[str]:
'''simple docstring'''
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)""" )
_a : Dict =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
_a : Tuple =[
f"This is another {tokenizer.mask_token} test",
]
return fill_masker, examples
def __UpperCAmelCase ( self :List[str] , SCREAMING_SNAKE_CASE :Tuple , SCREAMING_SNAKE_CASE :Optional[Any] ) -> Dict:
'''simple docstring'''
_a : Any =fill_masker.tokenizer
_a : Union[str, Any] =fill_masker.model
_a : Any =fill_masker(
f"This is a {tokenizer.mask_token}" , )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
_a : Any =fill_masker([f"This is a {tokenizer.mask_token}"] )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
_a : Optional[Any] =fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."] )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
[
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
],
[
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
],
] , )
with self.assertRaises(SCREAMING_SNAKE_CASE ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(SCREAMING_SNAKE_CASE ):
fill_masker("""This is""" )
self.run_test_top_k(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.run_test_targets(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.run_test_top_k_targets(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.fill_mask_with_duplicate_targets_and_top_k(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.fill_mask_with_multiple_masks(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __UpperCAmelCase ( self :Tuple , SCREAMING_SNAKE_CASE :Any , SCREAMING_SNAKE_CASE :int ) -> int:
'''simple docstring'''
_a : Any =tokenizer.get_vocab()
_a : Dict =sorted(vocab.keys() )[:2]
# Pipeline argument
_a : Any =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , targets=SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =fill_masker(f"This is a {tokenizer.mask_token}" )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
_a : str ={vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , SCREAMING_SNAKE_CASE )
_a : Tuple =[tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(SCREAMING_SNAKE_CASE ) )
# Call argument
_a : Tuple =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
_a : Optional[int] =fill_masker(f"This is a {tokenizer.mask_token}" , targets=SCREAMING_SNAKE_CASE )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
_a : Union[str, Any] ={vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =[tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(SCREAMING_SNAKE_CASE ) )
# Score equivalence
_a : Tuple =fill_masker(f"This is a {tokenizer.mask_token}" , targets=SCREAMING_SNAKE_CASE )
_a : List[str] =[top_mask['token_str'] for top_mask in outputs]
_a : List[str] =[top_mask['score'] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(SCREAMING_SNAKE_CASE ) == set(SCREAMING_SNAKE_CASE ):
_a : int =fill_masker(f"This is a {tokenizer.mask_token}" , targets=SCREAMING_SNAKE_CASE )
_a : int =[top_mask['score'] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE ) , nested_simplify(SCREAMING_SNAKE_CASE ) )
# Raises with invalid
with self.assertRaises(SCREAMING_SNAKE_CASE ):
_a : Optional[Any] =fill_masker(f"This is a {tokenizer.mask_token}" , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(SCREAMING_SNAKE_CASE ):
_a : Optional[int] =fill_masker(f"This is a {tokenizer.mask_token}" , targets=[""""""] )
with self.assertRaises(SCREAMING_SNAKE_CASE ):
_a : str =fill_masker(f"This is a {tokenizer.mask_token}" , targets="""""" )
def __UpperCAmelCase ( self :List[str] , SCREAMING_SNAKE_CASE :Union[str, Any] , SCREAMING_SNAKE_CASE :List[str] ) -> Union[str, Any]:
'''simple docstring'''
_a : List[Any] =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , top_k=2 )
_a : Optional[int] =fill_masker(f"This is a {tokenizer.mask_token}" )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
_a : str =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
_a : Tuple =fill_masker(f"This is a {tokenizer.mask_token}" , top_k=2 )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
] , )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE ) , nested_simplify(SCREAMING_SNAKE_CASE ) )
def __UpperCAmelCase ( self :str , SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :Dict ) -> Optional[Any]:
'''simple docstring'''
_a : Union[str, Any] =tokenizer.get_vocab()
_a : List[Any] =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
# top_k=2, ntargets=3
_a : Any =sorted(vocab.keys() )[:3]
