code
stringlengths 86
54.5k
| code_codestyle
int64 0
371
| style_context
stringlengths 87
49.2k
| style_context_codestyle
int64 0
349
| label
int64 0
1
|
|---|---|---|---|---|
from __future__ import annotations
def A_ ( _UpperCAmelCase ):
if len(_UpperCAmelCase ) == 0:
return []
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] = min(_UpperCAmelCase ), max(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Dict = int(max_value - min_value ) + 1
SCREAMING_SNAKE_CASE_: list[list] = [[] for _ in range(_UpperCAmelCase )]
for i in my_list:
buckets[int(i - min_value )].append(_UpperCAmelCase )
return [v for bucket in buckets for v in sorted(_UpperCAmelCase )]
if __name__ == "__main__":
from doctest import testmod
testmod()
assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]
| 13 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : Dict = '''informer'''
__lowercase : Union[str, Any] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "student_t" , lowerCAmelCase__ = "nll" , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = "mean" , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , lowerCAmelCase__ = "gelu" , lowerCAmelCase__ = 0.05 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 1_0_0 , lowerCAmelCase__ = 0.02 , lowerCAmelCase__=True , lowerCAmelCase__ = "prob" , lowerCAmelCase__ = 5 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ):
# time series specific configuration
__SCREAMING_SNAKE_CASE = prediction_length
__SCREAMING_SNAKE_CASE = context_length or 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 if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
__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
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != 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]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase__) != 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(5_0 , (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
# Informer
__SCREAMING_SNAKE_CASE = attention_type
__SCREAMING_SNAKE_CASE = sampling_factor
__SCREAMING_SNAKE_CASE = distil
super().__init__(is_encoder_decoder=lowerCAmelCase__ , **lowerCAmelCase__)
@property
def snake_case_ ( self):
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
)
| 100 | 0 |
'''simple docstring'''
import numpy as np
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1e-12 , lowerCAmelCase = 1_00 , ):
"""simple docstring"""
assert np.shape(lowerCAmelCase )[0] == np.shape(lowerCAmelCase )[1]
# Ensure proper dimensionality.
assert np.shape(lowerCAmelCase )[0] == np.shape(lowerCAmelCase )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowerCAmelCase ) == np.iscomplexobj(lowerCAmelCase )
_lowerCAmelCase = np.iscomplexobj(lowerCAmelCase )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowerCAmelCase , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_lowerCAmelCase = False
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 1e12
while not convergence:
# Multiple matrix by the vector.
_lowerCAmelCase = np.dot(lowerCAmelCase , lowerCAmelCase )
# Normalize the resulting output vector.
_lowerCAmelCase = w / np.linalg.norm(lowerCAmelCase )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_lowerCAmelCase = vector.conj().T if is_complex else vector.T
_lowerCAmelCase = np.dot(lowerCAmelCase , np.dot(lowerCAmelCase , lowerCAmelCase ) )
# Check convergence.
_lowerCAmelCase = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_lowerCAmelCase = True
_lowerCAmelCase = lambda_
if is_complex:
_lowerCAmelCase = np.real(lambda_ )
return lambda_, vector
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_lowerCAmelCase = np.array([41, 4, 20] )
_lowerCAmelCase = real_input_matrix.astype(np.complexaaa )
_lowerCAmelCase = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_lowerCAmelCase = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_lowerCAmelCase = real_input_matrix
_lowerCAmelCase = real_vector
elif problem_type == "complex":
_lowerCAmelCase = complex_input_matrix
_lowerCAmelCase = complex_vector
# Our implementation.
_lowerCAmelCase , _lowerCAmelCase = power_iteration(lowerCAmelCase , lowerCAmelCase )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_lowerCAmelCase , _lowerCAmelCase = np.linalg.eigh(lowerCAmelCase )
# Last eigenvalue is the maximum one.
_lowerCAmelCase = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_lowerCAmelCase = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowerCAmelCase ) - np.abs(lowerCAmelCase ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 366 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A__ : List[str] =TypeVar('''T''')
class UpperCAmelCase ( Generic[T] ):
def __init__( self : Tuple , __snake_case : bool = True ) -> None:
_lowerCAmelCase = {} # dictionary of lists
_lowerCAmelCase = directed
def lowercase__ ( self : Union[str, Any] , __snake_case : T , __snake_case : T ) -> GraphAdjacencyList[T]:
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__snake_case )
self.adj_list[destination_vertex].append(__snake_case )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__snake_case )
_lowerCAmelCase = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(__snake_case )
_lowerCAmelCase = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
_lowerCAmelCase = [destination_vertex]
_lowerCAmelCase = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(__snake_case )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(__snake_case )
_lowerCAmelCase = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
_lowerCAmelCase = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
_lowerCAmelCase = [destination_vertex]
_lowerCAmelCase = []
return self
def __repr__( self : int ) -> str:
return pformat(self.adj_list )
| 220 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_A : int = logging.get_logger(__name__)
def UpperCamelCase_ ( snake_case_ : nn.ModuleList , snake_case_ : nn.ModuleList , snake_case_ : List[int] ) -> None:
'''simple docstring'''
__lowerCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(snake_case_ ) == len(snake_case_ ), f"""{len(snake_case_ )} != {len(snake_case_ )}"""
dest_layers.load_state_dict(layers_to_copy.state_dict() )
_A : Tuple = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_A : List[str] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def UpperCamelCase_ ( snake_case_ : List[Any] , snake_case_ : str ) -> Optional[Any]:
'''simple docstring'''
try:
__lowerCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f"""no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"""
f""" {n_student}""" )
return list(range(snake_case_ ) )
def UpperCamelCase_ ( snake_case_ : Dict , snake_case_ : str ) -> List[int]:
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(f"""Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}""" )
elif n_teacher == n_student:
return list(range(snake_case_ ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def UpperCamelCase_ ( snake_case_ : Union[str, PreTrainedModel] , snake_case_ : Union[str, Path] = "student" , snake_case_ : Union[int, None] = None , snake_case_ : Union[int, None] = None , snake_case_ : int=False , snake_case_ : Union[str, Any]=None , snake_case_ : Dict=None , **snake_case_ : Dict , ) -> Tuple[PreTrainedModel, List[int], List[int]]:
'''simple docstring'''
__lowerCAmelCase = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(snake_case_ , snake_case_ ):
AutoTokenizer.from_pretrained(snake_case_ ).save_pretrained(snake_case_ ) # purely for convenience
__lowerCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(snake_case_ ).eval()
else:
assert isinstance(snake_case_ , snake_case_ ), f"""teacher must be a model or string got type {type(snake_case_ )}"""
__lowerCAmelCase = teacher.config.to_diff_dict()
try:
__lowerCAmelCase , __lowerCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
__lowerCAmelCase = teacher_e
if d is None:
__lowerCAmelCase = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
__lowerCAmelCase , __lowerCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
__lowerCAmelCase , __lowerCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
__lowerCAmelCase = teacher_e
if d is None:
__lowerCAmelCase = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(snake_case_ )
# Copy weights
__lowerCAmelCase = teacher.config_class(**snake_case_ )
__lowerCAmelCase = AutoModelForSeqaSeqLM.from_config(snake_case_ )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
__lowerCAmelCase = student.load_state_dict(teacher.state_dict() , strict=snake_case_ )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
__lowerCAmelCase , __lowerCAmelCase = list(range(snake_case_ ) ), list(range(snake_case_ ) )
logger.info(
f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"""
f""" {save_path}""" )
student.save_pretrained(snake_case_ )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
__lowerCAmelCase = pick_layers_to_copy(snake_case_ , snake_case_ )
if d_layers_to_copy is None:
__lowerCAmelCase = pick_layers_to_copy(snake_case_ , snake_case_ )
try:
if hasattr(
snake_case_ , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , snake_case_ )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , snake_case_ )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , snake_case_ )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , snake_case_ )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , snake_case_ )
copy_layers(teacher.decoder.block , student.decoder.block , snake_case_ )
logger.info(
f"""Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}""" )
__lowerCAmelCase = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(snake_case_ )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 229 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNAKE_CASE : TreeNode | None = None
def UpperCamelCase_ ( snake_case_ : TreeNode | None ) -> bool:
'''simple docstring'''
def is_valid_tree(snake_case_ : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(snake_case_ , snake_case_ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(snake_case_ ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
snake_case_ : TreeNode | None , snake_case_ : float , snake_case_ : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , snake_case_ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , snake_case_ )
)
return is_binary_search_tree_recursive_check(snake_case_ , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 229 | 1 |
"""simple docstring"""
import argparse
import json
import subprocess
def lowercase__(A , A ) ->int:
"""simple docstring"""
lowercase__ : Optional[int]= []
lowercase__ : List[str]= (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
lowercase__ : Union[str, Any]= subprocess.run(A , shell=A , stdout=subprocess.PIPE )
lowercase__ : int= output.stdout.decode("utf-8" )
lowercase__ : str= json.loads(A )
lowercase__ : Tuple= status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(A )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(A ) )
if len(A ) > 0:
lowercase__ : Tuple= "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def lowercase__(A ) ->List[Any]:
"""simple docstring"""
return values.split("," )
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--target_runners""",
default=None,
type=list_str,
required=True,
help="""Comma-separated list of runners to check status.""",
)
parser.add_argument(
"""--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
)
a : str = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 352 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase__(A="ro" , A="en" , A="wmt16" , A=None ) ->None:
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
lowercase__ : int= f'''{src_lang}-{tgt_lang}'''
print(f'''Converting {dataset}-{pair}''' )
lowercase__ : List[Any]= datasets.load_dataset(A , A )
if save_dir is None:
lowercase__ : Union[str, Any]= f'''{dataset}-{pair}'''
lowercase__ : str= Path(A )
save_dir.mkdir(exist_ok=A )
for split in ds.keys():
print(f'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
lowercase__ : Any= "val" if split == "validation" else split
lowercase__ : List[Any]= save_dir.joinpath(f'''{fn}.source''' )
lowercase__ : Optional[Any]= save_dir.joinpath(f'''{fn}.target''' )
lowercase__ : Optional[int]= src_path.open("w+" )
lowercase__ : Any= tgt_path.open("w+" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
lowercase__ : int= x["translation"]
src_fp.write(ex[src_lang] + "\n" )
tgt_fp.write(ex[tgt_lang] + "\n" )
print(f'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 150 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] ):
"""simple docstring"""
if "cls_token" in name:
UpperCAmelCase_: Tuple = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
UpperCAmelCase_: int = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
UpperCAmelCase_: Union[str, Any] = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
UpperCAmelCase_: Union[str, Any] = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
UpperCAmelCase_: Optional[Any] = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
UpperCAmelCase_: List[str] = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
UpperCAmelCase_: Optional[int] = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
UpperCAmelCase_: Any = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
UpperCAmelCase_: Any = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
UpperCAmelCase_: List[Any] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
UpperCAmelCase_: List[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
UpperCAmelCase_: Tuple = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
UpperCAmelCase_: Optional[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCAmelCase_: List[str] = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
UpperCAmelCase_: str = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
UpperCAmelCase_: Optional[int] = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
UpperCAmelCase_: Any = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
UpperCAmelCase_: Dict = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
UpperCAmelCase_: Dict = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def lowerCAmelCase_ (lowerCAmelCase__: str , lowerCAmelCase__: Optional[Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
UpperCAmelCase_: str = orig_state_dict.pop(lowerCAmelCase__ )
if "qkv" in key:
UpperCAmelCase_: Tuple = key.split(""".""" )
UpperCAmelCase_: str = int(key_split[1] )
if "decoder_blocks" in key:
UpperCAmelCase_: int = config.decoder_hidden_size
UpperCAmelCase_: Union[str, Any] = """decoder.decoder_layers."""
if "weight" in key:
UpperCAmelCase_: str = val[:dim, :]
UpperCAmelCase_: Any = val[dim : dim * 2, :]
UpperCAmelCase_: List[Any] = val[-dim:, :]
elif "bias" in key:
UpperCAmelCase_: Tuple = val[:dim]
UpperCAmelCase_: Optional[int] = val[dim : dim * 2]
UpperCAmelCase_: Tuple = val[-dim:]
else:
UpperCAmelCase_: Optional[Any] = config.hidden_size
UpperCAmelCase_: Tuple = """vit.encoder.layer."""
if "weight" in key:
UpperCAmelCase_: Tuple = val[:dim, :]
UpperCAmelCase_: Optional[int] = val[dim : dim * 2, :]
UpperCAmelCase_: int = val[-dim:, :]
elif "bias" in key:
UpperCAmelCase_: Optional[int] = val[:dim]
UpperCAmelCase_: Tuple = val[dim : dim * 2]
UpperCAmelCase_: Any = val[-dim:]
else:
UpperCAmelCase_: Union[str, Any] = val
return orig_state_dict
def lowerCAmelCase_ (lowerCAmelCase__: Optional[Any] , lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: List[Any] = ViTMAEConfig()
if "large" in checkpoint_url:
UpperCAmelCase_: Optional[Any] = 1_0_2_4
UpperCAmelCase_: Union[str, Any] = 4_0_9_6
UpperCAmelCase_: List[str] = 2_4
UpperCAmelCase_: Dict = 1_6
elif "huge" in checkpoint_url:
UpperCAmelCase_: Optional[int] = 1_4
UpperCAmelCase_: Optional[int] = 1_2_8_0
UpperCAmelCase_: List[Any] = 5_1_2_0
UpperCAmelCase_: Tuple = 3_2
UpperCAmelCase_: Any = 1_6
UpperCAmelCase_: str = ViTMAEForPreTraining(lowerCAmelCase__ )
UpperCAmelCase_: Optional[Any] = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location="""cpu""" )["""model"""]
UpperCAmelCase_: Optional[Any] = ViTMAEImageProcessor(size=config.image_size )
UpperCAmelCase_: str = convert_state_dict(lowerCAmelCase__ , lowerCAmelCase__ )
model.load_state_dict(lowerCAmelCase__ )
model.eval()
UpperCAmelCase_: int = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
UpperCAmelCase_: Any = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw )
UpperCAmelCase_: Optional[Any] = ViTMAEImageProcessor(size=config.image_size )
UpperCAmelCase_: Optional[int] = image_processor(images=lowerCAmelCase__ , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
UpperCAmelCase_: str = model(**lowerCAmelCase__ )
UpperCAmelCase_: Tuple = outputs.logits
if "large" in checkpoint_url:
UpperCAmelCase_: List[Any] = torch.tensor(
[[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] )
elif "huge" in checkpoint_url:
UpperCAmelCase_: Any = torch.tensor(
[[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] )
else:
UpperCAmelCase_: Union[str, Any] = torch.tensor(
[[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , lowerCAmelCase__ , atol=1e-4 )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCAmelCase__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
a : Union[str, Any] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 147 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=13, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=37, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=10, SCREAMING_SNAKE_CASE_=0.0_2, SCREAMING_SNAKE_CASE_="divided_space_time", SCREAMING_SNAKE_CASE_=None, ) -> int:
UpperCAmelCase_: Union[str, Any] = parent
UpperCAmelCase_: str = batch_size
UpperCAmelCase_: str = image_size
UpperCAmelCase_: Optional[Any] = num_channels
UpperCAmelCase_: List[Any] = patch_size
UpperCAmelCase_: Optional[Any] = num_frames
UpperCAmelCase_: Any = is_training
UpperCAmelCase_: Union[str, Any] = use_labels
UpperCAmelCase_: Union[str, Any] = hidden_size
UpperCAmelCase_: Tuple = num_hidden_layers
UpperCAmelCase_: Optional[Any] = num_attention_heads
UpperCAmelCase_: Optional[Any] = intermediate_size
UpperCAmelCase_: Tuple = hidden_act
UpperCAmelCase_: Optional[int] = hidden_dropout_prob
UpperCAmelCase_: Tuple = attention_probs_dropout_prob
UpperCAmelCase_: Dict = attention_type
UpperCAmelCase_: List[Any] = initializer_range
UpperCAmelCase_: Union[str, Any] = scope
UpperCAmelCase_: List[Any] = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
UpperCAmelCase_: str = (image_size // patch_size) ** 2
UpperCAmelCase_: str = (num_frames) * self.num_patches_per_frame + 1
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: List[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_: List[str] = None
if self.use_labels:
UpperCAmelCase_: Optional[int] = ids_tensor([self.batch_size], self.num_labels )
UpperCAmelCase_: Tuple = self.get_config()
return config, pixel_values, labels
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: int = TimesformerConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_frames=self.num_frames, 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, initializer_range=self.initializer_range, attention_type=self.attention_type, )
UpperCAmelCase_: Tuple = self.num_labels
return config
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> int:
UpperCAmelCase_: Union[str, Any] = TimesformerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCAmelCase_: Tuple = TimesformerForVideoClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
# verify the logits shape
UpperCAmelCase_: Dict = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape, SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> int:
UpperCAmelCase_: Tuple = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_: Optional[Any] = config_and_inputs
UpperCAmelCase_: Union[str, Any] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _a ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
A = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
A = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
A = False
A = False
A = False
A = False
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: str = TimesformerModelTester(self )
UpperCAmelCase_: Optional[Any] = ConfigTester(
self, config_class=SCREAMING_SNAKE_CASE_, has_text_modality=SCREAMING_SNAKE_CASE_, hidden_size=37 )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=False ) -> Optional[Any]:
UpperCAmelCase_: Optional[Any] = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase_: List[str] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def __snake_case (self ) -> Optional[int]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def __snake_case (self ) -> Optional[int]:
pass
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_ , UpperCAmelCase_: Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_: Union[str, Any] = model_class(SCREAMING_SNAKE_CASE_ )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase_: Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_, nn.Linear ) )
def __snake_case (self ) -> Optional[Any]:
UpperCAmelCase_ , UpperCAmelCase_: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_: Tuple = model_class(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_: int = [*signature.parameters.keys()]
UpperCAmelCase_: Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1], SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> str:
UpperCAmelCase_: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Any:
UpperCAmelCase_: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def __snake_case (self ) -> Any:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_: Optional[Any] = TimesformerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Union[str, Any]:
if not self.has_attentions:
pass
else:
UpperCAmelCase_ , UpperCAmelCase_: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_: List[Any] = True
for model_class in self.all_model_classes:
UpperCAmelCase_: str = self.model_tester.seq_length
UpperCAmelCase_: Any = self.model_tester.num_frames
UpperCAmelCase_: Optional[int] = True
UpperCAmelCase_: int = False
UpperCAmelCase_: Optional[int] = True
UpperCAmelCase_: Any = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
UpperCAmelCase_: Any = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
UpperCAmelCase_: str = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ), self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_: Any = True
UpperCAmelCase_: Tuple = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
UpperCAmelCase_: Tuple = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
UpperCAmelCase_: Dict = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ), self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1], )
UpperCAmelCase_: Tuple = len(SCREAMING_SNAKE_CASE_ )
# Check attention is always last and order is fine
UpperCAmelCase_: Optional[int] = True
UpperCAmelCase_: str = True
UpperCAmelCase_: Union[str, Any] = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
UpperCAmelCase_: Any = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(out_len + 1, len(SCREAMING_SNAKE_CASE_ ) )
UpperCAmelCase_: Optional[Any] = outputs.attentions
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ), self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ), [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1], )
def __snake_case (self ) -> Dict:
def check_hidden_states_output(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase_: List[str] = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
with torch.no_grad():
UpperCAmelCase_: str = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) )
UpperCAmelCase_: str = outputs.hidden_states
UpperCAmelCase_: int = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ), SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [seq_length, self.model_tester.hidden_size], )
UpperCAmelCase_ , UpperCAmelCase_: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_: List[str] = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase_: Dict = True
check_hidden_states_output(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: Tuple = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
UpperCAmelCase_: str = np.load(lowerCAmelCase__ )
return list(lowerCAmelCase__ )
@require_torch
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def __snake_case (self ) -> Tuple:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __snake_case (self ) -> List[Any]:
UpperCAmelCase_: Optional[Any] = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[str] = self.default_image_processor
UpperCAmelCase_: List[Any] = prepare_video()
UpperCAmelCase_: Optional[Any] = image_processor(video[:8], return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
# forward pass
with torch.no_grad():
UpperCAmelCase_: Optional[int] = model(**SCREAMING_SNAKE_CASE_ )
# verify the logits
UpperCAmelCase_: Optional[int] = torch.Size((1, 400) )
self.assertEqual(outputs.logits.shape, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 ) )
| 147 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept negative values' )
__snake_case : Any = 0
__snake_case : str = str(UpperCAmelCase_ )
while len(UpperCAmelCase_ ) != 1:
__snake_case : Union[str, Any] = [int(UpperCAmelCase_ ) for i in num_string]
__snake_case : str = 1
for i in range(0 , len(UpperCAmelCase_ ) ):
total *= numbers[i]
__snake_case : Optional[Any] = str(UpperCAmelCase_ )
steps += 1
return steps
def __UpperCAmelCase ( UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('additive_persistence() only accepts integral values' )
if num < 0:
raise ValueError('additive_persistence() does not accept negative values' )
__snake_case : Dict = 0
__snake_case : Optional[int] = str(UpperCAmelCase_ )
while len(UpperCAmelCase_ ) != 1:
__snake_case : List[Any] = [int(UpperCAmelCase_ ) for i in num_string]
__snake_case : Union[str, Any] = 0
for i in range(0 , len(UpperCAmelCase_ ) ):
total += numbers[i]
__snake_case : Optional[int] = str(UpperCAmelCase_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 | """simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Union[str, Any] = b % a, a
return b
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
if gcd(UpperCAmelCase_ , UpperCAmelCase_ ) != 1:
__snake_case : Union[str, Any] = F"mod inverse of {a!r} and {m!r} does not exist"
raise ValueError(UpperCAmelCase_ )
__snake_case , __snake_case , __snake_case : List[str] = 1, 0, a
__snake_case , __snake_case , __snake_case : Dict = 0, 1, m
while va != 0:
__snake_case : List[str] = ua // va
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case : List[str] = (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
return ua % m
| 95 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __A = 4 ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase__ = abs(__A ) or 4
return [[1 + x + y * row_size for x in range(__A )] for y in range(__A )]
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
return reverse_row(transpose(__A ) )
# OR.. transpose(reverse_column(matrix))
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
return reverse_row(reverse_column(__A ) )
# OR.. reverse_column(reverse_row(matrix))
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
return reverse_column(transpose(__A ) )
# OR.. transpose(reverse_row(matrix))
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase__ = [list(__A ) for x in zip(*__A )]
return matrix
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase__ = matrix[::-1]
return matrix
def lowerCAmelCase_ ( __A ) -> list[list[int]]:
'''simple docstring'''
UpperCAmelCase__ = [x[::-1] for x in matrix]
return matrix
def lowerCAmelCase_ ( __A ) -> None:
'''simple docstring'''
for i in matrix:
print(*__A )
if __name__ == "__main__":
UpperCamelCase__ = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
UpperCamelCase__ = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
UpperCamelCase__ = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 65 |
def lowerCamelCase__ ( a ) -> bool:
_A: Dict = [int(a ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(a ) == 4 and all(0 <= int(a ) <= 2_54 for octet in octets )
if __name__ == "__main__":
UpperCAmelCase__ : str = input().strip()
UpperCAmelCase__ : Any = 'valid' if is_ip_va_address_valid(ip) else 'invalid'
print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 121 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A : Optional[Any] = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
A : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = ['NllbTokenizerFast']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 33 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def lowerCAmelCase ( _lowerCAmelCase : str ):
"""simple docstring"""
UpperCAmelCase__ = VideoMAEConfig()
set_architecture_configs(A_ , A_ )
if "finetuned" not in model_name:
UpperCAmelCase__ = False
if "finetuned" in model_name:
UpperCAmelCase__ = "huggingface/label-files"
if "kinetics" in model_name:
UpperCAmelCase__ = 400
UpperCAmelCase__ = "kinetics400-id2label.json"
elif "ssv2" in model_name:
UpperCAmelCase__ = 174
UpperCAmelCase__ = "something-something-v2-id2label.json"
else:
raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned." )
UpperCAmelCase__ = json.load(open(hf_hub_download(A_ , A_ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase__ = {int(A_ ): v for k, v in idalabel.items()}
UpperCAmelCase__ = idalabel
UpperCAmelCase__ = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Dict ):
"""simple docstring"""
if "small" in model_name:
UpperCAmelCase__ = 384
UpperCAmelCase__ = 1536
UpperCAmelCase__ = 12
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 3
UpperCAmelCase__ = 192
UpperCAmelCase__ = 768
elif "large" in model_name:
UpperCAmelCase__ = 1024
UpperCAmelCase__ = 4096
UpperCAmelCase__ = 24
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 8
UpperCAmelCase__ = 512
UpperCAmelCase__ = 2048
elif "huge" in model_name:
UpperCAmelCase__ = 1280
UpperCAmelCase__ = 5120
UpperCAmelCase__ = 32
UpperCAmelCase__ = 16
UpperCAmelCase__ = 12
UpperCAmelCase__ = 8
UpperCAmelCase__ = 640
UpperCAmelCase__ = 2560
elif "base" not in model_name:
raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\"" )
def lowerCAmelCase ( _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if "encoder." in name:
UpperCAmelCase__ = name.replace("encoder." , "" )
if "cls_token" in name:
UpperCAmelCase__ = name.replace("cls_token" , "videomae.embeddings.cls_token" )
if "decoder_pos_embed" in name:
UpperCAmelCase__ = name.replace("decoder_pos_embed" , "decoder.decoder_pos_embed" )
if "pos_embed" in name and "decoder" not in name:
UpperCAmelCase__ = name.replace("pos_embed" , "videomae.embeddings.position_embeddings" )
if "patch_embed.proj" in name:
UpperCAmelCase__ = name.replace("patch_embed.proj" , "videomae.embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
UpperCAmelCase__ = name.replace("patch_embed.norm" , "videomae.embeddings.norm" )
if "decoder.blocks" in name:
UpperCAmelCase__ = name.replace("decoder.blocks" , "decoder.decoder_layers" )
if "blocks" in name:
UpperCAmelCase__ = name.replace("blocks" , "videomae.encoder.layer" )
if "attn.proj" in name:
UpperCAmelCase__ = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name and "bias" not in name:
UpperCAmelCase__ = name.replace("attn" , "attention.self" )
if "attn" in name:
UpperCAmelCase__ = name.replace("attn" , "attention.attention" )
if "norm1" in name:
UpperCAmelCase__ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
UpperCAmelCase__ = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
UpperCAmelCase__ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
UpperCAmelCase__ = name.replace("mlp.fc2" , "output.dense" )
if "decoder_embed" in name:
UpperCAmelCase__ = name.replace("decoder_embed" , "decoder.decoder_embed" )
if "decoder_norm" in name:
UpperCAmelCase__ = name.replace("decoder_norm" , "decoder.decoder_norm" )
if "decoder_pred" in name:
UpperCAmelCase__ = name.replace("decoder_pred" , "decoder.decoder_pred" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
UpperCAmelCase__ = name.replace("norm.weight" , "videomae.layernorm.weight" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
UpperCAmelCase__ = name.replace("norm.bias" , "videomae.layernorm.bias" )
if "head" in name and "decoder" not in name:
UpperCAmelCase__ = name.replace("head" , "classifier" )
return name
def lowerCAmelCase ( _lowerCAmelCase : Dict , _lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
UpperCAmelCase__ = orig_state_dict.pop(A_ )
if key.startswith("encoder." ):
UpperCAmelCase__ = key.replace("encoder." , "" )
if "qkv" in key:
UpperCAmelCase__ = key.split("." )
if key.startswith("decoder.blocks" ):
UpperCAmelCase__ = config.decoder_hidden_size
UpperCAmelCase__ = int(key_split[2] )
UpperCAmelCase__ = "decoder.decoder_layers."