_a : Dict =fill_masker(f"This is a {tokenizer.mask_token}" , top_k=2 , targets=SCREAMING_SNAKE_CASE )
# If we use the most probably targets, and filter differently, we should still
# have the same results
_a : Tuple =[el['token_str'] for el in sorted(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE : x["score"] , reverse=SCREAMING_SNAKE_CASE )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(SCREAMING_SNAKE_CASE ).issubset(SCREAMING_SNAKE_CASE ):
_a : int =fill_masker(f"This is a {tokenizer.mask_token}" , top_k=3 , targets=SCREAMING_SNAKE_CASE )
# They should yield exactly the same result
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE ) , nested_simplify(SCREAMING_SNAKE_CASE ) )
def __UpperCAmelCase ( self :List[Any] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :str ) -> Dict:
'''simple docstring'''
_a : Union[str, Any] =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
_a : Any =tokenizer.get_vocab()
# String duplicates + id duplicates
_a : int =sorted(vocab.keys() )[:3]
_a : List[str] =[targets[0], targets[1], targets[0], targets[2], targets[1]]
_a : Optional[int] =fill_masker(f"My name is {tokenizer.mask_token}" , targets=SCREAMING_SNAKE_CASE , top_k=1_0 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 3 )
def __UpperCAmelCase ( self :Tuple , SCREAMING_SNAKE_CASE :Optional[Any] , SCREAMING_SNAKE_CASE :Tuple ) -> Dict:
'''simple docstring'''
_a : Union[str, Any] =FillMaskPipeline(model=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
_a : int =fill_masker(
f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}" , top_k=2 )
self.assertEqual(
SCREAMING_SNAKE_CASE , [
[
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
],
[
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
],
[
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
{"""sequence""": ANY(SCREAMING_SNAKE_CASE ), """score""": ANY(SCREAMING_SNAKE_CASE ), """token""": ANY(SCREAMING_SNAKE_CASE ), """token_str""": ANY(SCREAMING_SNAKE_CASE )},
],
] , )
| 276 |
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[Any] = 0
__lowerCAmelCase: Optional[int] = len(SCREAMING_SNAKE_CASE )
for i in range(n - 1 ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a ( SCREAMING_SNAKE_CASE : Any ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) <= 1:
return arr, 0
__lowerCAmelCase: str = len(SCREAMING_SNAKE_CASE ) // 2
__lowerCAmelCase: str = arr[0:mid]
__lowerCAmelCase: int = arr[mid:]
__lowerCAmelCase , __lowerCAmelCase: List[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: int = _count_cross_inversions(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase: int = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a ( SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any ) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase: List[str] = []
__lowerCAmelCase: List[str] = 0
while i < len(SCREAMING_SNAKE_CASE ) and j < len(SCREAMING_SNAKE_CASE ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(SCREAMING_SNAKE_CASE ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a ( ) -> int:
"""simple docstring"""
__lowerCAmelCase: List[Any] = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: str = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 8
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
__lowerCAmelCase: Tuple = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Optional[Any] = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
# an empty list should also have zero inversions
__lowerCAmelCase: int = []
__lowerCAmelCase: Any = count_inversions_bf(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase: Dict = count_inversions_recursive(SCREAMING_SNAKE_CASE )
assert num_inversions_bf == num_inversions_recursive == 0
print('number of inversions = ' , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 322 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
snake_case_ = CLIPTokenizer
snake_case_ = CLIPTokenizerFast
snake_case_ = True
snake_case_ = {}
snake_case_ = False
def __magic_name__ ( self : List[str] ) -> str:
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
SCREAMING_SNAKE_CASE__ : str =dict(zip(__lowercase , range(len(__lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ : List[Any] =['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
SCREAMING_SNAKE_CASE__ : Optional[Any] ={'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__lowercase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowercase ) )
def __magic_name__ ( self : Dict , **__lowercase : Union[str, Any] ) -> Optional[int]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def __magic_name__ ( self : List[str] , **__lowercase : Optional[Any] ) -> List[Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def __magic_name__ ( self : List[str] , __lowercase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : str ='lower newer'
SCREAMING_SNAKE_CASE__ : Dict ='lower newer'
return input_text, output_text
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ : Optional[Any] ='lower newer'
SCREAMING_SNAKE_CASE__ : Dict =['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
SCREAMING_SNAKE_CASE__ : Any =tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : int =tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : List[Any] =[10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
@require_ftfy
def __magic_name__ ( self : List[Any] ) -> List[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE__ : int =self.tokenizer_class.from_pretrained(__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple ='A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer_s.tokenize(__lowercase )
SCREAMING_SNAKE_CASE__ : int =tokenizer_r.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
SCREAMING_SNAKE_CASE__ : Optional[Any] ='xa\u0303y' + ' ' + 'x\xe3y'
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer_s.tokenize(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =tokenizer_r.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Test that the tokenization is identical on unicode of space type
SCREAMING_SNAKE_CASE__ : List[str] =[
'\u0009', # (horizontal tab, '\t')
'\u000B', # (vertical tab)
'\u000C', # (form feed)
'\u0020', # (space, ' ')
'\u200E', # (left-to-right mark):w
'\u200F', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer_s.tokenize(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer_r.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Test that the tokenization is identical on unicode of line break type
SCREAMING_SNAKE_CASE__ : Optional[int] =[
'\u000A', # (line feed, '\n')
'\r\n', # (carriage return and line feed, '\r\n')
'\u000D', # (carriage return, '\r')
'\r', # (carriage return, '\r')
'\u000D', # (carriage return, '\r')
'\u2028', # (line separator)
'\u2029', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
SCREAMING_SNAKE_CASE__ : Dict =tokenizer_s.tokenize(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer_r.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
def __magic_name__ ( self : Optional[Any] ) -> List[str]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='hello' # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE__ : int =F"{text_of_1_token} {text_of_1_token}"
SCREAMING_SNAKE_CASE__ : str =self.rust_tokenizer_class.from_pretrained(
__lowercase , use_fast=__lowercase , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer_r(__lowercase , return_offsets_mapping=__lowercase , add_special_tokens=__lowercase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__lowercase ) + 1, len(__lowercase ) + 1 + len(__lowercase )) , )
SCREAMING_SNAKE_CASE__ : Any =F" {text}"
SCREAMING_SNAKE_CASE__ : Dict =self.rust_tokenizer_class.from_pretrained(
__lowercase , use_fast=__lowercase , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer_r(__lowercase , return_offsets_mapping=__lowercase , add_special_tokens=__lowercase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__lowercase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__lowercase ) + 1, 1 + len(__lowercase ) + 1 + len(__lowercase )) , )
def __magic_name__ ( self : Optional[Any] ) -> Dict:
# Test related to the breaking change introduced in transformers v4.17.0
# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
with self.assertRaises(__lowercase ) as context:
self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer''' )
self.assertTrue(
context.exception.args[0].startswith(
'''The `backend_tokenizer` provided does not match the expected format.''' ) )
@require_ftfy
def __magic_name__ ( self : Optional[int] ) -> Any:
super().test_tokenization_python_rust_equals()
def __magic_name__ ( self : Optional[Any] ) -> str:
# CLIP always lower cases letters
pass | 152 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( snake_case__ ):
_lowercase : int = (DPMSolverSinglestepScheduler,)
_lowercase : Optional[Any] = (('num_inference_steps', 2_5),)