if "weight" in key:
UpperCAmelCase__ = val[:dim, :]
UpperCAmelCase__ = val[dim : dim * 2, :]
UpperCAmelCase__ = val[-dim:, :]
else:
UpperCAmelCase__ = config.hidden_size
UpperCAmelCase__ = int(key_split[1] )
UpperCAmelCase__ = "videomae.encoder.layer."
if "weight" in key:
UpperCAmelCase__ = val[:dim, :]
UpperCAmelCase__ = val[dim : dim * 2, :]
UpperCAmelCase__ = val[-dim:, :]
else:
UpperCAmelCase__ = val
return orig_state_dict
def lowerCAmelCase ( ):
"""simple docstring"""
UpperCAmelCase__ = hf_hub_download(
repo_id="hf-internal-testing/spaghetti-video" , filename="eating_spaghetti.npy" , repo_type="dataset" )
UpperCAmelCase__ = np.load(A_ )
return list(A_ )
def lowerCAmelCase ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
UpperCAmelCase__ = get_videomae_config(A_ )
if "finetuned" in model_name:
UpperCAmelCase__ = VideoMAEForVideoClassification(A_ )
else:
UpperCAmelCase__ = VideoMAEForPreTraining(A_ )
# download original checkpoint, hosted on Google Drive
UpperCAmelCase__ = "pytorch_model.bin"
gdown.cached_download(A_ , A_ , quiet=A_ )
UpperCAmelCase__ = torch.load(A_ , map_location="cpu" )
if "model" in files:
UpperCAmelCase__ = files["model"]
else:
UpperCAmelCase__ = files["module"]
UpperCAmelCase__ = convert_state_dict(A_ , A_ )
model.load_state_dict(A_ )
model.eval()
# verify model on basic input
UpperCAmelCase__ = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
UpperCAmelCase__ = prepare_video()
UpperCAmelCase__ = image_processor(A_ , return_tensors="pt" )
if "finetuned" not in model_name:
UpperCAmelCase__ = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos" , filename="bool_masked_pos.pt" )
UpperCAmelCase__ = torch.load(A_ )
UpperCAmelCase__ = model(**A_ )
UpperCAmelCase__ = outputs.logits
UpperCAmelCase__ = [
"videomae-small-finetuned-kinetics",
"videomae-small-finetuned-ssv2",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"videomae-base-short",
"videomae-base-short-finetuned-kinetics",
"videomae-base",
"videomae-base-finetuned-kinetics",
"videomae-large",
"videomae-large-finetuned-kinetics",
"videomae-huge-finetuned-kinetics",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"videomae-base-short-ssv2",
"videomae-base-short-finetuned-ssv2",
"videomae-base-ssv2",
"videomae-base-finetuned-ssv2",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([-0.9_291, -0.4_061, -0.9_307] )
elif model_name == "videomae-small-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([0.2_671, -0.4_689, -0.8_235] )
elif model_name == "videomae-base":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]] )
elif model_name == "videomae-base-short":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] )
# we verified the loss both for normalized and unnormalized targets for this one
UpperCAmelCase__ = torch.tensor([0.5_142] ) if config.norm_pix_loss else torch.tensor([0.6_469] )
elif model_name == "videomae-large":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]] )
elif model_name == "videomae-large-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.0_771, 0.0_011, -0.3_625] )
elif model_name == "videomae-huge-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.2_433, 0.1_632, -0.4_894] )
elif model_name == "videomae-base-short-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.6_588, 0.0_990, -0.2_493] )
elif model_name == "videomae-base-finetuned-kinetics":
UpperCAmelCase__ = torch.Size([1, 400] )
UpperCAmelCase__ = torch.tensor([0.3_669, -0.0_688, -0.2_421] )
elif model_name == "videomae-base-short-ssv2":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([-0.0_537, -0.1_539, -0.3_266] )
elif model_name == "videomae-base-ssv2":
UpperCAmelCase__ = torch.Size([1, 1408, 1536] )
UpperCAmelCase__ = torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]] )
elif model_name == "videomae-base-finetuned-ssv2":
UpperCAmelCase__ = torch.Size([1, 174] )
UpperCAmelCase__ = torch.tensor([0.1_961, -0.8_337, -0.6_389] )
else:
raise ValueError(F'''Model name not supported. Should be one of {model_names}''' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , A_ , atol=1E-4 )
else:
print("Logits:" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , A_ , atol=1E-4 )
print("Logits ok!" )
# verify loss, if applicable
if model_name == "videomae-base-short":
UpperCAmelCase__ = outputs.loss
assert torch.allclose(A_ , A_ , atol=1E-4 )
print("Loss ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(A_ )
model.save_pretrained(A_ )
if push_to_hub:
print("Pushing to the hub..." )
model.push_to_hub(A_ , organization="nielsr" )
if __name__ == "__main__":
_lowerCAmelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
type=str,
help=(
"URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"
" download link."
),
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="/Users/nielsrogge/Documents/VideoMAE/Test",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.")
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
_lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 169 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ = torch.nn.Linear(10 , 10 )
__magic_name__ = torch.optim.SGD(model.parameters() , 0.1 )
__magic_name__ = Accelerator()
__magic_name__ = accelerator.prepare(UpperCamelCase__ )
try:
pickle.loads(pickle.dumps(UpperCamelCase__ ) )
except Exception as e:
self.fail(F'''Accelerated optimizer pickling failed with {e}''' )
AcceleratorState._reset_state()
| 88 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 369 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
UpperCamelCase_ = None
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase_ = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
UpperCamelCase_ = {
"facebook/nllb-large-en-ro": 1_0_2_4,
"facebook/nllb-200-distilled-600M": 1_0_2_4,
}
# fmt: off
UpperCamelCase_ = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = VOCAB_FILES_NAMES
A : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Dict = PRETRAINED_VOCAB_FILES_MAP
A : Any = ['''input_ids''', '''attention_mask''']
A : Dict = NllbTokenizer
A : List[int] = []
A : List[int] = []
def __init__( self, A=None, A=None, A="<s>", A="</s>", A="</s>", A="<s>", A="<unk>", A="<pad>", A="<mask>", A=None, A=None, A=None, A=False, **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AddedToken(A, lstrip=A, rstrip=A ) if isinstance(A, A ) else mask_token
SCREAMING_SNAKE_CASE : Tuple = legacy_behaviour
super().__init__(
vocab_file=A, tokenizer_file=A, bos_token=A, eos_token=A, sep_token=A, cls_token=A, unk_token=A, pad_token=A, mask_token=A, src_lang=A, tgt_lang=A, additional_special_tokens=A, legacy_behaviour=A, **A, )
SCREAMING_SNAKE_CASE : Any = vocab_file
SCREAMING_SNAKE_CASE : Optional[int] = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE : List[str] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
SCREAMING_SNAKE_CASE : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(A ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE : Dict = src_lang if src_lang is not None else 'eng_Latn'
SCREAMING_SNAKE_CASE : Optional[Any] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE : List[str] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [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]
def UpperCamelCase_ ( self, A, A, A, A, **A ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang
SCREAMING_SNAKE_CASE : List[Any] = self(A, add_special_tokens=A, return_tensors=A, **A )
SCREAMING_SNAKE_CASE : Any = self.convert_tokens_to_ids(A )
SCREAMING_SNAKE_CASE : int = tgt_lang_id
return inputs
def UpperCamelCase_ ( self, A, A = "eng_Latn", A = None, A = "fra_Latn", **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang
SCREAMING_SNAKE_CASE : List[Any] = tgt_lang
return super().prepare_seqaseq_batch(A, A, **A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.convert_tokens_to_ids(A )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : List[Any] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE : Tuple = [self.cur_lang_code]
SCREAMING_SNAKE_CASE : List[Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.convert_tokens_to_ids(A )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE : str = [self.eos_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : List[str] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(A ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory." )
return
SCREAMING_SNAKE_CASE : int = os.path.join(
A, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file, A )
return (out_vocab_file,)
| 246 | 0 |
# Copyright 2021 The HuggingFace 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 argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
A : int = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowercase__ = _ask_options(
"""In which compute environment are you running?""" , ["""This machine""", """AWS (Amazon SageMaker)"""] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
lowercase__ = get_sagemaker_input()
else:
lowercase__ = get_cluster_input()
return config
def UpperCamelCase ( __magic_name__ : Optional[Any]=None ) -> int:
"""simple docstring"""
if subparsers is not None:
lowercase__ = subparsers.add_parser("""config""" , description=__magic_name__ )
else:
lowercase__ = argparse.ArgumentParser("""Accelerate config command""" , description=__magic_name__ )
parser.add_argument(
"""--config_file""" , default=__magic_name__ , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , )
if subparsers is not None:
parser.set_defaults(func=__magic_name__ )
return parser
def UpperCamelCase ( __magic_name__ : List[str] ) -> Dict:
"""simple docstring"""
lowercase__ = get_user_input()
if args.config_file is not None:
lowercase__ = args.config_file
else:
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
lowercase__ = default_yaml_config_file
if config_file.endswith(""".json""" ):
config.to_json_file(__magic_name__ )
else:
config.to_yaml_file(__magic_name__ )
print(f'''accelerate configuration saved at {config_file}''' )
def UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
lowercase__ = config_command_parser()
lowercase__ = parser.parse_args()
config_command(__magic_name__ )
if __name__ == "__main__":
main()
| 305 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__ )] )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , __magic_name__ ) ) , x.transpose() ) , __magic_name__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = (1, 2, 1)
lowercase__ = (1, 1, 0, 7)
lowercase__ = SARIMAX(
__magic_name__ , exog=__magic_name__ , order=__magic_name__ , seasonal_order=__magic_name__ )
lowercase__ = model.fit(disp=__magic_name__ , maxiter=600 , method="""nm""" )
lowercase__ = model_fit.predict(1 , len(__magic_name__ ) , exog=[test_match] )
return result[0]
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : list ) -> float:
"""simple docstring"""
lowercase__ = SVR(kernel="""rbf""" , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(__magic_name__ , __magic_name__ )
lowercase__ = regressor.predict(__magic_name__ )
return y_pred[0]
def UpperCamelCase ( __magic_name__ : list ) -> float:
"""simple docstring"""
train_user.sort()
lowercase__ = np.percentile(__magic_name__ , 25 )
lowercase__ = np.percentile(__magic_name__ , 75 )
lowercase__ = qa - qa
lowercase__ = qa - (iqr * 0.1)
return low_lim
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : float ) -> bool:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
for i in list_vote:
if i > actual_result:
lowercase__ = not_safe + 1
else:
if abs(abs(__magic_name__ ) - abs(__magic_name__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
A : Dict = [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
A : str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
A : Any = Normalizer().fit_transform(data_input_df.values)
# split data
A : Optional[int] = normalize_df[:, 2].tolist()
A : Any = normalize_df[:, 0].tolist()
A : str = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
A : int = normalize_df[:, [1, 2]].tolist()
A : Any = x[: len(x) - 1]
A : Tuple = x[len(x) - 1 :]
# for linear regression & sarimax
A : Optional[int] = total_date[: len(total_date) - 1]
A : Optional[int] = total_user[: len(total_user) - 1]
A : str = total_match[: len(total_match) - 1]
A : Union[str, Any] = total_date[len(total_date) - 1 :]
A : List[str] = total_user[len(total_user) - 1 :]
A : str = total_match[len(total_match) - 1 :]
# voting system with forecasting
A : int = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
A : int = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 305 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class UpperCAmelCase ( _a ):
_lowercase: "DiagonalGaussianDistribution"
class UpperCAmelCase ( _a , _a ):
_lowercase: List[str] = True
@register_to_config
def __init__( self : Optional[int] , __snake_case : int = 3 , __snake_case : int = 3 , __snake_case : Tuple[str] = ("DownEncoderBlock2D",) , __snake_case : Tuple[str] = ("UpDecoderBlock2D",) , __snake_case : Tuple[int] = (64,) , __snake_case : int = 1 , __snake_case : str = "silu" , __snake_case : int = 4 , __snake_case : int = 32 , __snake_case : int = 32 , __snake_case : float = 0.1_82_15 , ) -> List[Any]:
super().__init__()
# pass init params to Encoder
_lowerCAmelCase = Encoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , down_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , act_fn=__lowerCamelCase , norm_num_groups=__lowerCamelCase , double_z=__lowerCamelCase , )
# pass init params to Decoder
_lowerCAmelCase = Decoder(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , up_block_types=__lowerCamelCase , block_out_channels=__lowerCamelCase , layers_per_block=__lowerCamelCase , norm_num_groups=__lowerCamelCase , act_fn=__lowerCamelCase , )
_lowerCAmelCase = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
_lowerCAmelCase = nn.Convad(__lowerCamelCase , __lowerCamelCase , 1 )
_lowerCAmelCase = False
_lowerCAmelCase = False
# only relevant if vae tiling is enabled
_lowerCAmelCase = self.config.sample_size
_lowerCAmelCase = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
_lowerCAmelCase = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
_lowerCAmelCase = 0.25
def lowercase__ ( self : Dict , __snake_case : str , __snake_case : int=False ) -> Any:
if isinstance(__lowerCamelCase , (Encoder, Decoder) ):
_lowerCAmelCase = value
def lowercase__ ( self : str , __snake_case : bool = True ) -> Optional[Any]:
_lowerCAmelCase = use_tiling
def lowercase__ ( self : Optional[int] ) -> str:
self.enable_tiling(__lowerCamelCase )
def lowercase__ ( self : Union[str, Any] ) -> int:
_lowerCAmelCase = True
def lowercase__ ( self : Tuple ) -> List[str]:
_lowerCAmelCase = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def lowercase__ ( self : Any ) -> List[str]:
_lowerCAmelCase = {}
def fn_recursive_add_processors(__snake_case : str , __snake_case : torch.nn.Module , __snake_case : Dict[str, AttentionProcessor] ):
if hasattr(__lowerCamelCase , """set_processor""" ):
_lowerCAmelCase = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}" , __lowerCamelCase , __lowerCamelCase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return processors
def lowercase__ ( self : List[str] , __snake_case : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> Any:
_lowerCAmelCase = len(self.attn_processors.keys() )
if isinstance(__lowerCamelCase , __lowerCamelCase ) and len(__lowerCamelCase ) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(__lowerCamelCase )} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes." )
def fn_recursive_attn_processor(__snake_case : str , __snake_case : torch.nn.Module , __snake_case : Tuple ):
if hasattr(__lowerCamelCase , """set_processor""" ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
module.set_processor(__lowerCamelCase )
else:
module.set_processor(processor.pop(f"{name}.processor" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}" , __lowerCamelCase , __lowerCamelCase )
for name, module in self.named_children():
fn_recursive_attn_processor(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def lowercase__ ( self : Dict ) -> Optional[int]:
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def lowercase__ ( self : List[str] , __snake_case : torch.FloatTensor , __snake_case : bool = True ) -> Optional[Any]:
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(__lowerCamelCase , return_dict=__lowerCamelCase )
if self.use_slicing and x.shape[0] > 1:
_lowerCAmelCase = [self.encoder(__lowerCamelCase ) for x_slice in x.split(1 )]
_lowerCAmelCase = torch.cat(__lowerCamelCase )
else:
_lowerCAmelCase = self.encoder(__lowerCamelCase )
_lowerCAmelCase = self.quant_conv(__lowerCamelCase )
_lowerCAmelCase = DiagonalGaussianDistribution(__lowerCamelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowerCamelCase )
def lowercase__ ( self : int , __snake_case : torch.FloatTensor , __snake_case : bool = True ) -> Tuple:
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(__lowerCamelCase , return_dict=__lowerCamelCase )
_lowerCAmelCase = self.post_quant_conv(__lowerCamelCase )
_lowerCAmelCase = self.decoder(__lowerCamelCase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
@apply_forward_hook
def lowercase__ ( self : List[str] , __snake_case : torch.FloatTensor , __snake_case : bool = True ) -> List[str]:
if self.use_slicing and z.shape[0] > 1:
_lowerCAmelCase = [self._decode(__lowerCamelCase ).sample for z_slice in z.split(1 )]
_lowerCAmelCase = torch.cat(__lowerCamelCase )
else:
_lowerCAmelCase = self._decode(__lowerCamelCase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=__lowerCamelCase )
def lowercase__ ( self : Optional[Any] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> str:
_lowerCAmelCase = min(a.shape[2] , b.shape[2] , __lowerCamelCase )
for y in range(__lowerCamelCase ):
_lowerCAmelCase = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def lowercase__ ( self : Union[str, Any] , __snake_case : List[Any] , __snake_case : Dict , __snake_case : Optional[Any] ) -> Any:
_lowerCAmelCase = min(a.shape[3] , b.shape[3] , __lowerCamelCase )
for x in range(__lowerCamelCase ):
_lowerCAmelCase = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def lowercase__ ( self : str , __snake_case : torch.FloatTensor , __snake_case : bool = True ) -> Optional[int]:
_lowerCAmelCase = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
_lowerCAmelCase = int(self.tile_latent_min_size * self.tile_overlap_factor )
_lowerCAmelCase = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
_lowerCAmelCase = []
for i in range(0 , x.shape[2] , __lowerCamelCase ):
_lowerCAmelCase = []
for j in range(0 , x.shape[3] , __lowerCamelCase ):
_lowerCAmelCase = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
_lowerCAmelCase = self.encoder(__lowerCamelCase )
_lowerCAmelCase = self.quant_conv(__lowerCamelCase )
row.append(__lowerCamelCase )
rows.append(__lowerCamelCase )
_lowerCAmelCase = []
for i, row in enumerate(__lowerCamelCase ):
_lowerCAmelCase = []
for j, tile in enumerate(__lowerCamelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_lowerCAmelCase = self.blend_v(rows[i - 1][j] , __lowerCamelCase , __lowerCamelCase )
if j > 0:
_lowerCAmelCase = self.blend_h(row[j - 1] , __lowerCamelCase , __lowerCamelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowerCamelCase , dim=3 ) )
_lowerCAmelCase = torch.cat(__lowerCamelCase , dim=2 )
_lowerCAmelCase = DiagonalGaussianDistribution(__lowerCamelCase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=__lowerCamelCase )
def lowercase__ ( self : str , __snake_case : torch.FloatTensor , __snake_case : bool = True ) -> int:
_lowerCAmelCase = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
_lowerCAmelCase = int(self.tile_sample_min_size * self.tile_overlap_factor )
_lowerCAmelCase = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
_lowerCAmelCase = []
for i in range(0 , z.shape[2] , __lowerCamelCase ):
_lowerCAmelCase = []
for j in range(0 , z.shape[3] , __lowerCamelCase ):
_lowerCAmelCase = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
_lowerCAmelCase = self.post_quant_conv(__lowerCamelCase )
_lowerCAmelCase = self.decoder(__lowerCamelCase )
row.append(__lowerCamelCase )
rows.append(__lowerCamelCase )
_lowerCAmelCase = []
for i, row in enumerate(__lowerCamelCase ):