def UpperCAmelCase ( self : Dict , **UpperCAmelCase : List[Any] ) -> Optional[Any]:
__lowerCAmelCase: Union[str, Any] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**UpperCAmelCase )
return config
def UpperCAmelCase ( self : str , UpperCAmelCase : List[Any]=0 , **UpperCAmelCase : str ) -> Any:
__lowerCAmelCase: Optional[int] = dict(self.forward_default_kwargs )
__lowerCAmelCase: int = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: int = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Union[str, Any] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: Dict = scheduler_class.from_pretrained(UpperCAmelCase )
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals
__lowerCAmelCase: Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase , __lowerCAmelCase: Optional[int] = sample, sample
for t in range(UpperCAmelCase , time_step + scheduler.config.solver_order + 1 ):
__lowerCAmelCase: str = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: str = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : str ) -> str:
pass
def UpperCAmelCase ( self : Optional[Any] , UpperCAmelCase : Any=0 , **UpperCAmelCase : Optional[int] ) -> Tuple:
__lowerCAmelCase: Tuple = dict(self.forward_default_kwargs )
__lowerCAmelCase: Tuple = kwargs.pop('num_inference_steps' , UpperCAmelCase )
__lowerCAmelCase: Tuple = self.dummy_sample
__lowerCAmelCase: Union[str, Any] = 0.1 * sample
__lowerCAmelCase: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
__lowerCAmelCase: Dict = self.get_scheduler_config()
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
__lowerCAmelCase: List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCAmelCase )
__lowerCAmelCase: List[str] = scheduler_class.from_pretrained(UpperCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
__lowerCAmelCase: Optional[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
__lowerCAmelCase: Dict = new_scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCAmelCase ( self : int , UpperCAmelCase : Dict=None , **UpperCAmelCase : List[str] ) -> Union[str, Any]:
if scheduler is None:
__lowerCAmelCase: str = self.scheduler_classes[0]
__lowerCAmelCase: int = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Any = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = self.scheduler_classes[0]
__lowerCAmelCase: List[str] = self.get_scheduler_config(**UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: List[Any] = 1_0
__lowerCAmelCase: Dict = self.dummy_model()
__lowerCAmelCase: Dict = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Dict = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
return sample
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Any = 5_0
__lowerCAmelCase: int = self.dummy_model()
__lowerCAmelCase: List[str] = self.dummy_sample_deter
scheduler.set_timesteps(UpperCAmelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
__lowerCAmelCase: List[Any] = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: List[Any] = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
__lowerCAmelCase: Optional[int] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCAmelCase )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
__lowerCAmelCase: List[str] = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
__lowerCAmelCase: Dict = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
__lowerCAmelCase: Tuple = DEISMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: List[str] = DPMSolverMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Any = UniPCMultistepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Optional[int] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
__lowerCAmelCase: Union[str, Any] = self.full_loop(scheduler=UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : List[str] ) -> List[str]:
self.check_over_configs(thresholding=UpperCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=UpperCAmelCase , prediction_type=UpperCAmelCase , sample_max_value=UpperCAmelCase , algorithm_type='dpmsolver++' , solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , )
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> str:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
__lowerCAmelCase: Dict = self.full_loop(
solver_order=UpperCAmelCase , solver_type=UpperCAmelCase , prediction_type=UpperCAmelCase , algorithm_type=UpperCAmelCase , )
assert not torch.isnan(UpperCAmelCase ).any(), "Samples have nan numbers"
def UpperCAmelCase ( self : Optional[Any] ) -> str:
self.check_over_configs(lower_order_final=UpperCAmelCase )
self.check_over_configs(lower_order_final=UpperCAmelCase )
def UpperCAmelCase ( self : str ) -> Any:
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase ( self : List[Any] ) -> str:
self.check_over_configs(variance_type=UpperCAmelCase )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=UpperCAmelCase , time_step=0 )
def UpperCAmelCase ( self : Any ) -> int:
__lowerCAmelCase: Any = self.full_loop()
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.full_loop(use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: str = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def UpperCAmelCase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase: Tuple = self.full_loop(prediction_type='v_prediction' )
__lowerCAmelCase: List[str] = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def UpperCAmelCase ( self : str ) -> List[str]:
__lowerCAmelCase: int = self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=UpperCAmelCase )
__lowerCAmelCase: Tuple = torch.mean(torch.abs(UpperCAmelCase ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase: Any = self.scheduler_classes[0]
__lowerCAmelCase: Optional[Any] = self.get_scheduler_config(thresholding=UpperCAmelCase , dynamic_thresholding_ratio=0 )
__lowerCAmelCase: List[str] = scheduler_class(**UpperCAmelCase )
__lowerCAmelCase: Optional[int] = 1_0
__lowerCAmelCase: Union[str, Any] = self.dummy_model()
__lowerCAmelCase: int = self.dummy_sample_deter.half()
scheduler.set_timesteps(UpperCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
__lowerCAmelCase: Any = model(UpperCAmelCase , UpperCAmelCase )
__lowerCAmelCase: Any = scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 322 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__a: str = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
__a: str = parser.parse_args()
if args.model_type == "roberta":
__a: Any = RobertaForMaskedLM.from_pretrained(args.model_name)
__a: Dict = """roberta"""
elif args.model_type == "gpt2":
__a: Tuple = GPTaLMHeadModel.from_pretrained(args.model_name)
__a: List[Any] = """transformer"""
__a: Optional[int] = model.state_dict()
__a: str = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__a: Union[str, Any] = state_dict[F'{prefix}.{param_name}']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__a: int = F'{prefix}.embeddings.{w}.weight'
__a: str = state_dict[param_name]
for w in ["weight", "bias"]:
__a: List[str] = F'{prefix}.embeddings.LayerNorm.{w}'
__a: List[Any] = state_dict[param_name]
# Transformer Blocks #
__a: List[str] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__a: int = state_dict[
F'{prefix}.h.{teacher_idx}.{layer}.{w}'
]
__a: int = state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__a: Optional[int] = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__a: List[Any] = state_dict[F'{layer}']
if args.vocab_transform:
for w in ["weight", "bias"]:
__a: Union[str, Any] = state_dict[F'lm_head.dense.{w}']
__a: Optional[Any] = state_dict[F'lm_head.layer_norm.{w}']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__a: str = state_dict[F'{prefix}.ln_f.{w}']
__a: Any = state_dict["""lm_head.weight"""]
print(F'N layers selected for distillation: {std_idx}')
print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 198 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: Union[str, Any] = int(SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = t // 36_00, (t // 60) % 60, t % 60
return f'''{h}:{m:02d}:{s:02d}''' if h != 0 else f'''{m:02d}:{s:02d}'''
def _a ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str=3_00 ) -> int:
"""simple docstring"""
return f'''
<div>
{prefix}
<progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
{label}
</div>
'''
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase: List[str] = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f''' <th>{i}</th>\n'''
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
__lowerCAmelCase: List[Any] = f'''{elt:.6f}''' if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else str(SCREAMING_SNAKE_CASE )
html_code += f''' <td>{elt}</td>\n'''
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class A_ :
_lowercase : str = 5
_lowercase : str = 0.2
def __init__( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Optional[str] = None , UpperCAmelCase : bool = True , UpperCAmelCase : Optional["NotebookTrainingTracker"] = None , UpperCAmelCase : int = 3_0_0 , ) -> List[Any]:
__lowerCAmelCase: List[str] = total
__lowerCAmelCase: Optional[int] = '' if prefix is None else prefix
__lowerCAmelCase: int = leave
__lowerCAmelCase: List[str] = parent
__lowerCAmelCase: Optional[Any] = width
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = None
__lowerCAmelCase: List[str] = None
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : bool = False , UpperCAmelCase : str = None ) -> Optional[int]:
__lowerCAmelCase: int = value
if comment is not None:
__lowerCAmelCase: Any = comment
if self.last_value is None:
__lowerCAmelCase: List[Any] = time.time()
__lowerCAmelCase: Any = value
__lowerCAmelCase: List[str] = None
__lowerCAmelCase: Dict = self.warmup
__lowerCAmelCase: List[str] = 1
self.update_bar(UpperCAmelCase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
__lowerCAmelCase: Union[str, Any] = time.time()
__lowerCAmelCase: str = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
__lowerCAmelCase: Dict = self.elapsed_time / (value - self.start_value)
else:
__lowerCAmelCase: int = None
if value >= self.total:
__lowerCAmelCase: Any = self.total
__lowerCAmelCase: str = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
__lowerCAmelCase: List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(UpperCAmelCase )
__lowerCAmelCase: Tuple = value
__lowerCAmelCase: int = current_time
if self.average_time_per_item is None:
__lowerCAmelCase: Optional[int] = 1
else:
__lowerCAmelCase: Optional[Any] = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCAmelCase ( self : int , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=None ) -> Union[str, Any]:
__lowerCAmelCase: int = ' ' * (len(str(self.total ) ) - len(str(UpperCAmelCase ) )) + str(UpperCAmelCase )
if self.elapsed_time is None:
__lowerCAmelCase: Dict = F'''[{spaced_value}/{self.total} : < :'''
elif self.predicted_remaining is None:
__lowerCAmelCase: str = F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'''
else:
__lowerCAmelCase: Any = (
F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'''
F''' {format_time(self.predicted_remaining )}'''
)
self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F''', {self.comment}]'''
self.display()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase: Any = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
__lowerCAmelCase: Tuple = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : str ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class A_ ( snake_case__ ):
def __init__( self : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : List[Any]=None ) -> Any:
super().__init__(UpperCAmelCase )
__lowerCAmelCase: Tuple = None if column_names is None else [column_names]
__lowerCAmelCase: Union[str, Any] = None
def UpperCAmelCase ( self : Union[str, Any] ) -> Any:
__lowerCAmelCase: str = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
__lowerCAmelCase: Optional[Any] = disp.display(disp.HTML(self.html_code ) , display_id=UpperCAmelCase )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[Any] ) -> Dict:
if self.inner_table is None:
__lowerCAmelCase: List[str] = [list(values.keys() ), list(values.values() )]
else:
__lowerCAmelCase: Any = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(UpperCAmelCase )
__lowerCAmelCase: List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCAmelCase ( self : Dict , UpperCAmelCase : int , UpperCAmelCase : List[Any]=None , UpperCAmelCase : List[str]=3_0_0 ) -> List[Any]:
__lowerCAmelCase: Union[str, Any] = NotebookProgressBar(UpperCAmelCase , prefix=UpperCAmelCase , parent=self , width=UpperCAmelCase )
return self.child_bar
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Tuple = None
self.display()
class A_ ( snake_case__ ):
def __init__( self : Any ) -> List[str]:
__lowerCAmelCase: int = None
__lowerCAmelCase: Optional[int] = None
__lowerCAmelCase: str = False
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , **UpperCAmelCase : Tuple ) -> str:
__lowerCAmelCase: Tuple = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
__lowerCAmelCase: Optional[int] = 0
__lowerCAmelCase: Any = 0
__lowerCAmelCase: Tuple = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
__lowerCAmelCase: List[Any] = NotebookTrainingTracker(state.max_steps , UpperCAmelCase )
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Union[str, Any] ) -> Any:
__lowerCAmelCase: Union[str, Any] = int(state.epoch ) if int(state.epoch ) == state.epoch else F'''{state.epoch:.2f}'''
self.training_tracker.update(