_lowerCAmelCase = []
for j, tile in enumerate(__lowerCamelCase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_lowerCAmelCase = self.blend_v(rows[i - 1][j] , __lowerCamelCase , __lowerCamelCase )
if j > 0:
_lowerCAmelCase = self.blend_h(row[j - 1] , __lowerCamelCase , __lowerCamelCase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(__lowerCamelCase , dim=3 ) )
_lowerCAmelCase = torch.cat(__lowerCamelCase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
def lowercase__ ( self : List[str] , __snake_case : torch.FloatTensor , __snake_case : bool = False , __snake_case : bool = True , __snake_case : Optional[torch.Generator] = None , ) -> Tuple:
_lowerCAmelCase = sample
_lowerCAmelCase = self.encode(__lowerCamelCase ).latent_dist
if sample_posterior:
_lowerCAmelCase = posterior.sample(generator=__lowerCamelCase )
else:
_lowerCAmelCase = posterior.mode()
_lowerCAmelCase = self.decode(__lowerCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=__lowerCamelCase )
| 368 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
def get_masked_lm_array(lowerCAmelCase ):
_lowerCAmelCase = f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
_lowerCAmelCase = tf.train.load_variable(lowerCAmelCase , lowerCAmelCase )
if "kernel" in name:
_lowerCAmelCase = array.transpose()
return torch.from_numpy(lowerCAmelCase )
def get_encoder_array(lowerCAmelCase ):
_lowerCAmelCase = f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
_lowerCAmelCase = tf.train.load_variable(lowerCAmelCase , lowerCAmelCase )
if "kernel" in name:
_lowerCAmelCase = array.transpose()
return torch.from_numpy(lowerCAmelCase )
def get_encoder_layer_array(lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
_lowerCAmelCase = tf.train.load_variable(lowerCAmelCase , lowerCAmelCase )
if "kernel" in name:
_lowerCAmelCase = array.transpose()
return torch.from_numpy(lowerCAmelCase )
def get_encoder_attention_layer_array(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
_lowerCAmelCase = tf.train.load_variable(lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = array.reshape(lowerCAmelCase )
if "kernel" in name:
_lowerCAmelCase = array.transpose()
return torch.from_numpy(lowerCAmelCase )
print(f"Loading model based on config from {config_path}..." )
_lowerCAmelCase = BertConfig.from_json_file(lowerCAmelCase )
_lowerCAmelCase = BertForMaskedLM(lowerCAmelCase )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
_lowerCAmelCase = model.bert.encoder.layer[layer_index]
# Self-attention
_lowerCAmelCase = layer.attention.self
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_query_dense/kernel""" , self_attn.query.weight.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_query_dense/bias""" , self_attn.query.bias.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_key_dense/kernel""" , self_attn.key.weight.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_key_dense/bias""" , self_attn.key.bias.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_value_dense/kernel""" , self_attn.value.weight.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_value_dense/bias""" , self_attn.value.bias.data.shape )
# Self-attention Output
_lowerCAmelCase = layer.attention.output
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_output_dense/kernel""" , self_output.dense.weight.data.shape )
_lowerCAmelCase = get_encoder_attention_layer_array(
lowerCAmelCase , """_output_dense/bias""" , self_output.dense.bias.data.shape )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_attention_layer_norm/gamma""" )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_attention_layer_norm/beta""" )
# Intermediate
_lowerCAmelCase = layer.intermediate
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_intermediate_dense/kernel""" )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_intermediate_dense/bias""" )
# Output
_lowerCAmelCase = layer.output
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_output_dense/kernel""" )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_output_dense/bias""" )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_output_layer_norm/gamma""" )
_lowerCAmelCase = get_encoder_layer_array(lowerCAmelCase , """_output_layer_norm/beta""" )
# Embeddings
_lowerCAmelCase = get_encoder_array("""_position_embedding_layer/embeddings""" )
_lowerCAmelCase = get_encoder_array("""_type_embedding_layer/embeddings""" )
_lowerCAmelCase = get_encoder_array("""_embedding_norm_layer/gamma""" )
_lowerCAmelCase = get_encoder_array("""_embedding_norm_layer/beta""" )
# LM Head
_lowerCAmelCase = model.cls.predictions.transform
_lowerCAmelCase = get_masked_lm_array("""dense/kernel""" )
_lowerCAmelCase = get_masked_lm_array("""dense/bias""" )
_lowerCAmelCase = get_masked_lm_array("""layer_norm/gamma""" )
_lowerCAmelCase = get_masked_lm_array("""layer_norm/beta""" )
_lowerCAmelCase = get_masked_lm_array("""embedding_table""" )
# Pooling
_lowerCAmelCase = BertPooler(config=lowerCAmelCase )
_lowerCAmelCase = get_encoder_array("""_pooler_layer/kernel""" )
_lowerCAmelCase = get_encoder_array("""_pooler_layer/bias""" )
# Export final model
model.save_pretrained(lowerCAmelCase )
# Integration test - should load without any errors ;)
_lowerCAmelCase = BertForMaskedLM.from_pretrained(lowerCAmelCase )
print(new_model.eval() )
print("""Model conversion was done sucessfully!""" )
if __name__ == "__main__":
A__ : str =argparse.ArgumentParser()
parser.add_argument(
'''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
type=str,
required=True,
help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''',
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
A__ : Dict =parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 220 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
),
}
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : List[str] = """audio-spectrogram-transformer"""
def __init__( self : List[Any] , __UpperCAmelCase : str=768 , __UpperCAmelCase : Dict=12 , __UpperCAmelCase : Optional[int]=12 , __UpperCAmelCase : Any=3072 , __UpperCAmelCase : List[Any]="gelu" , __UpperCAmelCase : Tuple=0.0 , __UpperCAmelCase : Any=0.0 , __UpperCAmelCase : List[str]=0.02 , __UpperCAmelCase : int=1e-12 , __UpperCAmelCase : str=16 , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : Any=10 , __UpperCAmelCase : List[Any]=10 , __UpperCAmelCase : List[str]=1024 , __UpperCAmelCase : str=128 , **__UpperCAmelCase : Dict , ):
super().__init__(**__UpperCAmelCase)
a : Any = hidden_size
a : Tuple = num_hidden_layers
a : Any = num_attention_heads
a : Optional[Any] = intermediate_size
a : str = hidden_act
a : Tuple = hidden_dropout_prob
a : Optional[int] = attention_probs_dropout_prob
a : Optional[int] = initializer_range
a : Any = layer_norm_eps
a : Optional[int] = patch_size
a : Optional[Any] = qkv_bias
a : Optional[Any] = frequency_stride
a : Optional[Any] = time_stride
a : Tuple = max_length
a : Optional[Any] = num_mel_bins
| 40 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__A = False
class A ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
def A__ ( self ) -> Any:
'''simple docstring'''
lowercase__ = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
lowercase__ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
lowercase__ = torch.manual_seed(0 )
lowercase__ = pipe(
image=lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
lowercase__ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowercase__ = np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 164 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self : Dict ,**lowerCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
super().__init__(**A__ )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self : List[Any] ,lowerCamelCase__ : Optional[int] ,**lowerCamelCase__ : List[str] ) -> str:
'''simple docstring'''
return super().__call__(A__ ,**A__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,**lowerCamelCase__ : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {}
if "candidate_labels" in kwargs:
SCREAMING_SNAKE_CASE = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
SCREAMING_SNAKE_CASE = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : str=None ,lowerCamelCase__ : Union[str, Any]="This is a photo of {}." ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_image(A__ )
SCREAMING_SNAKE_CASE = self.image_processor(images=[image] ,return_tensors=self.framework )
SCREAMING_SNAKE_CASE = candidate_labels
SCREAMING_SNAKE_CASE = [hypothesis_template.format(A__ ) for x in candidate_labels]
SCREAMING_SNAKE_CASE = self.tokenizer(A__ ,return_tensors=self.framework ,padding=A__ )
SCREAMING_SNAKE_CASE = [text_inputs]
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = model_inputs.pop("""candidate_labels""" )
SCREAMING_SNAKE_CASE = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] ,A__ ):
SCREAMING_SNAKE_CASE = text_inputs[0]
else:
# Batching case.
SCREAMING_SNAKE_CASE = text_inputs[0][0]
SCREAMING_SNAKE_CASE = self.model(**A__ ,**A__ )
SCREAMING_SNAKE_CASE = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_image,
}
return model_outputs
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : List[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = model_outputs.pop("""candidate_labels""" )
SCREAMING_SNAKE_CASE = model_outputs["""logits"""][0]
if self.framework == "pt":
SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE = probs.tolist()
if not isinstance(A__ ,A__ ):
SCREAMING_SNAKE_CASE = [scores]
elif self.framework == "tf":
SCREAMING_SNAKE_CASE = stable_softmax(A__ ,axis=-1 )
SCREAMING_SNAKE_CASE = probs.numpy().tolist()
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
SCREAMING_SNAKE_CASE = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(A__ ,A__ ) ,key=lambda lowerCamelCase__ : -x[0] )
]
return result
| 359 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = TypeVar("""DatasetType""", Dataset, IterableDataset)
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "first_exhausted" , ) -> DatasetType:
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("""Unable to interleave an empty list of datasets.""" )
for i, dataset in enumerate(_SCREAMING_SNAKE_CASE ):
if not isinstance(_SCREAMING_SNAKE_CASE , (Dataset, IterableDataset) ):
if isinstance(_SCREAMING_SNAKE_CASE , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
"""is an empty dataset dictionary.""" )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE ).__name__}.""" )
if i == 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else (IterableDataset, Dataset)
)
elif not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , info=_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , stopping_strategy=_SCREAMING_SNAKE_CASE )
else:
return _interleave_iterable_datasets(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , info=_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , stopping_strategy=_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 0 , ) -> DatasetType:
'''simple docstring'''
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(_SCREAMING_SNAKE_CASE ):
if not isinstance(_SCREAMING_SNAKE_CASE , (Dataset, IterableDataset) ):
if isinstance(_SCREAMING_SNAKE_CASE , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
"""is an empty dataset dictionary.""" )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(_SCREAMING_SNAKE_CASE )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_SCREAMING_SNAKE_CASE ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_SCREAMING_SNAKE_CASE ).__name__}.""" )
if i == 0:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else (IterableDataset, Dataset)
)
elif not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_SCREAMING_SNAKE_CASE , info=_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , axis=_SCREAMING_SNAKE_CASE )
else:
return _concatenate_iterable_datasets(_SCREAMING_SNAKE_CASE , info=_SCREAMING_SNAKE_CASE , split=_SCREAMING_SNAKE_CASE , axis=_SCREAMING_SNAKE_CASE )
| 193 | 0 |
'''simple docstring'''
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
raise ValueError('''Undefined for non-natural numbers''' )
A : Optional[int] = precision
A : str = ceil(precision / 14 )
A : List[str] = 42_6880 * Decimal(1_0005 ).sqrt()
A : List[str] = 1
A : Tuple = 1359_1409
A : List[Any] = Decimal(snake_case__ )
for k in range(1 , snake_case__ ):
A : List[Any] = factorial(6 * k ) // (factorial(3 * k ) * factorial(snake_case__ ) ** 3)
linear_term += 5_4514_0134
exponential_term *= -26_2537_4126_4076_8000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
lowercase : int = 50
print(f'''The first {n} digits of pi is: {pi(n)}''')
| 3 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = int(__A)
# Initialize Result
_a = []
# Traverse through all denomination
for denomination in reversed(__A):
# Find denominations
while int(__A) >= int(__A):
total_value -= int(__A)
answer.append(__A) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
lowercase_ = []
lowercase_ = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
lowercase_ = 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()))
lowercase_ = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
lowercase_ = [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
lowercase_ = 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}: """)
lowercase_ = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 211 | 0 |
import datasets
from .evaluate import evaluate
a__ : Dict = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
'''
a__ : List[str] = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''
a__ : List[Any] = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the CUAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
\'aupr\': Area Under the Precision-Recall curve
\'prec_at_80_recall\': Precision at 80% recall
\'prec_at_90_recall\': Precision at 90% recall
Examples:
>>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> cuad_metric = datasets.load_metric("cuad")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a_ ( datasets.Metric ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->Tuple:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://www.atticusprojectai.org/cuad'''] , reference_urls=['''https://www.atticusprojectai.org/cuad'''] , )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase ) ->Tuple:
SCREAMING_SNAKE_CASE : Any = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
SCREAMING_SNAKE_CASE : int = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
SCREAMING_SNAKE_CASE : Dict = evaluate(dataset=_lowerCamelCase , predictions=_lowerCamelCase )
return score
| 19 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Dict = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
a__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 19 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowercase__ : Any = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ ) -> Optional[Any]:
lowerCAmelCase = to_pil_image(__A )
lowerCAmelCase , lowerCAmelCase = pil_image.size
lowerCAmelCase = pytesseract.image_to_data(__A , lang=__A , output_type='''dict''' , config=__A )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
lowerCAmelCase = [idx for idx, word in enumerate(__A ) if not word.strip()]
lowerCAmelCase = [word for idx, word in enumerate(__A ) if idx not in irrelevant_indices]
lowerCAmelCase = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
lowerCAmelCase = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
lowerCAmelCase = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
lowerCAmelCase = [coord for idx, coord in enumerate(__A ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCAmelCase = []
for x, y, w, h in zip(__A , __A , __A , __A ):
lowerCAmelCase = [x, y, x + w, y + h]
actual_boxes.append(__A )
# finally, normalize the bounding boxes
lowerCAmelCase = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(__A , __A , __A ) )
assert len(__A ) == len(__A ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = ['pixel_values']
def __init__( self , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = 1 / 255 , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = "" , **__SCREAMING_SNAKE_CASE , ) ->None:
super().__init__(**__SCREAMING_SNAKE_CASE )
lowerCAmelCase = size if size is not None else {'''height''': 224, '''width''': 224}
lowerCAmelCase = get_size_dict(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = do_resize
lowerCAmelCase = size
lowerCAmelCase = resample
lowerCAmelCase = do_rescale
lowerCAmelCase = rescale_value
lowerCAmelCase = do_normalize
lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCAmelCase = apply_ocr
lowerCAmelCase = ocr_lang
lowerCAmelCase = tesseract_config
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ) ->np.ndarray:
lowerCAmelCase = get_size_dict(__SCREAMING_SNAKE_CASE )
if "height" not in size or "width" not in size:
raise ValueError(F"The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}" )
lowerCAmelCase = (size['''height'''], size['''width'''])
return resize(__SCREAMING_SNAKE_CASE , size=__SCREAMING_SNAKE_CASE , resample=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ) ->np.ndarray:
return rescale(__SCREAMING_SNAKE_CASE , scale=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ) ->np.ndarray:
return normalize(__SCREAMING_SNAKE_CASE , mean=__SCREAMING_SNAKE_CASE , std=__SCREAMING_SNAKE_CASE , data_format=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **__SCREAMING_SNAKE_CASE , ) ->PIL.Image.Image:
lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase = size if size is not None else self.size
lowerCAmelCase = get_size_dict(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = resample if resample is not None else self.resample
lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase = image_std if image_std is not None else self.image_std
lowerCAmelCase = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCAmelCase = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCAmelCase = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCAmelCase = make_list_of_images(__SCREAMING_SNAKE_CASE )
if not valid_images(__SCREAMING_SNAKE_CASE ):
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_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('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
lowerCAmelCase = [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
lowerCAmelCase = []
lowerCAmelCase = []
for image in images:
lowerCAmelCase , lowerCAmelCase = apply_tesseract(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
words_batch.append(__SCREAMING_SNAKE_CASE )
boxes_batch.append(__SCREAMING_SNAKE_CASE )
if do_resize:
lowerCAmelCase = [self.resize(image=__SCREAMING_SNAKE_CASE , size=__SCREAMING_SNAKE_CASE , resample=__SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
lowerCAmelCase = [self.rescale(image=__SCREAMING_SNAKE_CASE , scale=__SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
lowerCAmelCase = [self.normalize(image=__SCREAMING_SNAKE_CASE , mean=__SCREAMING_SNAKE_CASE , std=__SCREAMING_SNAKE_CASE ) for image in images]
lowerCAmelCase = [to_channel_dimension_format(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for image in images]
lowerCAmelCase = BatchFeature(data={'''pixel_values''': images} , tensor_type=__SCREAMING_SNAKE_CASE )
if apply_ocr:
lowerCAmelCase = words_batch
lowerCAmelCase = boxes_batch
return data
| 338 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = 'gptj'
__lowerCamelCase : List[str] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , A=50_400 , A=2_048 , A=4_096 , A=28 , A=16 , A=64 , A=None , A="gelu_new" , A=0.0 , A=0.0 , A=0.0 , A=1E-5 , A=0.02 , A=True , A=50_256 , A=50_256 , A=False , **A , ) -> Tuple:
"""simple docstring"""
_a = vocab_size
_a = n_positions
_a = n_embd
_a = n_layer
_a = n_head
_a = n_inner
_a = rotary_dim
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = use_cache
_a = bos_token_id
_a = eos_token_id
super().__init__(
bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A )
class __A ( A ):
'''simple docstring'''
def __init__(self , A , A = "default" , A = None , A = False , ) -> List[str]:
"""simple docstring"""
super().__init__(A , task=A , patching_specs=A , use_past=A )
if not getattr(self._config , '''pad_token_id''' , A ):
# TODO: how to do that better?
_a = 0
@property
def a__ (self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_a = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(A , direction='''inputs''' )
_a = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
_a = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def a__ (self ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def a__ (self ) -> int:
"""simple docstring"""
return self._config.n_head
def a__ (self , A , A = -1 , A = -1 , A = False , A = None , ) -> Mapping[str, Any]:
"""simple docstring"""
_a = super(A , self ).generate_dummy_inputs(
A , batch_size=A , seq_length=A , is_pair=A , framework=A )
# We need to order the input in the way they appears in the forward()
_a = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
_a , _a = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
_a = seqlen + 2
_a = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_a = [
(torch.zeros(A ), torch.zeros(A )) for _ in range(self.num_layers )
]
_a = common_inputs['''attention_mask''']
if self.use_past:
_a = ordered_inputs['''attention_mask'''].dtype
_a = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(A , A , dtype=A )] , dim=1 )
return ordered_inputs
@property
def a__ (self ) -> int:
"""simple docstring"""
return 13
| 211 | 0 |
"""simple docstring"""
def lowercase__ ( _UpperCAmelCase : Optional[Any] = 10 ) -> str:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or n < 0:
raise ValueError('Invalid input' )
lowercase : List[Any] = 10**n
lowercase : int = 2_84_33 * (pow(2 , 7_83_04_57 , _UpperCAmelCase )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f'''{solution(1_0) = }''')
| 368 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
_UpperCamelCase: Tuple = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class a__ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : int, **lowerCAmelCase : str ) -> Any:
super().__init__(**lowerCAmelCase )
requires_backends(self, 'vision' )
requires_backends(self, 'torch' )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
self.check_model_type(lowerCAmelCase )
def lowercase ( self : Optional[int], **lowerCAmelCase : int ) -> Tuple:
lowercase : List[Any] = {}
lowercase : List[str] = {}
lowercase : Optional[int] = {}
# preprocess args
if "points_per_batch" in kwargs:
lowercase : List[Any] = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
lowercase : Tuple = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
lowercase : Any = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
lowercase : Dict = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
lowercase : str = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
lowercase : List[str] = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
lowercase : List[str] = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
lowercase : str = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
lowercase : Optional[int] = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
lowercase : Dict = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
lowercase : int = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
lowercase : Union[str, Any] = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : List[Any], lowerCAmelCase : List[str], *lowerCAmelCase : Optional[Any], lowerCAmelCase : Dict=None, lowerCAmelCase : Union[str, Any]=None, **lowerCAmelCase : int ) -> List[str]:
return super().__call__(lowerCAmelCase, *lowerCAmelCase, num_workers=lowerCAmelCase, batch_size=lowerCAmelCase, **lowerCAmelCase )
def lowercase ( self : List[Any], lowerCAmelCase : Union[str, Any], lowerCAmelCase : Tuple=64, lowerCAmelCase : int = 0, lowerCAmelCase : float = 512 / 1500, lowerCAmelCase : Optional[int] = 32, lowerCAmelCase : Optional[int] = 1, ) -> Union[str, Any]:
lowercase : List[Any] = load_image(lowerCAmelCase )
lowercase : str = self.image_processor.size['longest_edge']
lowercase , lowercase , lowercase , lowercase : List[Any] = self.image_processor.generate_crop_boxes(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
lowercase : Any = self.image_processor(images=lowerCAmelCase, return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
lowercase : Optional[int] = self.get_inference_context()
with inference_context():
lowercase : List[str] = self._ensure_tensor_on_device(lowerCAmelCase, device=self.device )
lowercase : int = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
lowercase : List[Any] = image_embeddings
lowercase : Dict = grid_points.shape[1]
lowercase : Any = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0, lowerCAmelCase, lowerCAmelCase ):
lowercase : Optional[int] = grid_points[:, i : i + points_per_batch, :, :]
lowercase : List[str] = input_labels[:, i : i + points_per_batch]
lowercase : Optional[Any] = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def lowercase ( self : Any, lowerCAmelCase : List[str], lowerCAmelCase : str=0.88, lowerCAmelCase : Optional[int]=0.95, lowerCAmelCase : str=0, lowerCAmelCase : Optional[int]=1, ) -> Optional[int]:
lowercase : Optional[int] = model_inputs.pop('input_boxes' )
lowercase : Any = model_inputs.pop('is_last' )
lowercase : Tuple = model_inputs.pop('original_sizes' ).tolist()
lowercase : Union[str, Any] = model_inputs.pop('reshaped_input_sizes' ).tolist()
lowercase : str = self.model(**lowerCAmelCase )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
lowercase : str = model_outputs['pred_masks']
lowercase : str = self.image_processor.post_process_masks(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, binarize=lowerCAmelCase )
lowercase : Dict = model_outputs['iou_scores']
lowercase , lowercase , lowercase : int = self.image_processor.filter_masks(
masks[0], iou_scores[0], original_sizes[0], input_boxes[0], lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def lowercase ( self : Optional[Any], lowerCAmelCase : str, lowerCAmelCase : Tuple=False, lowerCAmelCase : Any=False, lowerCAmelCase : Tuple=0.7, ) -> List[str]:
lowercase : Any = []
lowercase : Optional[Any] = []
lowercase : Optional[Any] = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
lowercase : Optional[Any] = torch.cat(lowerCAmelCase )
lowercase : List[Any] = torch.cat(lowerCAmelCase )
lowercase , lowercase , lowercase , lowercase : str = self.image_processor.post_process_for_mask_generation(
lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
lowercase : str = defaultdict(lowerCAmelCase )
for output in model_outputs:
for k, v in output.items():
extra[k].append(lowerCAmelCase )
lowercase : Dict = {}
if output_rle_mask:
lowercase : Tuple = rle_mask
if output_bboxes_mask:
lowercase : Tuple = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 53 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
_lowerCamelCase : Union[str, Any] = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
_lowerCamelCase : List[str] = {
"""google/realm-cc-news-pretrained-embedder""": 512,
"""google/realm-cc-news-pretrained-encoder""": 512,
"""google/realm-cc-news-pretrained-scorer""": 512,
"""google/realm-cc-news-pretrained-openqa""": 512,
"""google/realm-orqa-nq-openqa""": 512,
"""google/realm-orqa-nq-reader""": 512,
"""google/realm-orqa-wq-openqa""": 512,
"""google/realm-orqa-wq-reader""": 512,
}
_lowerCamelCase : Dict = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = VOCAB_FILES_NAMES
UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ = RealmTokenizer
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Union[str, Any]=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Any="[UNK]" , UpperCAmelCase__ : List[str]="[SEP]" , UpperCAmelCase__ : Tuple="[PAD]" , UpperCAmelCase__ : str="[CLS]" , UpperCAmelCase__ : Dict="[MASK]" , UpperCAmelCase__ : str=True , UpperCAmelCase__ : Tuple=None , **UpperCAmelCase__ : List[str] , ) ->Union[str, Any]:
'''simple docstring'''
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , tokenize_chinese_chars=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ , **UpperCAmelCase__ , )
A__ = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get('''lowercase''' , UpperCAmelCase__) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCAmelCase__) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCAmelCase__) != tokenize_chinese_chars
):
A__ = getattr(UpperCAmelCase__ , normalizer_state.pop('''type'''))
A__ = do_lower_case
A__ = strip_accents
A__ = tokenize_chinese_chars
A__ = normalizer_class(**UpperCAmelCase__)
A__ = do_lower_case
def SCREAMING_SNAKE_CASE ( self : int , UpperCAmelCase__ : Dict , **UpperCAmelCase__ : int) ->str:
'''simple docstring'''
A__ = PaddingStrategy.MAX_LENGTH
A__ = text
A__ = kwargs.pop('''text_pair''' , UpperCAmelCase__)
A__ = kwargs.pop('''return_tensors''' , UpperCAmelCase__)
A__ = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(UpperCAmelCase__):
if batch_text_pair is not None:
A__ = batch_text_pair[idx]
else:
A__ = None
A__ = super().__call__(UpperCAmelCase__ , UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__)
A__ = encoded_candidates.get('''input_ids''')
A__ = encoded_candidates.get('''attention_mask''')
A__ = encoded_candidates.get('''token_type_ids''')
if encoded_input_ids is not None:
output_data["input_ids"].append(UpperCAmelCase__)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(UpperCAmelCase__)
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(UpperCAmelCase__)
A__ = {key: item for key, item in output_data.items() if len(UpperCAmelCase__) != 0}
return BatchEncoding(UpperCAmelCase__ , tensor_type=UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[Any]=None) ->str:
'''simple docstring'''
A__ = [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 SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None) ->List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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 SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None) ->Tuple[str]:
'''simple docstring'''
A__ = self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__)
return tuple(UpperCAmelCase__)
| 14 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A__ ( __UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
__A : Optional[int] = ShapEImgaImgPipeline
__A : Tuple = ['''image''']
__A : Any = ['''image''']
__A : Optional[Any] = [
'''num_images_per_prompt''',
'''num_inference_steps''',
'''generator''',
'''latents''',
'''guidance_scale''',
'''frame_size''',
'''output_type''',
'''return_dict''',
]
__A : Dict = False
@property
def __lowercase ( self) -> Any:
'''simple docstring'''
return 32
@property
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
return 32
@property
def __lowercase ( self) -> Optional[Any]:
'''simple docstring'''
return self.time_input_dim * 4
@property
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
return 8
@property
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0)
a__ : Any = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
a__ : Dict = CLIPVisionModel(lowercase)
return model
@property
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : str = CLIPImageProcessor(
crop_size=224 , do_center_crop=lowercase , do_normalize=lowercase , do_resize=lowercase , 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] , resample=3 , size=224 , )
return image_processor
@property
def __lowercase ( self) -> str:
'''simple docstring'''
torch.manual_seed(0)
a__ : str = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
a__ : Any = PriorTransformer(**lowercase)
return model
@property
def __lowercase ( self) -> Any:
'''simple docstring'''
torch.manual_seed(0)
a__ : List[Any] = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
a__ : List[str] = ShapERenderer(**lowercase)
return model
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Dict = self.dummy_prior
a__ : List[str] = self.dummy_image_encoder
a__ : int = self.dummy_image_processor
a__ : str = self.dummy_renderer
a__ : Optional[int] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=lowercase , clip_sample=lowercase , clip_sample_range=1.0 , )
a__ : List[Any] = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowercase ( self , lowercase , lowercase=0) -> List[str]:
'''simple docstring'''
a__ : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase)).to(lowercase)
if str(lowercase).startswith('mps'):
a__ : List[str] = torch.manual_seed(lowercase)
else:
a__ : str = torch.Generator(device=lowercase).manual_seed(lowercase)
a__ : Tuple = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowercase ( self) -> Any:
'''simple docstring'''
a__ : int = 'cpu'
a__ : List[str] = self.get_dummy_components()
a__ : Dict = self.pipeline_class(**lowercase)
a__ : Optional[int] = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__ : Tuple = pipe(**self.get_dummy_inputs(lowercase))
a__ : Any = output.images[0]
a__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
a__ : List[str] = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def __lowercase ( self) -> Any:
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def __lowercase ( self) -> Union[str, Any]:
'''simple docstring'''
a__ : str = torch_device == 'cpu'
a__ : Tuple = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowercase , relax_max_difference=lowercase , )
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : List[str] = self.get_dummy_components()
a__ : str = self.pipeline_class(**lowercase)
a__ : List[str] = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__ : Optional[Any] = 1
a__ : List[str] = 2
a__ : Optional[Any] = self.get_dummy_inputs(lowercase)
for key in inputs.keys():
if key in self.batch_params:
a__ : Any = batch_size * [inputs[key]]
a__ : int = pipe(**lowercase , num_images_per_prompt=lowercase)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : str = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
a__ : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy')
a__ : List[str] = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
a__ : Tuple = pipe.to(lowercase)
pipe.set_progress_bar_config(disable=lowercase)
a__ : List[Any] = torch.Generator(device=lowercase).manual_seed(0)
a__ : Optional[int] = pipe(
lowercase , generator=lowercase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowercase , lowercase)
| 99 | 0 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 0
for ch in input_str:
SCREAMING_SNAKE_CASE__ = ord(_A )
SCREAMING_SNAKE_CASE__ = pow(2 , _A )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 218 |
import comet # From: unbabel-comet
import torch
import datasets
_SCREAMING_SNAKE_CASE : List[str] = datasets.logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Any = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
_SCREAMING_SNAKE_CASE : Optional[Any] = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
_SCREAMING_SNAKE_CASE : str = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowercase_ ( self : List[Any] ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def lowercase_ ( self : List[Any] , __lowerCamelCase : Dict ) -> Tuple:
if self.config_name == "default":
SCREAMING_SNAKE_CASE__ = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
SCREAMING_SNAKE_CASE__ = comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def lowercase_ ( self : int , __lowerCamelCase : List[str] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Dict=None , __lowerCamelCase : Optional[int]=False ) -> str:
if gpus is None:
SCREAMING_SNAKE_CASE__ = 1 if torch.cuda.is_available() else 0
SCREAMING_SNAKE_CASE__ = {'''src''': sources, '''mt''': predictions, '''ref''': references}
SCREAMING_SNAKE_CASE__ = [dict(zip(__lowerCamelCase , __lowerCamelCase ) ) for t in zip(*data.values() )]
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = self.scorer.predict(__lowerCamelCase , gpus=__lowerCamelCase , progress_bar=__lowerCamelCase )
return {"mean_score": mean_score, "scores": scores}
| 218 | 1 |
import functools
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase, _UpperCAmelCase ) or not all(isinstance(_UpperCAmelCase, _UpperCAmelCase ) for day in days ):
raise ValueError('The parameter days should be a list of integers' )
if len(_UpperCAmelCase ) != 3 or not all(isinstance(_UpperCAmelCase, _UpperCAmelCase ) for cost in costs ):
raise ValueError('The parameter costs should be a list of three integers' )
if len(_UpperCAmelCase ) == 0:
return 0
if min(_UpperCAmelCase ) <= 0:
raise ValueError('All days elements should be greater than 0' )
if max(_UpperCAmelCase ) >= 366:
raise ValueError('All days elements should be less than 366' )
lowerCAmelCase : List[str] = set(_UpperCAmelCase )
@functools.cache
def dynamic_programming(_UpperCAmelCase ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ), costs[1] + dynamic_programming(index + 7 ), costs[2] + dynamic_programming(index + 30 ), )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 138 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1 or len(SCREAMING_SNAKE_CASE ) <= key:
return input_string
for position, character in enumerate(SCREAMING_SNAKE_CASE ):
__UpperCAmelCase = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase = min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [''''''.join(SCREAMING_SNAKE_CASE ) for row in temp_grid]
__UpperCAmelCase = ''''''.join(SCREAMING_SNAKE_CASE )
return output_string
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = []
__UpperCAmelCase = key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1:
return input_string
__UpperCAmelCase = [[] for _ in range(SCREAMING_SNAKE_CASE )] # generates template
for position in range(len(SCREAMING_SNAKE_CASE ) ):
__UpperCAmelCase = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase = min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('''*''' )
__UpperCAmelCase = 0
for row in temp_grid: # fills in the characters
__UpperCAmelCase = input_string[counter : counter + len(SCREAMING_SNAKE_CASE )]
grid.append(list(SCREAMING_SNAKE_CASE ) )
counter += len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = '''''' # reads as zigzag
for position in range(len(SCREAMING_SNAKE_CASE ) ):
__UpperCAmelCase = position % (lowest * 2) # puts it in bounds
__UpperCAmelCase = min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def __a ( SCREAMING_SNAKE_CASE ) -> dict[int, str]:
'''simple docstring'''
__UpperCAmelCase = {}
for key_guess in range(1 , len(SCREAMING_SNAKE_CASE ) ): # tries every key
__UpperCAmelCase = decrypt(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 333 | 0 |
A_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
A_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def UpperCamelCase (lowercase_: float , lowercase_: str , lowercase_: str ) -> float:
if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
A__ : List[str] = (
f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
f"""Valid values are: {', '.join(lowercase_ )}"""
)
raise ValueError(lowercase_ )
return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to] , 3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _a :
'''simple docstring'''
def __init__( self , A__ , ):
A__ : Any = parent
A__ : Any = 13
A__ : Optional[Any] = 7
A__ : Union[str, Any] = 30
A__ : str = self.seq_length + self.mem_len
A__ : Dict = 15
A__ : int = True
A__ : Tuple = True
A__ : Union[str, Any] = 99
A__ : Optional[Any] = [10, 50, 80]
A__ : str = 32
A__ : Tuple = 32
A__ : Union[str, Any] = 4
A__ : Optional[Any] = 8
A__ : int = 128
A__ : List[Any] = 2
A__ : List[str] = 2
A__ : int = None
A__ : List[str] = 1
A__ : Union[str, Any] = 0
A__ : List[str] = 3
A__ : int = self.vocab_size - 1
A__ : Optional[Any] = 0.0_1
def __A ( self ):
A__ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Optional[Any] = None
if self.use_labels:
A__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Any = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def __A ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Dict = TFTransfoXLModel(A__ )
A__ , A__ : Tuple = model(A__ ).to_tuple()
A__ : List[str] = {"""input_ids""": input_ids_a, """mems""": mems_a}
A__ , A__ : str = model(A__ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Optional[int] = TFTransfoXLLMHeadModel(A__ )
A__ , A__ : int = model(A__ ).to_tuple()
A__ : int = {"""input_ids""": input_ids_a, """labels""": lm_labels}
A__ , A__ : Optional[Any] = model(A__ ).to_tuple()
A__ , A__ : Union[str, Any] = model([input_ids_a, mems_a] ).to_tuple()
A__ : Any = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
A__ , A__ : Tuple = model(A__ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __A ( self , A__ , A__ , A__ , A__ ):
A__ : Any = TFTransfoXLForSequenceClassification(A__ )
A__ : Optional[Any] = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A ( self ):
A__ : Optional[Any] = self.prepare_config_and_inputs()
((A__) , (A__) , (A__) , (A__)) : List[Any] = config_and_inputs
A__ : int = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class _a (__magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__: List[Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
UpperCAmelCase__: Optional[Any] = () if is_tf_available() else ()
UpperCAmelCase__: int = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
UpperCAmelCase__: Optional[int] = False
UpperCAmelCase__: Optional[int] = False
UpperCAmelCase__: Tuple = False
UpperCAmelCase__: List[str] = False
def __A ( self , A__ , A__ , A__ , A__ , A__ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __A ( self ):
A__ : Tuple = TFTransfoXLModelTester(self )
A__ : List[Any] = ConfigTester(self , config_class=A__ , d_embed=37 )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
self.model_tester.set_seed()
A__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*A__ )
def __A ( self ):
self.model_tester.set_seed()
A__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*A__ )
def __A ( self ):
A__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A__ )
def __A ( self ):
A__ , A__ : int = self.model_tester.prepare_config_and_inputs_for_common()
A__ : Union[str, Any] = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
A__ : Any = model_class(A__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
A__ : Optional[Any] = model.get_output_embeddings()
assert isinstance(A__ , tf.keras.layers.Layer )
A__ : Tuple = model.get_bias()
assert name is None
else:
A__ : Dict = model.get_output_embeddings()
assert x is None
A__ : int = model.get_bias()
assert name is None
def __A ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def __A ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ : List[Any] = TFTransfoXLModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def __A ( self ):
pass
@require_tf
class _a (unittest.TestCase ):
'''simple docstring'''
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def __A ( self ):
A__ : List[Any] = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
A__ : Tuple = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
A__ : Dict = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
A__ : Any = model.generate(A__ , max_length=200 , do_sample=A__ )
self.assertListEqual(output_ids[0].numpy().tolist() , A__ )
| 141 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( lowerCamelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = str(lowerCamelCase )
UpperCAmelCase__ = [n]
for i in range(1 , len(lowerCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def a_ ( lowerCamelCase ):
if len(str(lowerCamelCase ) ) > 3:
if not is_prime(int(str(lowerCamelCase )[-3:] ) ) or not is_prime(int(str(lowerCamelCase )[:3] ) ):
return False
return True
def a_ ( lowerCamelCase = 1_1 ):
UpperCAmelCase__ = []
UpperCAmelCase__ = 1_3
while len(lowerCamelCase ) != count:
if validate(lowerCamelCase ):
UpperCAmelCase__ = list_truncated_nums(lowerCamelCase )
if all(is_prime(lowerCamelCase ) for i in list_nums ):
list_truncated_primes.append(lowerCamelCase )
num += 2
return list_truncated_primes
def a_ ( ):
return sum(compute_truncated_primes(1_1 ) )
if __name__ == "__main__":
print(F"""{sum(compute_truncated_primes(11)) = }""")
| 98 | '''simple docstring'''
# Copyright 2021 The HuggingFace 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 warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : Optional[Any] , lowercase_ : str ):
lowercase_ : int = data
def __iter__( self : int ):
for element in self.data:
yield element
def lowerCamelCase ( UpperCAmelCase__ : Any=True ) -> Any:
lowercase_ : Optional[int] = Accelerator(even_batches=UpperCAmelCase__ )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : bool = False ) -> Optional[Any]:
if iterable:
lowercase_ : Dict = DummyIterableDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
else:
lowercase_ : Union[str, Any] = TensorDataset(torch.as_tensor(range(UpperCAmelCase__ ) ) )
lowercase_ : Any = DataLoader(UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : Any = accelerator.prepare(UpperCAmelCase__ )
return dl
def lowerCamelCase ( UpperCAmelCase__ : Accelerator , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : List[int] , ) -> int:
lowercase_ : List[str] = create_dataloader(accelerator=UpperCAmelCase__ , dataset_size=UpperCAmelCase__ , batch_size=UpperCAmelCase__ )
lowercase_ : Tuple = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def lowerCamelCase ( ) -> int:
lowercase_ : Tuple = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def lowerCamelCase ( ) -> Optional[int]:
lowercase_ : Optional[int] = create_accelerator(even_batches=UpperCAmelCase__ )
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
UpperCAmelCase__ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def lowerCamelCase ( ) -> List[str]:
lowercase_ : str = create_accelerator(even_batches=UpperCAmelCase__ )
lowercase_ : Dict = torch.nn.Linear(1 , 1 )
lowercase_ : Optional[Any] = accelerator.prepare(UpperCAmelCase__ )
lowercase_ : List[Any] = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
lowercase_ : Optional[Any] = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(UpperCAmelCase__ ):
lowercase_ : Any = ddp_model(batch[0].float() )
lowercase_ : List[str] = output.sum()
loss.backward()
batch_idxs.append(UpperCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def lowerCamelCase ( UpperCAmelCase__ : List[str] ) -> List[str]:
with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , UpperCAmelCase__ )
assert "only supported for multi-GPU" in str(w[-1].message )
def lowerCamelCase ( ) -> Any:
lowercase_ : str = True
lowercase_ : Tuple = False
lowercase_ : str = create_accelerator(even_batches=UpperCAmelCase__ )
lowercase_ : Union[str, Any] = torch.nn.Linear(1 , 1 )
lowercase_ : Any = accelerator.prepare(UpperCAmelCase__ )
lowercase_ : Optional[int] = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
lowercase_ : List[Any] = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
lowercase_ : Union[str, Any] = train_dl.batch_sampler.even_batches
lowercase_ : List[str] = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def lowerCamelCase ( ) -> Dict:
lowercase_ : str = True
lowercase_ : Optional[Any] = False
lowercase_ : Union[str, Any] = create_accelerator(even_batches=UpperCAmelCase__ )
lowercase_ : Optional[int] = torch.nn.Linear(1 , 1 )
lowercase_ : Optional[int] = accelerator.prepare(UpperCAmelCase__ )
create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=UpperCAmelCase__ )
lowercase_ : List[str] = create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings("""ignore""" )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
lowercase_ : Optional[Any] = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def lowerCamelCase ( ) -> List[Any]:
lowercase_ : Optional[Any] = create_accelerator()
lowercase_ : Optional[int] = torch.nn.Linear(1 , 1 )
lowercase_ : List[Any] = accelerator.prepare(UpperCAmelCase__ )
create_dataloader(UpperCAmelCase__ , dataset_size=3 , batch_size=1 , iterable=UpperCAmelCase__ )
with warnings.catch_warnings(record=UpperCAmelCase__ ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=UpperCAmelCase__ ):
pass
assert issubclass(w[-1].category , UpperCAmelCase__ )
assert "only supported for map-style datasets" in str(w[-1].message )
def lowerCamelCase ( ) -> List[str]:
lowercase_ : List[Any] = create_accelerator()
accelerator.print("""Test that even_batches variable ensures uniform batches across processes""" )
test_default_ensures_even_batch_sizes()
accelerator.print("""Run tests with even_batches disabled""" )
test_can_disable_even_batches()
accelerator.print("""Test joining uneven inputs""" )
test_can_join_uneven_inputs()
accelerator.print("""Test overriding even_batches when joining uneven inputs""" )
test_join_can_override_even_batches()
accelerator.print("""Test overriding even_batches for mixed dataloader types""" )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders""" )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print("""Test join with non DDP distributed raises warning""" )
lowercase_ : List[Any] = accelerator.state.distributed_type
lowercase_ : Union[str, Any] = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(UpperCAmelCase__ )
lowercase_ : str = original_state
if __name__ == "__main__":
main()
| 239 | 0 |
'''simple docstring'''
import itertools
import math
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase ( ):
"""simple docstring"""
__magic_name__ : Optional[int] = 2
while True:
if is_prime(lowerCAmelCase ):
yield num
num += 1
def lowerCamelCase ( lowerCAmelCase : int = 1_0001 ):
"""simple docstring"""
return next(itertools.islice(prime_generator() , nth - 1 , lowerCAmelCase ) )
if __name__ == "__main__":
print(F'{solution() = }') | 275 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowerCAmelCase :str = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase :int = '''
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
'''
def lowerCamelCase ( lowerCAmelCase : Optional[int] , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : List[Any]=8 ):
"""simple docstring"""
__magic_name__ : List[str] = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
__magic_name__ : str = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , _A : MultilingualCLIP , _A : XLMRobertaTokenizer , _A : UNetaDConditionModel , _A : Union[DDIMScheduler, DDPMScheduler] , _A : VQModel , ) -> int:
super().__init__()
self.register_modules(
text_encoder=_A , tokenizer=_A , unet=_A , scheduler=_A , movq=_A , )
__magic_name__ : List[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowerCAmelCase ( self : List[Any] , _A : Tuple , _A : Optional[Any] , _A : Optional[int] , _A : Dict , _A : str , _A : List[str] ) -> str:
if latents is None:
__magic_name__ : Any = randn_tensor(_A , generator=_A , device=_A , dtype=_A )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
__magic_name__ : int = latents.to(_A )
__magic_name__ : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def __lowerCAmelCase ( self : List[Any] , _A : List[str] , _A : List[str] , _A : List[str] , _A : List[Any] , _A : str=None , ) -> Dict:
__magic_name__ : Optional[Any] = len(_A ) if isinstance(_A , _A ) else 1
# get prompt text embeddings
__magic_name__ : str = self.tokenizer(
_A , padding='max_length' , truncation=_A , max_length=77 , return_attention_mask=_A , add_special_tokens=_A , return_tensors='pt' , )
__magic_name__ : Optional[Any] = text_inputs.input_ids
__magic_name__ : Optional[Any] = self.tokenizer(_A , padding='longest' , return_tensors='pt' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_A , _A ):
__magic_name__ : str = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F' {self.tokenizer.model_max_length} tokens: {removed_text}' )
__magic_name__ : Union[str, Any] = text_input_ids.to(_A )
__magic_name__ : Dict = text_inputs.attention_mask.to(_A )
__magic_name__ , __magic_name__ : str = self.text_encoder(
input_ids=_A , attention_mask=_A )
__magic_name__ : Tuple = prompt_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : int = text_encoder_hidden_states.repeat_interleave(_A , dim=0 )
__magic_name__ : Union[str, Any] = text_mask.repeat_interleave(_A , dim=0 )
if do_classifier_free_guidance:
__magic_name__ : List[str]
if negative_prompt is None:
__magic_name__ : Optional[Any] = [''] * batch_size
elif type(_A ) is not type(_A ):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(_A )} !='
F' {type(_A )}.' )
elif isinstance(_A , _A ):
__magic_name__ : int = [negative_prompt]
elif batch_size != len(_A ):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(_A )}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
' the batch size of `prompt`.' )
else:
__magic_name__ : Dict = negative_prompt
__magic_name__ : List[str] = self.tokenizer(
_A , padding='max_length' , max_length=77 , truncation=_A , return_attention_mask=_A , add_special_tokens=_A , return_tensors='pt' , )
__magic_name__ : Optional[int] = uncond_input.input_ids.to(_A )
__magic_name__ : Optional[Any] = uncond_input.attention_mask.to(_A )
__magic_name__ , __magic_name__ : int = self.text_encoder(
input_ids=_A , attention_mask=_A )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__magic_name__ : List[str] = negative_prompt_embeds.shape[1]
__magic_name__ : str = negative_prompt_embeds.repeat(1 , _A )
__magic_name__ : Dict = negative_prompt_embeds.view(batch_size * num_images_per_prompt , _A )
__magic_name__ : Any = uncond_text_encoder_hidden_states.shape[1]
__magic_name__ : Optional[int] = uncond_text_encoder_hidden_states.repeat(1 , _A , 1 )
__magic_name__ : Tuple = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt , _A , -1 )
__magic_name__ : List[Any] = uncond_text_mask.repeat_interleave(_A , dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__magic_name__ : Tuple = torch.cat([negative_prompt_embeds, prompt_embeds] )
__magic_name__ : str = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
__magic_name__ : str = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def __lowerCAmelCase ( self : Dict , _A : List[Any]=0 ) -> Tuple:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
__magic_name__ : List[Any] = torch.device(F'cuda:{gpu_id}' )
__magic_name__ : Dict = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_A , _A )
def __lowerCAmelCase ( self : List[Any] , _A : List[str]=0 ) -> str:
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
__magic_name__ : int = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=_A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__magic_name__ : Optional[int] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
__magic_name__ , __magic_name__ : Union[str, Any] = cpu_offload_with_hook(_A , _A , prev_module_hook=_A )
if self.safety_checker is not None:
__magic_name__ , __magic_name__ : List[str] = cpu_offload_with_hook(self.safety_checker , _A , prev_module_hook=_A )
# We'll offload the last model manually.
__magic_name__ : Any = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowerCAmelCase ( self : int ) -> List[str]:
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_A , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_A )
def __call__( self : int , _A : Union[str, List[str]] , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : Optional[Union[str, List[str]]] = None , _A : int = 512 , _A : int = 512 , _A : int = 100 , _A : float = 4.0 , _A : int = 1 , _A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _A : Optional[torch.FloatTensor] = None , _A : Optional[str] = "pil" , _A : bool = True , ) -> Optional[int]:
if isinstance(_A , _A ):
__magic_name__ : Optional[int] = 1
elif isinstance(_A , _A ):
__magic_name__ : Union[str, Any] = len(_A )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_A )}' )
__magic_name__ : Tuple = self._execution_device
__magic_name__ : Any = batch_size * num_images_per_prompt
__magic_name__ : int = guidance_scale > 1.0
__magic_name__ , __magic_name__ , __magic_name__ : Optional[int] = self._encode_prompt(
_A , _A , _A , _A , _A )
if isinstance(_A , _A ):
__magic_name__ : Union[str, Any] = torch.cat(_A , dim=0 )
if isinstance(_A , _A ):
__magic_name__ : Dict = torch.cat(_A , dim=0 )
if do_classifier_free_guidance:
__magic_name__ : Dict = image_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : Optional[int] = negative_image_embeds.repeat_interleave(_A , dim=0 )
__magic_name__ : Optional[int] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(
dtype=prompt_embeds.dtype , device=_A )
self.scheduler.set_timesteps(_A , device=_A )
__magic_name__ : Tuple = self.scheduler.timesteps
__magic_name__ : Optional[int] = self.unet.config.in_channels
__magic_name__ , __magic_name__ : Dict = get_new_h_w(_A , _A , self.movq_scale_factor )
# create initial latent
__magic_name__ : Union[str, Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , text_encoder_hidden_states.dtype , _A , _A , _A , self.scheduler , )
for i, t in enumerate(self.progress_bar(_A ) ):
# expand the latents if we are doing classifier free guidance
__magic_name__ : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__magic_name__ : Tuple = {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
__magic_name__ : Union[str, Any] = self.unet(
sample=_A , timestep=_A , encoder_hidden_states=_A , added_cond_kwargs=_A , return_dict=_A , )[0]
if do_classifier_free_guidance:
__magic_name__ , __magic_name__ : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
__magic_name__ , __magic_name__ : Dict = noise_pred.chunk(2 )
__magic_name__ , __magic_name__ : List[str] = variance_pred.chunk(2 )
__magic_name__ : List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__magic_name__ : Any = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__magic_name__ , __magic_name__ : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__magic_name__ : List[Any] = self.scheduler.step(
_A , _A , _A , generator=_A , ).prev_sample
# post-processing
__magic_name__ : int = self.movq.decode(_A , force_not_quantize=_A )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
__magic_name__ : Dict = image * 0.5 + 0.5
__magic_name__ : str = image.clamp(0 , 1 )
__magic_name__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__magic_name__ : str = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A ) | 275 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __lowerCamelCase ( __a :str , __a :List[str] , __a :List[str] ) -> Optional[Any]:
"""simple docstring"""
if isinstance(__a , torch.Tensor ):
return image
elif isinstance(__a , PIL.Image.Image ):
A__ = [image]
if isinstance(image[0] , PIL.Image.Image ):
A__ = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
A__ = np.concatenate(__a , axis=0 )
A__ = np.array(__a ).astype(np.floataa ) / 255.0
A__ = image.transpose(0 , 3 , 1 , 2 )
A__ = 2.0 * image - 1.0
A__ = torch.from_numpy(__a )
elif isinstance(image[0] , torch.Tensor ):
A__ = torch.cat(__a , dim=0 )
return image
def __lowerCamelCase ( __a :Union[str, Any] , __a :Optional[int] , __a :Optional[Any] , __a :Optional[Any]=0.9995 ) -> int:
"""simple docstring"""
if not isinstance(__a , np.ndarray ):
A__ = True
A__ = va.device
A__ = va.cpu().numpy()
A__ = va.cpu().numpy()
A__ = np.sum(va * va / (np.linalg.norm(__a ) * np.linalg.norm(__a )) )
if np.abs(__a ) > DOT_THRESHOLD:
A__ = (1 - t) * va + t * va
else:
A__ = np.arccos(__a )
A__ = np.sin(__a )
A__ = theta_a * t
A__ = np.sin(__a )
A__ = np.sin(theta_a - theta_t ) / sin_theta_a
A__ = sin_theta_t / sin_theta_a
A__ = sa * va + sa * va
if inputs_are_torch:
A__ = torch.from_numpy(__a ).to(__a )
return va
def __lowerCamelCase ( __a :int , __a :Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = F.normalize(__a , dim=-1 )
A__ = F.normalize(__a , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __lowerCamelCase ( __a :Any , __a :Optional[int] ) -> int:
"""simple docstring"""
for param in model.parameters():
A__ = value
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCAmelCase : AutoencoderKL , __lowerCAmelCase : CLIPTextModel , __lowerCAmelCase : CLIPModel , __lowerCAmelCase : CLIPTokenizer , __lowerCAmelCase : UNetaDConditionModel , __lowerCAmelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , __lowerCAmelCase : CLIPFeatureExtractor , __lowerCAmelCase : Dict=None , __lowerCAmelCase : Optional[Any]=None , __lowerCAmelCase : List[str]=None , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(
vae=__lowerCAmelCase , text_encoder=__lowerCAmelCase , clip_model=__lowerCAmelCase , tokenizer=__lowerCAmelCase , unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , feature_extractor=__lowerCAmelCase , coca_model=__lowerCAmelCase , coca_tokenizer=__lowerCAmelCase , coca_transform=__lowerCAmelCase , )
A__ = (
feature_extractor.size
if isinstance(feature_extractor.size , __lowerCAmelCase )
else feature_extractor.size["""shortest_edge"""]
)
A__ = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , __lowerCAmelCase )
set_requires_grad(self.clip_model , __lowerCAmelCase )
def a_ ( self : int , __lowerCAmelCase : Optional[Union[str, int]] = "auto" ) -> Tuple:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
A__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__lowerCAmelCase )
def a_ ( self : int ) -> str:
"""simple docstring"""
self.enable_attention_slicing(__lowerCAmelCase )
def a_ ( self : int ) -> Tuple:
"""simple docstring"""
set_requires_grad(self.vae , __lowerCAmelCase )
def a_ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
set_requires_grad(self.vae , __lowerCAmelCase )
def a_ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
set_requires_grad(self.unet , __lowerCAmelCase )
def a_ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
set_requires_grad(self.unet , __lowerCAmelCase )
def a_ ( self : Dict , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
A__ = min(int(num_inference_steps * strength ) , __lowerCAmelCase )
A__ = max(num_inference_steps - init_timestep , 0 )
A__ = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def a_ ( self : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : Tuple , __lowerCAmelCase : Union[str, Any]=None ) -> Optional[int]:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , torch.Tensor ):
raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(__lowerCAmelCase )}' )
A__ = image.to(device=__lowerCAmelCase , dtype=__lowerCAmelCase )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
A__ = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__lowerCAmelCase )
]
A__ = torch.cat(__lowerCAmelCase , dim=0 )
else:
A__ = self.vae.encode(__lowerCAmelCase ).latent_dist.sample(__lowerCAmelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 0.1_8_2_1_5 * init_latents
A__ = init_latents.repeat_interleave(__lowerCAmelCase , dim=0 )
A__ = randn_tensor(init_latents.shape , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase )
# get latents
A__ = self.scheduler.add_noise(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
A__ = init_latents
return latents
def a_ ( self : Tuple , __lowerCAmelCase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
A__ = self.coca_transform(__lowerCAmelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
A__ = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
A__ = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" , """""" ).rstrip(""" .,""" )
def a_ ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.feature_extractor.preprocess(__lowerCAmelCase )
A__ = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
A__ = self.clip_model.get_image_features(__lowerCAmelCase )
A__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__lowerCAmelCase )
A__ = image_embeddings_clip.repeat_interleave(__lowerCAmelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def a_ ( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , ) -> Tuple:
"""simple docstring"""
A__ = latents.detach().requires_grad_()
A__ = self.scheduler.scale_model_input(__lowerCAmelCase , __lowerCAmelCase )
# predict the noise residual
A__ = self.unet(__lowerCAmelCase , __lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
A__ = self.scheduler.alphas_cumprod[timestep]
A__ = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A__ = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
A__ = torch.sqrt(__lowerCAmelCase )
A__ = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , __lowerCAmelCase ):
A__ = self.scheduler.sigmas[index]
A__ = latents - sigma * noise_pred
else:
raise ValueError(f'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 1 / 0.1_8_2_1_5 * sample
A__ = self.vae.decode(__lowerCAmelCase ).sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = transforms.Resize(self.feature_extractor_size )(__lowerCAmelCase )
A__ = self.normalize(__lowerCAmelCase ).to(latents.dtype )
A__ = self.clip_model.get_image_features(__lowerCAmelCase )
A__ = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=__lowerCAmelCase )
A__ = spherical_dist_loss(__lowerCAmelCase , __lowerCAmelCase ).mean() * clip_guidance_scale
A__ = -torch.autograd.grad(__lowerCAmelCase , __lowerCAmelCase )[0]
if isinstance(self.scheduler , __lowerCAmelCase ):
A__ = latents.detach() + grads * (sigma**2)
A__ = noise_pred_original
else:
A__ = noise_pred_original - torch.sqrt(__lowerCAmelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : List[Any] , __lowerCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , __lowerCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[str] = None , __lowerCAmelCase : Optional[int] = 5_12 , __lowerCAmelCase : Optional[int] = 5_12 , __lowerCAmelCase : float = 0.6 , __lowerCAmelCase : Optional[int] = 50 , __lowerCAmelCase : Optional[float] = 7.5 , __lowerCAmelCase : Optional[int] = 1 , __lowerCAmelCase : float = 0.0 , __lowerCAmelCase : Optional[float] = 1_00 , __lowerCAmelCase : Optional[torch.Generator] = None , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , __lowerCAmelCase : float = 0.8 , __lowerCAmelCase : float = 0.1 , __lowerCAmelCase : float = 0.1 , ) -> Dict:
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and len(__lowerCAmelCase ) != batch_size:
raise ValueError(f'You have passed {batch_size} batch_size, but only {len(__lowerCAmelCase )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(__lowerCAmelCase , torch.Generator ) and batch_size > 1:
A__ = [generator] + [None] * (batch_size - 1)
A__ = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
A__ = [x[0] for x in coca_is_none if x[1]]
A__ = """, """.join(__lowerCAmelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(__lowerCAmelCase ):
raise ValueError(
f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
A__ = self.get_image_description(__lowerCAmelCase )
if style_prompt is None:
if len(__lowerCAmelCase ):
raise ValueError(
f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
A__ = self.get_image_description(__lowerCAmelCase )
# get prompt text embeddings for content and style
A__ = self.tokenizer(
__lowerCAmelCase , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=__lowerCAmelCase , return_tensors="""pt""" , )
A__ = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
A__ = self.tokenizer(
__lowerCAmelCase , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=__lowerCAmelCase , return_tensors="""pt""" , )
A__ = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
A__ = slerp(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# duplicate text embeddings for each generation per prompt
A__ = text_embeddings.repeat_interleave(__lowerCAmelCase , dim=0 )
# set timesteps
A__ = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
A__ = {}
if accepts_offset:
A__ = 1
self.scheduler.set_timesteps(__lowerCAmelCase , **__lowerCAmelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
A__ , A__ = self.get_timesteps(__lowerCAmelCase , __lowerCAmelCase , self.device )
A__ = timesteps[:1].repeat(__lowerCAmelCase )
# Preprocess image
A__ = preprocess(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
A__ = self.prepare_latents(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , text_embeddings.dtype , self.device , __lowerCAmelCase )
A__ = preprocess(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
A__ = self.prepare_latents(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , text_embeddings.dtype , self.device , __lowerCAmelCase )
A__ = slerp(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if clip_guidance_scale > 0:
A__ = self.get_clip_image_embeddings(__lowerCAmelCase , __lowerCAmelCase )
A__ = self.get_clip_image_embeddings(__lowerCAmelCase , __lowerCAmelCase )
A__ = slerp(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
A__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
A__ = content_text_input.input_ids.shape[-1]
A__ = self.tokenizer([""""""] , padding="""max_length""" , max_length=__lowerCAmelCase , return_tensors="""pt""" )
A__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
A__ = uncond_embeddings.repeat_interleave(__lowerCAmelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
A__ = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
A__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
A__ = torch.randn(__lowerCAmelCase , generator=__lowerCAmelCase , device="""cpu""" , dtype=__lowerCAmelCase ).to(
self.device )
else:
A__ = torch.randn(__lowerCAmelCase , generator=__lowerCAmelCase , device=self.device , dtype=__lowerCAmelCase )
else:
if latents.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
A__ = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
A__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
A__ = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A__ = {}
if accepts_eta:
A__ = eta
# check if the scheduler accepts generator
A__ = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
A__ = generator
with self.progress_bar(total=__lowerCAmelCase ):
for i, t in enumerate(__lowerCAmelCase ):
# expand the latents if we are doing classifier free guidance
A__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A__ = self.scheduler.scale_model_input(__lowerCAmelCase , __lowerCAmelCase )
# predict the noise residual
A__ = self.unet(__lowerCAmelCase , __lowerCAmelCase , encoder_hidden_states=__lowerCAmelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
A__ , A__ = noise_pred.chunk(2 )
A__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
A__ = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
A__ , A__ = self.cond_fn(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )
# compute the previous noisy sample x_t -> x_t-1
A__ = self.scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
A__ = 1 / 0.1_8_2_1_5 * latents
A__ = self.vae.decode(__lowerCAmelCase ).sample
A__ = (image / 2 + 0.5).clamp(0 , 1 )
A__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=__lowerCAmelCase , nsfw_content_detected=__lowerCAmelCase )
| 274 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A (unittest.TestCase ):
'''simple docstring'''
def a_ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
A__ = ["""a""", """b""", """c"""]
# Defaults to last layer if both are None
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""c"""] )
self.assertEqual(__lowerCAmelCase , [2] )
# Out indices set to match out features
A__ , A__ = get_aligned_output_features_output_indices(["""a""", """c"""] , __lowerCAmelCase , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [0, 2] )
# Out features set to match out indices
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , [0, 2] , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [0, 2] )
# Out features selected from negative indices
A__ , A__ = get_aligned_output_features_output_indices(__lowerCAmelCase , [-3, -1] , __lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , ["""a""", """c"""] )
self.assertEqual(__lowerCAmelCase , [-3, -1] )
def a_ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , __lowerCAmelCase )
# Out features must be a list
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(("""a""", """b""") , (0, 1) , ["""a""", """b"""] )
# Out features must be a subset of stage names
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 1) , ["""a"""] )
# Out indices must be a list or tuple
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(__lowerCAmelCase , 0 , ["""a""", """b"""] )
# Out indices must be a subset of stage names
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(__lowerCAmelCase , (0, 1) , ["""a"""] )
# Out features and out indices must be the same length
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0,) , ["""a""", """b""", """c"""] )
# Out features should match out indices
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""a""", """b"""] , (0, 2) , ["""a""", """b""", """c"""] )
# Out features and out indices should be in order
with self.assertRaises(__lowerCAmelCase ):
verify_out_features_out_indices(["""b""", """a"""] , (0, 1) , ["""a""", """b"""] )
# Check passes with valid inputs
verify_out_features_out_indices(["""a""", """b""", """d"""] , (0, 1, -1) , ["""a""", """b""", """c""", """d"""] )
def a_ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
A__ = BackboneMixin()
A__ = ["""a""", """b""", """c"""]
A__ = ["""a""", """c"""]
A__ = [0, 2]
# Check that the output features and indices are set correctly
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [0, 2] )
# Check out features and indices are updated correctly
A__ = ["""a""", """b"""]
self.assertEqual(backbone.out_features , ["""a""", """b"""] )
self.assertEqual(backbone.out_indices , [0, 1] )
A__ = [-3, -1]
self.assertEqual(backbone.out_features , ["""a""", """c"""] )
self.assertEqual(backbone.out_indices , [-3, -1] )
| 274 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ : Tuple = {
'bart': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'bert': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'bert-base-cased-finetuned-mrpc': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'dpr': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'gpt2': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlnet': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'xlm-roberta': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'transfo-xl': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'openai-gpt': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'roberta': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'layoutlm': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'roberta-large-mnli': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'camembert': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'flaubert': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'distilbert-base-distilled-squad': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'lxmert-visual-feature-encoder': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'ctrl': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'albert': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
't5': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'electra': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'wav2vec2': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def _snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Dict=True ):
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.""" )
A__ , A__ , A__ , A__ = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
A__ = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
A__ = config_class.from_json_file(UpperCAmelCase_ )
A__ = True
A__ = True
print(F"""Building TensorFlow model from configuration: {config}""" )
A__ = model_class(UpperCAmelCase_ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
A__ = cached_file(
UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
A__ = load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase_ , UpperCAmelCase_ )
if compare_with_pt_model:
A__ = tf_model(tf_model.dummy_inputs , training=UpperCAmelCase_ ) # build the network
A__ = torch.load(UpperCAmelCase_ , map_location="""cpu""" )
A__ = pt_model_class.from_pretrained(
pretrained_model_name_or_path=UpperCAmelCase_ , config=UpperCAmelCase_ , state_dict=UpperCAmelCase_ )
with torch.no_grad():
A__ = pt_model(**pt_model.dummy_inputs )
A__ = pto[0].numpy()
A__ = tfo[0].numpy()
A__ = np.amax(np.abs(np_pt - np_tf ) )
print(F"""Max absolute difference between models outputs {diff}""" )
assert diff <= 2e-2, F"""Error, model absolute difference is >2e-2: {diff}"""
# Save pytorch-model
print(F"""Save TensorFlow model to {tf_dump_path}""" )
tf_model.save_weights(UpperCAmelCase_ , save_format="""h5""" )
def _snake_case ( UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Optional[Any]=False , UpperCAmelCase_ : List[Any]=False , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Dict=False , ):
if args_model_type is None:
A__ = list(MODEL_CLASSES.keys() )
else:
A__ = [args_model_type]
for j, model_type in enumerate(UpperCAmelCase_ , start=1 ):
print("""=""" * 100 )
print(F""" Converting model type {j}/{len(UpperCAmelCase_ )}: {model_type}""" )
print("""=""" * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.""" )
A__ , A__ , A__ , A__ , A__ = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
A__ = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
A__ = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(UpperCAmelCase_ , UpperCAmelCase_ ) , start=1 ):
print("""-""" * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F""" Skipping finetuned checkpoint {model_shortcut_name}""" )
continue
A__ = model_shortcut_name
elif only_convert_finetuned_models:
print(F""" Skipping not finetuned checkpoint {model_shortcut_name}""" )
continue
print(
F""" Converting checkpoint {i}/{len(UpperCAmelCase_ )}: {model_shortcut_name} - model_type {model_type}""" )
print("""-""" * 100 )
if config_shortcut_name in aws_config_map:
A__ = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
else:
A__ = config_shortcut_name
if model_shortcut_name in aws_model_maps:
A__ = cached_file(UpperCAmelCase_ , UpperCAmelCase_ , force_download=not use_cached_models )
else:
A__ = model_shortcut_name
if os.path.isfile(UpperCAmelCase_ ):
A__ = """converted_model"""
convert_pt_checkpoint_to_tf(
model_type=UpperCAmelCase_ , pytorch_checkpoint_path=UpperCAmelCase_ , config_file=UpperCAmelCase_ , tf_dump_path=os.path.join(UpperCAmelCase_ , model_shortcut_name + """-tf_model.h5""" ) , compare_with_pt_model=UpperCAmelCase_ , )
if remove_cached_files:
os.remove(UpperCAmelCase_ )
os.remove(UpperCAmelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
)
parser.add_argument(
'--model_type',
default=None,
type=str,
help=(
f"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
'convert all the models from AWS.'