state.global_step + 1 , comment=F'''Epoch {epoch}/{state.num_train_epochs}''' , force_update=self._force_next_update , )
__lowerCAmelCase: Any = False
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Dict ) -> List[Any]:
if not has_length(UpperCAmelCase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
__lowerCAmelCase: int = self.training_tracker.add_child(len(UpperCAmelCase ) )
else:
__lowerCAmelCase: List[str] = NotebookProgressBar(len(UpperCAmelCase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : int ) -> Union[str, Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
__lowerCAmelCase: Any = None
def UpperCAmelCase ( self : str , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=None , **UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
__lowerCAmelCase: Union[str, Any] = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
__lowerCAmelCase: Dict = state.global_step
self.training_tracker.write_line(UpperCAmelCase )
def UpperCAmelCase ( self : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple=None , **UpperCAmelCase : int ) -> List[str]:
if self.training_tracker is not None:
__lowerCAmelCase: Dict = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
__lowerCAmelCase: List[str] = log['loss']
break
if self.first_column == "Epoch":
__lowerCAmelCase: int = int(state.epoch )
else:
__lowerCAmelCase: Tuple = state.global_step
__lowerCAmelCase: Optional[int] = 'eval'
for k in metrics:
if k.endswith('_loss' ):
__lowerCAmelCase: Union[str, Any] = re.sub(R'\_loss$' , '' , UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = metrics.pop('total_flos' , UpperCAmelCase )
__lowerCAmelCase: str = metrics.pop('epoch' , UpperCAmelCase )
__lowerCAmelCase: int = metrics.pop(F'''{metric_key_prefix}_runtime''' , UpperCAmelCase )
__lowerCAmelCase: List[Any] = metrics.pop(F'''{metric_key_prefix}_samples_per_second''' , UpperCAmelCase )
__lowerCAmelCase: List[str] = metrics.pop(F'''{metric_key_prefix}_steps_per_second''' , UpperCAmelCase )
__lowerCAmelCase: Tuple = metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''' , UpperCAmelCase )
for k, v in metrics.items():
if k == F'''{metric_key_prefix}_loss''':
__lowerCAmelCase: Tuple = v
else:
__lowerCAmelCase: int = k.split('_' )
__lowerCAmelCase: List[Any] = ' '.join([part.capitalize() for part in splits[1:]] )
__lowerCAmelCase: List[Any] = v
self.training_tracker.write_line(UpperCAmelCase )
self.training_tracker.remove_child()
__lowerCAmelCase: List[str] = None
# Evaluation takes a long time so we should force the next update.
__lowerCAmelCase: str = True
def UpperCAmelCase ( self : int , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ) -> Optional[int]:
self.training_tracker.update(
state.global_step , comment=F'''Epoch {int(state.epoch )}/{state.num_train_epochs}''' , force_update=UpperCAmelCase )
__lowerCAmelCase: Union[str, Any] = None
| 322 | 0 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=[] ):
lowerCamelCase_ = size[0] - overlap_pixels * 2
lowerCamelCase_ = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
lowerCamelCase_ = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_5_5
lowerCamelCase_ = np.pad(lowerCamelCase__ , mode="linear_ramp" , pad_width=lowerCamelCase__ , end_values=0 )
if "l" in remove_borders:
lowerCamelCase_ = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
lowerCamelCase_ = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
lowerCamelCase_ = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
lowerCamelCase_ = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return max(lowerCamelCase__ , min(lowerCamelCase__ , lowerCamelCase__ ) )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = list(lowerCamelCase__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
lowerCamelCase_ = clamp_rect(lowerCamelCase__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = Image.new("RGB" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(lowerCamelCase__ , (original_slice, 0) )
return result
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
lowerCamelCase_ = tile.crop(lowerCamelCase__ )
return tile
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = n % d
return n - divisor
class _SCREAMING_SNAKE_CASE ( snake_case__ ):
def __init__( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase = 350 , ) -> Optional[Any]:
super().__init__(
vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , unet=lowercase , low_res_scheduler=lowercase , scheduler=lowercase , max_noise_level=lowercase , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , **lowercase ) -> Optional[int]:
torch.manual_seed(0 )
lowerCamelCase_ = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
lowerCamelCase_ = add_overlap_rect(lowercase , lowercase , image.size )
lowerCamelCase_ = image.crop(lowercase )
lowerCamelCase_ = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
lowerCamelCase_ = translated_slice_x - (original_image_slice / 2)
lowerCamelCase_ = max(0 , lowercase )
lowerCamelCase_ = squeeze_tile(lowercase , lowercase , lowercase , lowercase )
lowerCamelCase_ = to_input.size
lowerCamelCase_ = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
lowerCamelCase_ = super(lowercase , self ).__call__(image=lowercase , **lowercase ).images[0]
lowerCamelCase_ = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
lowerCamelCase_ = unsqueeze_tile(lowercase , lowercase )
lowerCamelCase_ = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
lowerCamelCase_ = []
if x == 0:
remove_borders.append("l" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("r" )
if y == 0:
remove_borders.append("t" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("b" )
lowerCamelCase_ = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=lowercase ) , mode="L" , )
final_image.paste(
lowercase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , lowercase )
@torch.no_grad()
def __call__( self , lowercase , lowercase , lowercase = 75 , lowercase = 9.0 , lowercase = 50 , lowercase = None , lowercase = 1 , lowercase = 0.0 , lowercase = None , lowercase = None , lowercase = None , lowercase = 1 , lowercase = 128 , lowercase = 32 , lowercase = 32 , ) -> str:
lowerCamelCase_ = Image.new("RGB" , (image.size[0] * 4, image.size[1] * 4) )
lowerCamelCase_ = math.ceil(image.size[0] / tile_size )
lowerCamelCase_ = math.ceil(image.size[1] / tile_size )
lowerCamelCase_ = tcx * tcy
lowerCamelCase_ = 0
for y in range(lowercase ):
for x in range(lowercase ):
self._process_tile(
lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , prompt=lowercase , num_inference_steps=lowercase , guidance_scale=lowercase , noise_level=lowercase , negative_prompt=lowercase , num_images_per_prompt=lowercase , eta=lowercase , generator=lowercase , latents=lowercase , )
current_count += 1
if callback is not None:
callback({"progress": current_count / total_tile_count, "image": final_image} )
return final_image
def lowerCamelCase_ ( ):
lowerCamelCase_ = 'stabilityai/stable-diffusion-x4-upscaler'
lowerCamelCase_ = StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCamelCase__ , revision="fp16" , torch_dtype=torch.floataa )
lowerCamelCase_ = pipe.to("cuda" )
lowerCamelCase_ = Image.open("../../docs/source/imgs/diffusers_library.jpg" )
def callback(lowerCamelCase__ ):
print(F'progress: {obj["progress"]:.4f}' )
obj["image"].save("diffusers_library_progress.jpg" )
lowerCamelCase_ = pipe(image=lowerCamelCase__ , prompt="Black font, white background, vector" , noise_level=4_0 , callback=lowerCamelCase__ )
final_image.save("diffusers_library.jpg" )
if __name__ == "__main__":
main()
| 19 |
import os
from datetime import datetime as dt
from github import Github
_a = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def _a ( ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase: Dict = Github(os.environ['GITHUB_TOKEN'] )
__lowerCAmelCase: Tuple = g.get_repo('huggingface/accelerate' )
__lowerCAmelCase: str = repo.get_issues(state='open' )
for issue in open_issues:
__lowerCAmelCase: Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda SCREAMING_SNAKE_CASE : i.created_at , reverse=SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = comments[0] if len(SCREAMING_SNAKE_CASE ) > 0 else None
__lowerCAmelCase: Tuple = dt.utcnow()
__lowerCAmelCase: Optional[int] = (current_time - issue.updated_at).days
__lowerCAmelCase: str = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state='closed' )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
'This issue has been automatically marked as stale because it has not had '
'recent activity. If you think this still needs to be addressed '
'please comment on this thread.\n\nPlease note that issues that do not follow the '
'[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
'are likely to be ignored.' )
if __name__ == "__main__":
main()
| 322 | 0 |
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