),
)
parser.add_argument(
'--pytorch_checkpoint_path',
default=None,
type=str,
help=(
'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
'If not given, will download and convert all the checkpoints from AWS.'
),
)
parser.add_argument(
'--config_file',
default=None,
type=str,
help=(
'The config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture. If not given and '
'--pytorch_checkpoint_path is not given or is a shortcut name '
'use the configuration associated to the shortcut name on the AWS'
),
)
parser.add_argument(
'--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
)
parser.add_argument(
'--use_cached_models',
action='store_true',
help='Use cached models if possible instead of updating to latest checkpoint versions.',
)
parser.add_argument(
'--remove_cached_files',
action='store_true',
help='Remove pytorch models after conversion (save memory when converting in batches).',
)
parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
SCREAMING_SNAKE_CASE_ : Optional[int] = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
)
| 69 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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 (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: int , UpperCamelCase: int , UpperCamelCase: Union[str, Any]=13 , UpperCamelCase: List[Any]=7 , UpperCamelCase: Any=True , UpperCamelCase: Optional[Any]=True , UpperCamelCase: Optional[Any]=True , UpperCamelCase: str=True , UpperCamelCase: Optional[int]=99 , UpperCamelCase: Optional[Any]=32 , UpperCamelCase: Tuple=5 , UpperCamelCase: Optional[int]=4 , UpperCamelCase: int=37 , UpperCamelCase: str="gelu" , UpperCamelCase: Optional[Any]=0.1 , UpperCamelCase: List[Any]=0.1 , UpperCamelCase: Tuple=5_12 , UpperCamelCase: List[str]=16 , UpperCamelCase: List[str]=2 , UpperCamelCase: List[Any]=0.02 , UpperCamelCase: List[str]=False , UpperCamelCase: int=True , UpperCamelCase: Union[str, Any]="None" , UpperCamelCase: Optional[int]=3 , UpperCamelCase: List[str]=4 , UpperCamelCase: List[str]=None , ):
"""simple docstring"""
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__ = relative_attention
A__ = position_biased_input
A__ = pos_att_type
A__ = scope
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_input_mask:
A__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A__ = None
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A__ = ids_tensor([self.batch_size] , self.num_choices )
A__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self: str ):
"""simple docstring"""
return DebertaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.get_config()
A__ = 3_00
return config
def UpperCamelCase ( self: List[Any] , UpperCamelCase: str ):
"""simple docstring"""
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def UpperCamelCase ( self: Tuple , UpperCamelCase: List[Any] , UpperCamelCase: List[str] , UpperCamelCase: List[Any] , UpperCamelCase: List[str] , UpperCamelCase: List[str] , UpperCamelCase: Optional[Any] , UpperCamelCase: Tuple ):
"""simple docstring"""
A__ = DebertaModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase )[0]
A__ = model(UpperCamelCase , token_type_ids=UpperCamelCase )[0]
A__ = model(UpperCamelCase )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def UpperCamelCase ( self: List[str] , UpperCamelCase: Optional[int] , UpperCamelCase: Optional[Any] , UpperCamelCase: Union[str, Any] , UpperCamelCase: Tuple , UpperCamelCase: Tuple , UpperCamelCase: str , UpperCamelCase: Any ):
"""simple docstring"""
A__ = DebertaForMaskedLM(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self: Union[str, Any] , UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any] , UpperCamelCase: int , UpperCamelCase: Dict , UpperCamelCase: Dict , UpperCamelCase: Tuple , UpperCamelCase: str ):
"""simple docstring"""
A__ = self.num_labels
A__ = DebertaForSequenceClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(UpperCamelCase )
def UpperCamelCase ( self: Optional[int] , UpperCamelCase: List[Any] , UpperCamelCase: Any , UpperCamelCase: Dict , UpperCamelCase: int , UpperCamelCase: Optional[Any] , UpperCamelCase: str , UpperCamelCase: int ):
"""simple docstring"""
A__ = self.num_labels
A__ = DebertaForTokenClassification(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self: Tuple , UpperCamelCase: Tuple , UpperCamelCase: Optional[int] , UpperCamelCase: Tuple , UpperCamelCase: int , UpperCamelCase: int , UpperCamelCase: Tuple , UpperCamelCase: Any ):
"""simple docstring"""
A__ = DebertaForQuestionAnswering(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
A__ = model(
UpperCamelCase , attention_mask=UpperCamelCase , token_type_ids=UpperCamelCase , start_positions=UpperCamelCase , end_positions=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: str ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class a ( _lowerCamelCase, _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase = (
{
"feature-extraction": DebertaModel,
"fill-mask": DebertaForMaskedLM,
"question-answering": DebertaForQuestionAnswering,
"text-classification": DebertaForSequenceClassification,
"token-classification": DebertaForTokenClassification,
"zero-shot": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase = True
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = DebertaModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase , hidden_size=37 )
def UpperCamelCase ( self: int ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase )
def UpperCamelCase ( self: List[Any] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase )
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase )
@slow
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = DebertaModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip(reason="""Model not available yet""" )
def UpperCamelCase ( self: Any ):
"""simple docstring"""
pass
@slow
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
A__ = DebertaModel.from_pretrained("""microsoft/deberta-base""" )
A__ = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
A__ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
A__ = model(UpperCamelCase , attention_mask=UpperCamelCase )[0]
# compare the actual values for a slice.
A__ = torch.tensor(
[[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCamelCase , atol=1e-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 69 | 1 |
'''simple docstring'''
def A_ ( snake_case ):
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:int = credit_card_number
SCREAMING_SNAKE_CASE:List[Any] = 0
SCREAMING_SNAKE_CASE:Optional[Any] = len(snake_case ) - 2
for i in range(snake_case , -1 , -2 ):
# double the value of every second digit
SCREAMING_SNAKE_CASE:int = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
SCREAMING_SNAKE_CASE:List[str] = cc_number[:i] + str(snake_case ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(snake_case ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:str = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''' )
return False
if not 13 <= len(snake_case ) <= 16:
print(F'''{error_message} of its length.''' )
return False
if not validate_initial_digits(snake_case ):
print(F'''{error_message} of its first two digits.''' )
return False
if not luhn_validation(snake_case ):
print(F'''{error_message} it fails the Luhn check.''' )
return False
print(F'''{credit_card_number} is a valid credit card number.''' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 139 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _snake_case ( _a ):
_A : int = ComputeEnvironment.AMAZON_SAGEMAKER
_A : List[Any] = True
_A : Dict = '''ml.p3.2xlarge'''
_A : Any = '''accelerate_sagemaker_execution_role'''
_A : Union[str, Any] = '''hf-sm'''
_A : Dict = '''us-east-1'''
_A : List[Any] = 1
_A : Union[str, Any] = '''accelerate-sagemaker-1'''
_A : List[Any] = '''1.6'''
_A : Optional[Any] = '''4.4'''
_A : Any = '''train.py'''
_A : int = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''False''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
_A : Optional[Any] = [
'''--model_name_or_path''',
'''bert''',
'''--do_train''',
'''--do_test''',
'''False''',
'''--do_predict''',
'''--epochs''',
'''3''',
'''--learning_rate''',
'''5e-5''',
'''--max_steps''',
'''50.5''',
]
class _snake_case ( unittest.TestCase ):
def __UpperCamelCase ( self : List[str] ):
# If no defaults are changed, `to_kwargs` returns an empty dict.
SCREAMING_SNAKE_CASE:str = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args["model_name_or_path"] ,SCREAMING_SNAKE_CASE__ )
assert isinstance(converted_args["do_train"] ,SCREAMING_SNAKE_CASE__ )
assert isinstance(converted_args["epochs"] ,SCREAMING_SNAKE_CASE__ )
assert isinstance(converted_args["learning_rate"] ,SCREAMING_SNAKE_CASE__ )
assert isinstance(converted_args["max_steps"] ,SCREAMING_SNAKE_CASE__ )
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 139 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
lowerCAmelCase_ = datasets.utils.logging.get_logger(__name__)
lowerCAmelCase_ = ['names', 'prefix']
lowerCAmelCase_ = ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCAmelCase_ = ['encoding_errors', 'on_bad_lines']
lowerCAmelCase_ = ['date_format']
@dataclass
class __A ( datasets.BuilderConfig ):
'''simple docstring'''
lowerCAmelCase : Tuple = ","
lowerCAmelCase : List[str] = None
lowerCAmelCase : List[str] = "infer"
lowerCAmelCase : str = None
lowerCAmelCase : int = None
lowerCAmelCase : Tuple = None
lowerCAmelCase : Any = None
lowerCAmelCase : Dict = None
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : Any = None
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Dict = None
lowerCAmelCase : Dict = None
lowerCAmelCase : List[Any] = False
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : List[str] = None
lowerCAmelCase : int = None
lowerCAmelCase : List[Any] = True
lowerCAmelCase : Any = True
lowerCAmelCase : str = False
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : Any = "."
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Optional[Any] = "\""
lowerCAmelCase : Dict = 0
lowerCAmelCase : Tuple = None
lowerCAmelCase : Any = None
lowerCAmelCase : List[Any] = None
lowerCAmelCase : Union[str, Any] = None
lowerCAmelCase : Tuple = True
lowerCAmelCase : List[str] = True
lowerCAmelCase : int = 0
lowerCAmelCase : Optional[Any] = True
lowerCAmelCase : Dict = False
lowerCAmelCase : str = None
lowerCAmelCase : str = 1_0_0_0_0
lowerCAmelCase : str = None
lowerCAmelCase : List[str] = "strict"
lowerCAmelCase : str = "error"
lowerCAmelCase : Union[str, Any] = None
def UpperCAmelCase ( self : str ) -> List[str]:
"""simple docstring"""
if self.delimiter is not None:
lowercase__ : Dict = self.delimiter
if self.column_names is not None:
lowercase__ : int = self.column_names
@property
def UpperCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = {
'''sep''': self.sep,
'''header''': self.header,
'''names''': self.names,
'''index_col''': self.index_col,
'''usecols''': self.usecols,
'''prefix''': self.prefix,
'''mangle_dupe_cols''': self.mangle_dupe_cols,
'''engine''': self.engine,
'''converters''': self.converters,
'''true_values''': self.true_values,
'''false_values''': self.false_values,
'''skipinitialspace''': self.skipinitialspace,
'''skiprows''': self.skiprows,
'''nrows''': self.nrows,
'''na_values''': self.na_values,
'''keep_default_na''': self.keep_default_na,
'''na_filter''': self.na_filter,
'''verbose''': self.verbose,
'''skip_blank_lines''': self.skip_blank_lines,
'''thousands''': self.thousands,
'''decimal''': self.decimal,
'''lineterminator''': self.lineterminator,
'''quotechar''': self.quotechar,
'''quoting''': self.quoting,
'''escapechar''': self.escapechar,
'''comment''': self.comment,
'''encoding''': self.encoding,
'''dialect''': self.dialect,
'''error_bad_lines''': self.error_bad_lines,
'''warn_bad_lines''': self.warn_bad_lines,
'''skipfooter''': self.skipfooter,
'''doublequote''': self.doublequote,
'''memory_map''': self.memory_map,
'''float_precision''': self.float_precision,
'''chunksize''': self.chunksize,
'''encoding_errors''': self.encoding_errors,
'''on_bad_lines''': self.on_bad_lines,
'''date_format''': self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,UpperCamelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class __A ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
lowerCAmelCase : Tuple = CsvConfig
def UpperCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowercase__ : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ ,(str, list, tuple) ):
lowercase__ : Any = data_files
if isinstance(UpperCamelCase__ ,UpperCamelCase__ ):
lowercase__ : List[str] = [files]
lowercase__ : Tuple = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={'''files''': files} )]
lowercase__ : Dict = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ ,UpperCamelCase__ ):
lowercase__ : Dict = [files]
lowercase__ : List[Any] = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ ,gen_kwargs={'''files''': files} ) )
return splits
def UpperCAmelCase ( self : str ,_snake_case : Any ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
lowercase__ : Any = self.config.features.arrow_schema
if all(not require_storage_cast(UpperCamelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
lowercase__ : Tuple = pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=UpperCamelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
lowercase__ : Any = table_cast(UpperCamelCase__ ,UpperCamelCase__ )
return pa_table
def UpperCAmelCase ( self : Dict ,_snake_case : str ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
lowercase__ : Union[str, Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase__ ) else object
for name, dtype, feature in zip(schema.names ,schema.types ,self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
lowercase__ : Optional[int] = pd.read_csv(UpperCamelCase__ ,iterator=UpperCamelCase__ ,dtype=UpperCamelCase__ ,**self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(UpperCamelCase__ ):
lowercase__ : int = pa.Table.from_pandas(UpperCamelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(f"""Failed to read file \'{file}\' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 363 |
"""simple docstring"""
from __future__ import annotations
lowerCAmelCase_ = 1.6021E-19 # units = C
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 302 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
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 (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__( self : Optional[Any] , lowercase : int , lowercase : str=13 , lowercase : Any=7 , lowercase : str=True , lowercase : int=True , lowercase : int=True , lowercase : Any=True , lowercase : Any=99 , lowercase : Any=32 , lowercase : Dict=5 , lowercase : Optional[int]=4 , lowercase : Dict=37 , lowercase : int="gelu" , lowercase : Union[str, Any]=0.1 , lowercase : Union[str, Any]=0.1 , lowercase : str=512 , lowercase : Tuple=16 , lowercase : List[str]=2 , lowercase : str=0.02 , lowercase : str=3 , lowercase : Dict=4 , lowercase : int=None , ):
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A ( self : Tuple ):
'''simple docstring'''
return NystromformerConfig(
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=lowercase , initializer_range=self.initializer_range , )
def A ( self : Optional[Any] , lowercase : Union[str, Any] , lowercase : Optional[Any] , lowercase : Tuple , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : List[Any] , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase )
UpperCAmelCase = model(lowercase , token_type_ids=lowercase )
UpperCAmelCase = model(lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Union[str, Any] , lowercase : Optional[int] , lowercase : Any , lowercase : str , lowercase : int , lowercase : int , lowercase : Dict , lowercase : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerForMaskedLM(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A ( self : Union[str, Any] , lowercase : Optional[Any] , lowercase : Dict , lowercase : Tuple , lowercase : int , lowercase : Optional[Any] , lowercase : List[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = NystromformerForQuestionAnswering(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , start_positions=lowercase , end_positions=lowercase , )
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 A ( self : List[str] , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Optional[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Tuple , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForSequenceClassification(lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self : Any , lowercase : str , lowercase : List[Any] , lowercase : str , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Tuple , lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NystromformerForTokenClassification(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = model(lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A ( self : Optional[int] , lowercase : int , lowercase : Optional[int] , lowercase : Optional[int] , lowercase : Any , lowercase : Optional[Any] , lowercase : List[str] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = self.num_choices
UpperCAmelCase = NystromformerForMultipleChoice(config=lowercase )
model.to(lowercase )
model.eval()
UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = model(
lowercase , attention_mask=lowercase , token_type_ids=lowercase , labels=lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _a ( __a , __a , unittest.TestCase ):
__a : Optional[int] = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__a : List[Any] = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__a : Optional[int] = False
__a : int = False
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = NystromformerModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowercase , hidden_size=37 )
def A ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase = type
self.model_tester.create_and_check_model(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase )
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = NystromformerModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@require_torch
class _a ( unittest.TestCase ):
@slow
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase = NystromformerModel.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
UpperCAmelCase = model(lowercase )[0]
UpperCAmelCase = torch.Size((1, 6, 768) )
self.assertEqual(output.shape , lowercase )
UpperCAmelCase = torch.tensor(
[[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase , atol=1E-4 ) )
@slow
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = '''the [MASK] of Belgium is Brussels'''
UpperCAmelCase = AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512''' )
UpperCAmelCase = tokenizer(lowercase , return_tensors='''pt''' )
with torch.no_grad():
UpperCAmelCase = model(encoding.input_ids ).logits
UpperCAmelCase = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(lowercase ) , '''capital''' )
| 34 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A =input('Enter image url: ').strip()
print(f"""Downloading image from {url} ...""")
A =BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the content field of the first meta tag with property og:image
A =soup.find('meta', {'property': 'og:image'})['content']
A =requests.get(image_url).content
A =f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, 'wb') as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
"""tokenization_convbert""": ["""ConvBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ["""ConvBertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"""CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvBertForMaskedLM""",
"""ConvBertForMultipleChoice""",
"""ConvBertForQuestionAnswering""",
"""ConvBertForSequenceClassification""",
"""ConvBertForTokenClassification""",
"""ConvBertLayer""",
"""ConvBertModel""",
"""ConvBertPreTrainedModel""",
"""load_tf_weights_in_convbert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"""TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFConvBertForMaskedLM""",
"""TFConvBertForMultipleChoice""",
"""TFConvBertForQuestionAnswering""",
"""TFConvBertForSequenceClassification""",
"""TFConvBertForTokenClassification""",
"""TFConvBertLayer""",
"""TFConvBertModel""",
"""TFConvBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 371 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = """▁"""
__lowerCamelCase = {"""vocab_file""": """prophetnet.tokenizer"""}
__lowerCamelCase = {
"""vocab_file""": {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
),
}
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}
__lowerCamelCase = {
"""microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def UpperCamelCase ( __lowerCamelCase : Dict ):
snake_case : Dict = collections.OrderedDict()
with open(__lowerCamelCase , "r" , encoding="utf-8" ) as reader:
snake_case : Any = reader.readlines()
for index, token in enumerate(__lowerCamelCase ):
snake_case : List[Any] = token.rstrip("\n" )
snake_case : int = index
return vocab
class UpperCAmelCase ( A_ ):
A__ : Tuple = VOCAB_FILES_NAMES
A__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : int = ["input_ids", "attention_mask"]
def __init__(self : Any , snake_case__ : Dict , snake_case__ : List[Any]="[SEP]" , snake_case__ : Optional[int]="[SEP]" , snake_case__ : Union[str, Any]="[SEP]" , snake_case__ : List[Any]="[UNK]" , snake_case__ : List[str]="[PAD]" , snake_case__ : List[str]="[CLS]" , snake_case__ : List[Any]="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : List[str] , ) -> None:
'''simple docstring'''
snake_case : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case__ , eos_token=snake_case__ , sep_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
snake_case : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case__ ) )
snake_case : Dict = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
snake_case : List[Any] = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}
for i in range(10 ):
snake_case : Dict = f"""[unused{i}]"""
snake_case : List[str] = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
snake_case : Dict = 12
snake_case : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(snake_case__ )
def __getstate__(self : str ) -> Union[str, Any]:
'''simple docstring'''
snake_case : str = self.__dict__.copy()
snake_case : Tuple = None
return state
def __setstate__(self : str , snake_case__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case : Union[str, Any] = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
" pip install sentencepiece" )
raise
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
snake_case : Dict = {}
snake_case : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None , snake_case__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case__ , token_ids_a=snake_case__ , already_has_special_tokens=snake_case__ )
if token_ids_a is None:
return ([0] * len(snake_case__ )) + [1]
return ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case : List[str] = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _SCREAMING_SNAKE_CASE (self : Any ) -> int:
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset
def _SCREAMING_SNAKE_CASE (self : int ) -> Any:
'''simple docstring'''
snake_case : List[str] = {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _SCREAMING_SNAKE_CASE (self : Optional[int] , snake_case__ : str ) -> str:
'''simple docstring'''
return self.sp_model.encode(snake_case__ , out_type=snake_case__ )
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case : Optional[Any] = self.sp_model.PieceToId(snake_case__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Optional[int] ) -> int:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] , snake_case__ : Dict ) -> List[Any]:
'''simple docstring'''
snake_case : Dict = "".join(snake_case__ ).replace(snake_case__ , " " ).strip()
return out_string
def _SCREAMING_SNAKE_CASE (self : Tuple , snake_case__ : str , snake_case__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case : Dict = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case__ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case__ , "wb" ) as fi:
snake_case : Tuple = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (out_vocab_file,)
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
snake_case : str = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 10 | 0 |
"""simple docstring"""
_UpperCamelCase : Tuple = [0, 2, 4, 6, 8]
_UpperCamelCase : Any = [1, 3, 5, 7, 9]
def a_ ( _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
lowercase__ : Any = 0
for digit in range(10 ):
lowercase__ : Any = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _lowerCAmelCase , _lowerCAmelCase )
return result
lowercase__ : Dict = 0
for digita in range(10 ):
lowercase__ : Union[str, Any] = digita
if (remainder + digita) % 2 == 0:
lowercase__ : List[str] = ODD_DIGITS
else:
lowercase__ : Dict = EVEN_DIGITS
for digita in other_parity_digits:
lowercase__ : Optional[int] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _lowerCAmelCase , _lowerCAmelCase , )
return result
def a_ ( _lowerCAmelCase : int = 9 ):
'''simple docstring'''
lowercase__ : Union[str, Any] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_lowerCAmelCase , 0 , [0] * length , _lowerCAmelCase )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
lowercase__ : Union[str, Any] = logging.get_logger(__name__)
# General docstring
lowercase__ : Dict = 'ResNetConfig'
# Base docstring
lowercase__ : str = 'microsoft/resnet-50'
lowercase__ : Tuple = [1, 20_48, 7, 7]
# Image classification docstring
lowercase__ : Optional[Any] = 'microsoft/resnet-50'
lowercase__ : List[str] = 'tiger cat'
lowercase__ : List[Any] = [
'microsoft/resnet-50',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 3 , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : str = "relu" ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase = nn.Convad(
lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=lowerCAmelCase__ , stride=lowerCAmelCase__ , padding=kernel_size // 2 , bias=lowerCAmelCase__ )
_UpperCamelCase = nn.BatchNormad(lowerCAmelCase__ )
_UpperCamelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def snake_case__ ( self : Any , lowerCAmelCase__ : Tensor ) -> Tensor:
'''simple docstring'''
_UpperCamelCase = self.convolution(lowerCAmelCase__ )
_UpperCamelCase = self.normalization(lowerCAmelCase__ )
_UpperCamelCase = self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase__ : ResNetConfig ) -> Tuple:
'''simple docstring'''
super().__init__()
_UpperCamelCase = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
_UpperCamelCase = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
_UpperCamelCase = config.num_channels
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : Tensor ) -> Tensor:
'''simple docstring'''
_UpperCamelCase = pixel_values.shape[1]
if 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.''' )
_UpperCamelCase = self.embedder(lowerCAmelCase__ )
_UpperCamelCase = self.pooler(lowerCAmelCase__ )
return embedding
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 2 ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase = nn.Convad(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , stride=lowerCAmelCase__ , bias=lowerCAmelCase__ )
_UpperCamelCase = nn.BatchNormad(lowerCAmelCase__ )
def snake_case__ ( self : Any , lowerCAmelCase__ : Tensor ) -> Tensor:
'''simple docstring'''
_UpperCamelCase = self.convolution(lowerCAmelCase__ )
_UpperCamelCase = self.normalization(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : str = "relu" ) -> str:
'''simple docstring'''
super().__init__()
_UpperCamelCase = in_channels != out_channels or stride != 1
_UpperCamelCase = (
ResNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
_UpperCamelCase = nn.Sequential(
ResNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) , ResNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , activation=lowerCAmelCase__ ) , )
_UpperCamelCase = ACTaFN[activation]
def snake_case__ ( self : Tuple , lowerCAmelCase__ : Tuple ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = hidden_state
_UpperCamelCase = self.layer(lowerCAmelCase__ )
_UpperCamelCase = self.shortcut(lowerCAmelCase__ )
hidden_state += residual
_UpperCamelCase = self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 1 , lowerCAmelCase__ : str = "relu" , lowerCAmelCase__ : int = 4 ) -> Optional[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase = in_channels != out_channels or stride != 1
_UpperCamelCase = out_channels // reduction
_UpperCamelCase = (
ResNetShortCut(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) if should_apply_shortcut else nn.Identity()
)
_UpperCamelCase = nn.Sequential(
ResNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 ) , ResNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ ) , ResNetConvLayer(lowerCAmelCase__ , lowerCAmelCase__ , kernel_size=1 , activation=lowerCAmelCase__ ) , )
_UpperCamelCase = ACTaFN[activation]
def snake_case__ ( self : int , lowerCAmelCase__ : List[Any] ) -> List[str]:
'''simple docstring'''
_UpperCamelCase = hidden_state
_UpperCamelCase = self.layer(lowerCAmelCase__ )
_UpperCamelCase = self.shortcut(lowerCAmelCase__ )
hidden_state += residual
_UpperCamelCase = self.activation(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase__ : ResNetConfig , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 2 , lowerCAmelCase__ : int = 2 , ) -> int:
'''simple docstring'''
super().__init__()
_UpperCamelCase = ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer
_UpperCamelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(lowerCAmelCase__ , lowerCAmelCase__ , stride=lowerCAmelCase__ , activation=config.hidden_act ) , *[layer(lowerCAmelCase__ , lowerCAmelCase__ , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def snake_case__ ( self : List[Any] , lowerCAmelCase__ : Tensor ) -> Tensor:
'''simple docstring'''
_UpperCamelCase = input
for layer in self.layers:
_UpperCamelCase = layer(lowerCAmelCase__ )
return hidden_state
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : ResNetConfig ) -> List[Any]:
'''simple docstring'''
super().__init__()
_UpperCamelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
lowerCAmelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_UpperCamelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowerCAmelCase__ , config.depths[1:] ):
self.stages.append(ResNetStage(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , depth=lowerCAmelCase__ ) )
def snake_case__ ( self : Union[str, Any] , lowerCAmelCase__ : Tensor , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = True ) -> BaseModelOutputWithNoAttention:
'''simple docstring'''
_UpperCamelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_UpperCamelCase = hidden_states + (hidden_state,)
_UpperCamelCase = stage_module(lowerCAmelCase__ )
if output_hidden_states:
_UpperCamelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=lowerCAmelCase__ , hidden_states=lowerCAmelCase__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : Optional[int] = ResNetConfig
_snake_case : Union[str, Any] = 'resnet'
_snake_case : Optional[int] = 'pixel_values'
_snake_case : int = True
def snake_case__ ( self : Optional[int] , lowerCAmelCase__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='''fan_out''' , nonlinearity='''relu''' )
elif isinstance(lowerCAmelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def snake_case__ ( self : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple=False ) -> List[str]:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCamelCase = value
lowercase__ : Optional[int] = R'\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
lowercase__ : Any = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'The bare ResNet model outputting raw features without any specific head on top.' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Union[str, Any] ) -> str:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
_UpperCamelCase = config
_UpperCamelCase = ResNetEmbeddings(lowerCAmelCase__ )
_UpperCamelCase = ResNetEncoder(lowerCAmelCase__ )
_UpperCamelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , modality='''vision''' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def snake_case__ ( self : Union[str, Any] , lowerCAmelCase__ : Tensor , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention:
'''simple docstring'''
_UpperCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCamelCase = self.embedder(lowerCAmelCase__ )
_UpperCamelCase = self.encoder(
lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
_UpperCamelCase = encoder_outputs[0]
_UpperCamelCase = self.pooler(lowerCAmelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCAmelCase__ , pooler_output=lowerCAmelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self : Optional[int] , lowerCAmelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
_UpperCamelCase = config.num_labels
_UpperCamelCase = ResNetModel(lowerCAmelCase__ )
# classification head
_UpperCamelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def snake_case__ ( self : int , lowerCAmelCase__ : Optional[torch.FloatTensor] = None , lowerCAmelCase__ : Optional[torch.LongTensor] = None , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Optional[bool] = None , ) -> ImageClassifierOutputWithNoAttention:
'''simple docstring'''
_UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCamelCase = self.resnet(lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
_UpperCamelCase = outputs.pooler_output if return_dict else outputs[1]
_UpperCamelCase = self.classifier(lowerCAmelCase__ )
_UpperCamelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_UpperCamelCase = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_UpperCamelCase = '''single_label_classification'''
else:
_UpperCamelCase = '''multi_label_classification'''
if self.config.problem_type == "regression":
_UpperCamelCase = MSELoss()
if self.num_labels == 1:
_UpperCamelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_UpperCamelCase = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
elif self.config.problem_type == "single_label_classification":
_UpperCamelCase = CrossEntropyLoss()
_UpperCamelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_UpperCamelCase = BCEWithLogitsLoss()
_UpperCamelCase = loss_fct(lowerCAmelCase__ , lowerCAmelCase__ )
if not return_dict:
_UpperCamelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCAmelCase__ , logits=lowerCAmelCase__ , hidden_states=outputs.hidden_states )
@add_start_docstrings(
'\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n ' , __magic_name__ , )
class __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase__ : Any ) -> Dict:
'''simple docstring'''
super().__init__(lowerCAmelCase__ )
super()._init_backbone(lowerCAmelCase__ )
_UpperCamelCase = [config.embedding_size] + config.hidden_sizes
_UpperCamelCase = ResNetEmbeddings(lowerCAmelCase__ )
_UpperCamelCase = ResNetEncoder(lowerCAmelCase__ )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCAmelCase__ )
@replace_return_docstrings(output_type=lowerCAmelCase__ , config_class=_CONFIG_FOR_DOC )
def snake_case__ ( self : Union[str, Any] , lowerCAmelCase__ : Tensor , lowerCAmelCase__ : Optional[bool] = None , lowerCAmelCase__ : Optional[bool] = None ) -> BackboneOutput:
'''simple docstring'''
_UpperCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCamelCase = self.embedder(lowerCAmelCase__ )
_UpperCamelCase = self.encoder(lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
_UpperCamelCase = outputs.hidden_states
_UpperCamelCase = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_UpperCamelCase = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=lowerCAmelCase__ , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=lowerCAmelCase__ , )
| 324 | 0 |
def __snake_case ( __UpperCamelCase : list[int] ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__UpperCamelCase ) )
def __snake_case ( __UpperCamelCase : list[list[int]] ,__UpperCamelCase : int ,__UpperCamelCase : list[int] ,__UpperCamelCase : int ):
"""simple docstring"""
if index == len(__UpperCamelCase ):
return True
# Recursive Step
for i in range(__UpperCamelCase ):
if valid_coloring(graph[index] ,__UpperCamelCase ,__UpperCamelCase ):
# Color current vertex
A_ = i
# Validate coloring
if util_color(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,index + 1 ):
return True
# Backtrack
A_ = -1
return False
def __snake_case ( __UpperCamelCase : list[list[int]] ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = [-1] * len(__UpperCamelCase )
if util_color(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,0 ):
return colored_vertices
return [] | 356 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Optional[Any] = logging.get_logger(__name__)
__a :Any = {'vocab_file': 'vocab.txt'}
__a :Any = {
'vocab_file': {
'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
'YituTech/conv-bert-medium-small': (
'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
),
'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
}
}
__a :List[str] = {
'YituTech/conv-bert-base': 512,
'YituTech/conv-bert-medium-small': 512,
'YituTech/conv-bert-small': 512,
}
__a :List[str] = {
'YituTech/conv-bert-base': {'do_lower_case': True},
'YituTech/conv-bert-medium-small': {'do_lower_case': True},
'YituTech/conv-bert-small': {'do_lower_case': True},
}
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = VOCAB_FILES_NAMES
_lowerCamelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : int = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase : Union[str, Any] = ConvBertTokenizer
def __init__( self : Optional[int] , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : int="[UNK]" , UpperCAmelCase : str="[SEP]" , UpperCAmelCase : Union[str, Any]="[PAD]" , UpperCAmelCase : Tuple="[CLS]" , UpperCAmelCase : Tuple="[MASK]" , UpperCAmelCase : Any=True , UpperCAmelCase : Union[str, Any]=None , **UpperCAmelCase : List[str] , ):
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , )
A_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCAmelCase ) != tokenize_chinese_chars
):
A_ = getattr(UpperCAmelCase , normalizer_state.pop("type" ) )
A_ = do_lower_case
A_ = strip_accents
A_ = tokenize_chinese_chars
A_ = normalizer_class(**UpperCAmelCase )
A_ = do_lower_case
def __A ( self : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Dict=None ):
A_ = [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] , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ):
A_ = [self.sep_token_id]
A_ = [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 : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ):
A_ = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase )
return tuple(UpperCAmelCase ) | 329 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : List[str] = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class UpperCamelCase__ (A__ ):
'''simple docstring'''
lowerCamelCase_ : int = '''gpt_neox_japanese'''
def __init__( self , UpperCamelCase__=3_2000 , UpperCamelCase__=2560 , UpperCamelCase__=32 , UpperCamelCase__=32 , UpperCamelCase__=4 , UpperCamelCase__="gelu" , UpperCamelCase__=1.00 , UpperCamelCase__=1_0000 , UpperCamelCase__=2048 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-5 , UpperCamelCase__=True , UpperCamelCase__=3_1996 , UpperCamelCase__=3_1999 , UpperCamelCase__=0.1 , UpperCamelCase__=0.0 , **UpperCamelCase__ , ) -> str:
super().__init__(bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : str = vocab_size
lowerCamelCase : List[str] = max_position_embeddings
lowerCamelCase : Optional[Any] = hidden_size
lowerCamelCase : List[str] = num_hidden_layers
lowerCamelCase : List[str] = num_attention_heads
lowerCamelCase : Optional[Any] = intermediate_multiple_size
lowerCamelCase : Dict = hidden_act
lowerCamelCase : Tuple = rotary_pct
lowerCamelCase : Any = rotary_emb_base
lowerCamelCase : Optional[Any] = initializer_range
lowerCamelCase : List[Any] = layer_norm_eps
lowerCamelCase : List[Any] = use_cache
lowerCamelCase : Tuple = attention_dropout
lowerCamelCase : Optional[Any] = hidden_dropout
| 48 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCamelCase : Tuple =_symbol_database.Default()
lowerCamelCase : List[str] =_descriptor_pool.Default().AddSerializedFile(
b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
lowerCamelCase : str =globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCamelCase : Optional[int] =None
lowerCamelCase : Tuple =b'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCamelCase : List[str] =45
lowerCamelCase : List[Any] =1581
lowerCamelCase : Optional[int] =1517
lowerCamelCase : Tuple =1570
lowerCamelCase : Dict =1584
lowerCamelCase : Optional[Any] =1793
lowerCamelCase : Dict =1795
lowerCamelCase : Any =1916
lowerCamelCase : Dict =1864
lowerCamelCase : Dict =1905
lowerCamelCase : Dict =1919
lowerCamelCase : Union[str, Any] =2429
lowerCamelCase : List[Any] =2208
lowerCamelCase : List[Any] =2418
lowerCamelCase : List[str] =2323
lowerCamelCase : Dict =2407
# @@protoc_insertion_point(module_scope) | 189 | 0 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_UpperCamelCase = logging.get_logger(__name__)
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(_snake_case ) == len(_snake_case ), F'''{len(_snake_case )} != {len(_snake_case )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
_UpperCamelCase = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_UpperCamelCase = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
try:
UpperCAmelCase = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
F''' {n_student}''' )
return list(range(_snake_case ) )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
if n_student > n_teacher:
raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(_snake_case ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def _a ( _snake_case , _snake_case = "student" , _snake_case = None , _snake_case = None , _snake_case=False , _snake_case=None , _snake_case=None , **_snake_case , ):
"""simple docstring"""
UpperCAmelCase = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(_snake_case , _snake_case ):
AutoTokenizer.from_pretrained(_snake_case ).save_pretrained(_snake_case ) # purely for convenience
UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(_snake_case ).eval()
else:
assert isinstance(_snake_case , _snake_case ), F'''teacher must be a model or string got type {type(_snake_case )}'''
UpperCAmelCase = teacher.config.to_diff_dict()
try:
UpperCAmelCase , UpperCAmelCase = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase = teacher_e
if d is None:
UpperCAmelCase = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config , """num_encoder_layers""" ):
UpperCAmelCase , UpperCAmelCase = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase , UpperCAmelCase = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase = teacher_e
if d is None:
UpperCAmelCase = teacher_d
if hasattr(teacher.config , """num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(_snake_case )
# Copy weights
UpperCAmelCase = teacher.config_class(**_snake_case )
UpperCAmelCase = AutoModelForSeqaSeqLM.from_config(_snake_case )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase = student.load_state_dict(teacher.state_dict() , strict=_snake_case )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase , UpperCAmelCase = list(range(_snake_case ) ), list(range(_snake_case ) )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
F''' {save_path}''' )
student.save_pretrained(_snake_case )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase = pick_layers_to_copy(_snake_case , _snake_case )
if d_layers_to_copy is None:
UpperCAmelCase = pick_layers_to_copy(_snake_case , _snake_case )
try:
if hasattr(
_snake_case , """prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , _snake_case )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , _snake_case )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , _snake_case )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , _snake_case )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , _snake_case )
copy_layers(teacher.decoder.block , student.decoder.block , _snake_case )
logger.info(
F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
UpperCAmelCase = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(_snake_case )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 234 |
"""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
_UpperCamelCase = ["""text""", """image""", """audio"""]
def _a ( _snake_case ):
"""simple docstring"""
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(_snake_case , _snake_case ):
inputs.append(create_inputs(_snake_case ) )
else:
raise ValueError(F'''Invalid type requested: {input_type}''' )
return inputs
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
for output in outputs:
if isinstance(_snake_case , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(_snake_case , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(_snake_case , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(F'''Invalid output: {output}''' )
return output_types
@is_tool_test
class lowerCamelCase__ :
def _UpperCamelCase ( self ):
self.assertTrue(hasattr(self.tool ,"""inputs""" ) )
self.assertTrue(hasattr(self.tool ,"""outputs""" ) )
UpperCAmelCase = self.tool.inputs
for _input in inputs:
if isinstance(_input ,A ):
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 ):
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = self.tool(*A )
# There is a single output
if len(self.tool.outputs ) == 1:
UpperCAmelCase = [outputs]
self.assertListEqual(output_types(A ) ,self.tool.outputs )
def _UpperCamelCase ( self ):
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 ):
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = self.tool(*A )
if not isinstance(A ,A ):
UpperCAmelCase = [outputs]
self.assertEqual(len(A ) ,len(self.tool.outputs ) )
for output, output_type in zip(A ,self.tool.outputs ):
UpperCAmelCase = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(A ,A ) )
def _UpperCamelCase ( self ):
UpperCAmelCase = create_inputs(self.tool.inputs )
UpperCAmelCase = []
for _input, input_type in zip(A ,self.tool.inputs ):
if isinstance(A ,A ):
_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(*A )
if not isinstance(A ,A ):
UpperCAmelCase = [outputs]
self.assertEqual(len(A ) ,len(self.tool.outputs ) )
| 234 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _UpperCAmelCase ( ) -> Union[str, Any]:
_lowerCAmelCase : int = 9
_lowerCAmelCase : Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_lowerCAmelCase : Tuple = kruskal(UpperCamelCase__ , UpperCamelCase__ )
_lowerCAmelCase : Dict = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(UpperCamelCase__ ) == sorted(UpperCamelCase__ )
| 309 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a__: Optional[int] = logging.get_logger(__name__)
a__: int = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a__: Optional[Any] = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a__: List[str] = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
'bert-base-german-dbmdz-cased': 512,
'bert-base-german-dbmdz-uncased': 512,
'TurkuNLP/bert-base-finnish-cased-v1': 512,
'TurkuNLP/bert-base-finnish-uncased-v1': 512,
'wietsedv/bert-base-dutch-cased': 512,
}
a__: Optional[Any] = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE = PRETRAINED_INIT_CONFIGURATION
__SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE = BertTokenizer
def __init__( self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
super().__init__(
__lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)
A__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
):
A__ = getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
A__ = do_lower_case
A__ = strip_accents
A__ = tokenize_chinese_chars
A__ = normalizer_class(**__lowerCamelCase )
A__ = do_lower_case
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase=None ):
A__ = [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 UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase = None ):
A__ = [self.sep_token_id]
A__ = [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,__lowerCamelCase,__lowerCamelCase = None ):
A__ = self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
return tuple(__lowerCamelCase )
| 193 | 0 |
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
if isinstance(a_ , a_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(a_ , a_ ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if num == 0:
return "0b0"
__A = False
if num < 0:
__A = True
__A = -num
__A = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(a_ ) for e in binary )
return "0b" + "".join(str(a_ ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
import copy
import re
class UpperCAmelCase :
'''simple docstring'''
snake_case_ = "hp"
snake_case_ = {}
snake_case_ = None
@classmethod
def UpperCamelCase_ ( cls : Dict ,A : Dict ,A : Any ):
__A = prefix
__A = defaults
cls.build_naming_info()
@staticmethod
def UpperCamelCase_ ( A : Dict ,A : int ):
if len(A ) == 0:
return ""
__A = None
if any(char.isdigit() for char in word ):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 ,len(A ) + 1 ):
__A = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
__A = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(A : str ):
__A = ""
while integer != 0:
__A = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
__A = 0
while True:
__A = word + "#" + int_to_alphabetic(A )
if sword in info["reverse_short_word"]:
continue
else:
__A = sword
break
__A = short_word
__A = word
return short_word
@staticmethod
def UpperCamelCase_ ( A : int ,A : Tuple ):
__A = param_name.split("_" )
__A = [TrialShortNamer.shortname_for_word(A ,A ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
__A = ["", "_"]
for separator in separators:
__A = separator.join(A )
if shortname not in info["reverse_short_param"]:
__A = shortname
__A = param_name
return shortname
return param_name
@staticmethod
def UpperCamelCase_ ( A : Optional[Any] ,A : Tuple ):
__A = TrialShortNamer.shortname_for_key(A ,A )
__A = short_name
__A = param_name
@classmethod
def UpperCamelCase_ ( cls : Dict ):
if cls.NAMING_INFO is not None:
return
__A = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
__A = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(A ,A )
__A = info
@classmethod
def UpperCamelCase_ ( cls : Dict ,A : List[str] ):
cls.build_naming_info()
assert cls.PREFIX is not None
__A = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
__A = cls.NAMING_INFO["short_param"][k]
if isinstance(A ,A ):
__A = 1 if v else 0
__A = "" if isinstance(A ,(int, float) ) else "-"
__A = f'''{key}{sep}{v}'''
name.append(A )
return "_".join(A )
@classmethod
def UpperCamelCase_ ( cls : Tuple ,A : Tuple ):
__A = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
__A = []
else:
__A = repr.split("_" )
__A = {}
for value in values:
if "-" in value:
__A , __A = value.split("-" )
else:
__A = re.sub("[0-9.]" ,"" ,A )
__A = float(re.sub("[^0-9.]" ,"" ,A ) )
__A = cls.NAMING_INFO["reverse_short_param"][p_k]
__A = p_v
for k in cls.DEFAULTS:
if k not in parameters:
__A = cls.DEFAULTS[k]
return parameters
| 124 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : str = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 274 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A : Dict = logging.get_logger(__name__)
def __lowerCamelCase ( __a :int=None , __a :Optional[Any]=None ) -> int:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__a )
@dataclass
class A :
'''simple docstring'''
__lowerCamelCase : List[str] = list_field(
default=[] , metadata={
'''help''': (
'''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
''' of all available models'''
)
} , )
__lowerCamelCase : List[int] = list_field(
default=[8] , metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''} )
__lowerCamelCase : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Use FP16 to accelerate inference.'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Benchmark training of model'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Verbose memory tracing'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''} , )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
} , )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Trace memory line by line'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save result to a CSV file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Save all print statements in a log file'''} )
__lowerCamelCase : bool = field(default=SCREAMING_SNAKE_CASE , metadata={'''help''': '''Whether to print environment information'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
''' for debugging / testing and on TPU.'''
)
} , )
__lowerCamelCase : str = field(
default=F'''inference_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''inference_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv.'''} , )
__lowerCamelCase : str = field(
default=F'''train_time_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''train_memory_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''} , )
__lowerCamelCase : str = field(
default=F'''env_info_{round(time() )}.csv''' , metadata={'''help''': '''CSV filename used if saving environment information.'''} , )
__lowerCamelCase : str = field(
default=F'''log_{round(time() )}.csv''' , metadata={'''help''': '''Log filename used if print statements are saved in log.'''} , )
__lowerCamelCase : int = field(default=3 , metadata={'''help''': '''Times an experiment will be run.'''} )
__lowerCamelCase : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={
'''help''': (
'''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
''' model weights.'''
)
} , )
def a_ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
warnings.warn(
f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" , __lowerCAmelCase , )
def a_ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def a_ ( self : Tuple ) -> List[str]:
"""simple docstring"""
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def a_ ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 274 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class lowerCamelCase_ ( _A ):
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : Union[str, "sqlalchemy.sql.Selectable"] , __lowerCamelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __lowerCamelCase : Optional[Features] = None , __lowerCamelCase : str = None , __lowerCamelCase : bool = False , **__lowerCamelCase : int , ) -> Dict:
super().__init__(features=__lowerCamelCase , cache_dir=__lowerCamelCase , keep_in_memory=__lowerCamelCase , **__lowerCamelCase )
A : Union[str, Any] = Sql(
cache_dir=__lowerCamelCase , features=__lowerCamelCase , sql=__lowerCamelCase , con=__lowerCamelCase , **__lowerCamelCase , )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[Any]:
A : str = None
A : List[Any] = None
A : Union[str, Any] = None
A : str = None
self.builder.download_and_prepare(
download_config=__lowerCamelCase , download_mode=__lowerCamelCase , verification_mode=__lowerCamelCase , base_path=__lowerCamelCase , )
# Build dataset for splits
A : str = self.builder.as_dataset(
split="train" , verification_mode=__lowerCamelCase , in_memory=self.keep_in_memory )
return dataset
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , __lowerCamelCase : Dataset , __lowerCamelCase : str , __lowerCamelCase : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , __lowerCamelCase : Optional[int] = None , __lowerCamelCase : Optional[int] = None , **__lowerCamelCase : Optional[Any] , ) -> Union[str, Any]:
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
A : Union[str, Any] = dataset
A : Union[str, Any] = name
A : List[Any] = con
A : Any = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
A : str = num_proc
A : int = to_sql_kwargs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
A : List[Any] = self.to_sql_kwargs.pop("sql" , __lowerCamelCase )
A : Union[str, Any] = self.to_sql_kwargs.pop("con" , __lowerCamelCase )
A : Optional[int] = self.to_sql_kwargs.pop("index" , __lowerCamelCase )
A : Union[str, Any] = self._write(index=__lowerCamelCase , **self.to_sql_kwargs )
return written
def SCREAMING_SNAKE_CASE__ ( self : List[str] , __lowerCamelCase : Dict ) -> Tuple:
A : Optional[int] = args
A : List[str] = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
A : List[str] = query_table(
table=self.dataset.data , key=slice(__lowerCamelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
A : List[Any] = batch.to_pandas()
A : List[str] = df.to_sql(self.name , self.con , index=__lowerCamelCase , **__lowerCamelCase )
return num_rows or len(__lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self : int , __lowerCamelCase : int , **__lowerCamelCase : List[str] ) -> int:
A : List[Any] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
A : List[str] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , __lowerCamelCase , __lowerCamelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written | 370 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def UpperCAmelCase ( _lowerCamelCase ):
A : List[Any] = R"\w+[.]\d+"
A : Optional[Any] = re.findall(_lowerCamelCase , _lowerCamelCase )
for pat in pats:
A : int = key.replace(_lowerCamelCase , "_".join(pat.split("." ) ) )
return key
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
A : Union[str, Any] = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
A : List[Any] = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
A : int = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
A : List[Any] = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
A : Optional[int] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
A : Union[str, Any] = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
A : List[Any] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
A : int = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
A : List[str] = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
A : Optional[Any] = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=42 ):
# Step 1: Convert pytorch tensor to numpy
A : Union[str, Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
A : Dict = flax_model.init_weights(PRNGKey(_lowerCamelCase ) )
A : Dict = flatten_dict(_lowerCamelCase )
A : Dict = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
A : Tuple = rename_key(_lowerCamelCase )
A : List[str] = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
A , A : str = rename_key_and_reshape_tensor(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
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}.""" )
# also add unexpected weight so that warning is thrown
A : Union[str, Any] = jnp.asarray(_lowerCamelCase )
return unflatten_dict(_lowerCamelCase ) | 256 | 0 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
_snake_case = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']
class UpperCamelCase ( __lowerCamelCase ):
def __init__( self : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : str , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : Union[str, Any]=1 ) -> Optional[Any]:
_a : Optional[int] = tokenizer
_a : List[Any] = dataset
_a : Any = len(__lowercase ) if n_tasks is None else n_tasks
_a : List[Any] = n_copies
def __iter__( self : Tuple ) -> Optional[Any]:
_a : Union[str, Any] = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["""prompt"""].strip() )
_a : Optional[int] = self.tokenizer(__lowercase , padding=__lowercase , return_tensors="""pt""" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class UpperCamelCase ( __lowerCamelCase ):
def __init__( self : Union[str, Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ) -> Tuple:
_a : List[str] = start_length
_a : Any = eof_strings
_a : Optional[Any] = tokenizer
def __call__( self : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , **UpperCAmelCase__ : Optional[int] ) -> Dict:
_a : Tuple = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
_a : Union[str, Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(__lowercase )
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : List[Any] = re.split("""(%s)""" % """|""".join(__lowerCAmelCase ) , __lowerCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=2_0 , **UpperCamelCase__ ):
'''simple docstring'''
_a : Optional[int] = defaultdict(__lowerCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__lowerCAmelCase ) ):
with torch.no_grad():
_a : List[Any] = batch['''ids'''].shape[-1]
_a : str = accelerator.unwrap_model(__lowerCAmelCase ).generate(
input_ids=batch["""ids"""][:, : batch["""input_len"""]] , num_return_sequences=__lowerCAmelCase , **__lowerCAmelCase )
# each task is generated batch_size times
_a : str = batch['''task_id'''].repeat(__lowerCAmelCase )
_a : Optional[int] = accelerator.pad_across_processes(
__lowerCAmelCase , dim=1 , pad_index=tokenizer.pad_token_id )
_a : Any = accelerator.gather((generated_tokens, generated_tasks) )
_a : Union[str, Any] = generated_tokens.cpu().numpy()
_a : Optional[Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__lowerCAmelCase , __lowerCAmelCase ):
gen_token_dict[task].append(__lowerCAmelCase )
_a : List[Any] = [[] for _ in range(__lowerCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
_a : int = tokenizer.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
code_gens[task].append(remove_last_block(__lowerCAmelCase ) )
return code_gens
def lowerCAmelCase__ ( ):
'''simple docstring'''
_a : List[str] = HfArgumentParser(__lowerCAmelCase )
_a : Optional[Any] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
_a : Union[str, Any] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
_a : Union[str, Any] = '''false'''
if args.num_workers is None:
_a : Union[str, Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
_a : Dict = Accelerator()
set_seed(args.seed , device_specific=__lowerCAmelCase )
# Load model and tokenizer
_a : str = AutoTokenizer.from_pretrained(args.model_ckpt )
_a : List[str] = tokenizer.eos_token
_a : Optional[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
_a : List[Any] = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __lowerCAmelCase , __lowerCAmelCase )] ),
}
# Load evaluation dataset and metric
_a : List[str] = load_dataset("""openai_humaneval""" )
_a : str = load_metric("""code_eval""" )
_a : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval["""test"""] )
_a : str = args.n_samples // args.batch_size
_a : Tuple = TokenizedDataset(__lowerCAmelCase , human_eval["""test"""] , n_copies=__lowerCAmelCase , n_tasks=__lowerCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
_a : Tuple = DataLoader(__lowerCAmelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
_a : Optional[Any] = code_eval_metric.compute(references=[""""""] , predictions=[[""""""]] )
except ValueError as exception:
print(
"""Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"""
""" flag to enable code evaluation.""" )
raise exception
_a : Optional[Any] = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
_a : Dict = complete_code(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , n_tasks=__lowerCAmelCase , batch_size=args.batch_size , **__lowerCAmelCase , )
if accelerator.is_main_process:
_a : Optional[Any] = []
for task in tqdm(range(__lowerCAmelCase ) ):
_a : str = human_eval['''test'''][task]['''test''']
_a : int = F"""check({human_eval['test'][task]['entry_point']})"""
references.append("""\n""" + test_func + """\n""" + entry_point )
# Evaluate completions with "code_eval" metric
_a : Any = code_eval_metric.compute(
references=__lowerCAmelCase , predictions=__lowerCAmelCase , num_workers=args.num_workers )
print(F"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , """w""" ) as fp:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 294 |
import math
import sys
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
snake_case__ : Optional[Any] = ''''''
try:
with open(__lowerCAmelCase , '''rb''' ) as binary_file:
snake_case__ : int = binary_file.read()
for dat in data:
snake_case__ : Any = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
snake_case__ : List[str] = {'''0''': '''0''', '''1''': '''1'''}
snake_case__ , snake_case__ : List[Any] = '''''', ''''''
snake_case__ : Tuple = len(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
snake_case__ : Tuple = lexicon[curr_string]
result += last_match_id
snake_case__ : Any = last_match_id + '''0'''
if math.loga(__lowerCAmelCase ).is_integer():
snake_case__ : Tuple = {}
for curr_key in list(__lowerCAmelCase ):
snake_case__ : Union[str, Any] = lexicon.pop(__lowerCAmelCase )
snake_case__ : Optional[Any] = new_lex
snake_case__ : Tuple = last_match_id + '''1'''
index += 1
snake_case__ : Dict = ''''''
return result
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> None:
"""simple docstring"""
snake_case__ : Dict = 8
try:
with open(__lowerCAmelCase , '''wb''' ) as opened_file:
snake_case__ : Union[str, Any] = [
to_write[i : i + byte_length]
for i in range(0 , len(__lowerCAmelCase ) , __lowerCAmelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(__lowerCAmelCase , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
snake_case__ : Any = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
snake_case__ : Optional[int] = data_bits[counter:]
snake_case__ : int = data_bits[counter + 1 :]
return data_bits
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> None:
"""simple docstring"""
snake_case__ : Union[str, Any] = read_file_binary(__lowerCAmelCase )
snake_case__ : List[str] = remove_prefix(__lowerCAmelCase )
snake_case__ : Any = decompress_data(__lowerCAmelCase )
write_file_binary(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 230 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_snake_case = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_snake_case = '''main'''
# Default branch name
_snake_case = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_snake_case = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_snake_case = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_snake_case = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def _UpperCamelCase ( ) -> Union[str, Any]:
print("Welcome!" )
yield
print("Bye!" )
@contextlib.contextmanager
def _UpperCamelCase ( ) -> List[str]:
print("Bonjour!" )
yield
print("Au revoir!" )
class _snake_case ( unittest.TestCase ):
def _lowerCamelCase ( self: Union[str, Any] ) -> str:
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec("transformers" ) is not None
class _snake_case ( unittest.TestCase ):
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: List[str] ) -> Tuple:
with ContextManagers([] ):
print("Transformers are awesome!" )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , "Transformers are awesome!\n" )
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO )
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: Union[str, Any] ) -> Optional[Any]:
with ContextManagers([context_en()] ):
print("Transformers are awesome!" )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , "Welcome!\nTransformers are awesome!\nBye!\n" )
@unittest.mock.patch("sys.stdout" , new_callable=io.StringIO )
def _lowerCamelCase ( self: Dict , __lowerCamelCase: List[Any] ) -> Union[str, Any]:
with ContextManagers([context_fr(), context_en()] ):
print("Transformers are awesome!" )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n" )
@require_torch
def _lowerCamelCase ( self: List[str] ) -> Optional[int]:
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels"] )
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels", "next_sentence_label"] )
self.assertEqual(find_labels(__lowerCamelCase ) , ["start_positions", "end_positions"] )
class _snake_case ( _lowercase ):
pass
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels"] )
@require_tf
def _lowerCamelCase ( self: List[Any] ) -> Union[str, Any]:
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels"] )
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels", "next_sentence_label"] )
self.assertEqual(find_labels(__lowerCamelCase ) , ["start_positions", "end_positions"] )
class _snake_case ( _lowercase ):
pass
self.assertEqual(find_labels(__lowerCamelCase ) , ["labels"] )
@require_flax
def _lowerCamelCase ( self: Dict ) -> int:
# Flax models don't have labels
self.assertEqual(find_labels(__lowerCamelCase ) , [] )
self.assertEqual(find_labels(__lowerCamelCase ) , [] )
self.assertEqual(find_labels(__lowerCamelCase ) , [] )
class _snake_case ( _lowercase ):
pass
self.assertEqual(find_labels(__lowerCamelCase ) , [] )
| 342 | import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_snake_case = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
('''beta''', '''bias'''),
('''gamma''', '''weight'''),
('''pegasus''', '''model'''),
]
_snake_case = [
('''.output.dense''', '''.fc2'''),
('''intermediate.LayerNorm''', '''final_layer_norm'''),
('''intermediate.dense''', '''fc1'''),
]
_snake_case = (
INIT_COMMON
+ [
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.out_proj'''),
('''attention.self''', '''self_attn'''),
('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
('''attention.encdec''', '''encoder_attn'''),
('''key''', '''k_proj'''),
('''value''', '''v_proj'''),
('''query''', '''q_proj'''),
('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
]
+ END_COMMON
)
_snake_case = (
INIT_COMMON
+ [
('''embeddings.word_embeddings''', '''shared.weight'''),
('''embeddings.position_embeddings''', '''embed_positions.weight'''),
('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
('''attention.output.dense''', '''self_attn.output'''),
('''attention.self''', '''self_attn.self'''),
('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
]
+ END_COMMON
)
_snake_case = [
'''encdec/key/bias''',
'''encdec/query/bias''',
'''encdec/value/bias''',
'''self/key/bias''',
'''self/query/bias''',
'''self/value/bias''',
'''encdec_output/dense/bias''',
'''attention/output/dense/bias''',
]
def _UpperCamelCase ( snake_case__, snake_case__ ) -> Any:
for tf_name, hf_name in patterns:
__UpperCAmelCase : Optional[int] = k.replace(snake_case__, snake_case__ )
return k
def _UpperCamelCase ( snake_case__, snake_case__ ) -> BigBirdPegasusForConditionalGeneration:
__UpperCAmelCase : Dict = BigBirdPegasusConfig(**snake_case__ )
__UpperCAmelCase : Dict = BigBirdPegasusForConditionalGeneration(snake_case__ )
__UpperCAmelCase : Optional[Any] = torch_model.state_dict()
__UpperCAmelCase : Optional[int] = {}
# separating decoder weights
__UpperCAmelCase : List[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )}
__UpperCAmelCase : str = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder" )}
for k, v in tqdm(decoder_weights.items(), "tf -> hf conversion" ):
__UpperCAmelCase : Optional[int] = [k.endswith(snake_case__ ) for ending in KEYS_TO_IGNORE]
if any(snake_case__ ):
continue
__UpperCAmelCase : List[str] = DECODER_PATTERNS
__UpperCAmelCase : str = rename_state_dict_key(snake_case__, snake_case__ )
if new_k not in state_dict:
raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
__UpperCAmelCase : Optional[int] = v.T
__UpperCAmelCase : str = torch.from_numpy(snake_case__ )
assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
for k, v in tqdm(remaining_weights.items(), "tf -> hf conversion" ):
__UpperCAmelCase : int = [k.endswith(snake_case__ ) for ending in KEYS_TO_IGNORE]
if any(snake_case__ ):
continue
__UpperCAmelCase : Optional[Any] = REMAINING_PATTERNS
__UpperCAmelCase : Optional[int] = rename_state_dict_key(snake_case__, snake_case__ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ["dense", "query", "key", "value"] ):
__UpperCAmelCase : List[Any] = v.T
__UpperCAmelCase : List[str] = torch.from_numpy(snake_case__ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
__UpperCAmelCase : List[Any] = mapping["model.embed_positions.weight"]
__UpperCAmelCase : Optional[Any] = mapping.pop("model.embed_positions.weight" )
__UpperCAmelCase , __UpperCAmelCase : Any = torch_model.load_state_dict(snake_case__, strict=snake_case__ )
__UpperCAmelCase : str = [
k
for k in missing
if k
not in [
"final_logits_bias",
"model.encoder.embed_tokens.weight",
"model.decoder.embed_tokens.weight",
"lm_head.weight",
]
]
assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], f'''no matches found for the following tf keys {extra}'''
return torch_model
def _UpperCamelCase ( snake_case__ ) -> Dict:
__UpperCAmelCase : Tuple = tf.train.list_variables(snake_case__ )
__UpperCAmelCase : List[str] = {}
__UpperCAmelCase : str = ["global_step"]
for name, shape in tqdm(snake_case__, desc="converting tf checkpoint to dict" ):
__UpperCAmelCase : Tuple = any(pat in name for pat in ignore_name )
if skip_key:
continue
__UpperCAmelCase : Optional[Any] = tf.train.load_variable(snake_case__, snake_case__ )
__UpperCAmelCase : Tuple = array
return tf_weights
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Dict:
__UpperCAmelCase : str = get_tf_weights_as_numpy(snake_case__ )
__UpperCAmelCase : List[Any] = convert_bigbird_pegasus(snake_case__, snake_case__ )
torch_model.save_pretrained(snake_case__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
_snake_case = parser.parse_args()
_snake_case = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 342 | 1 |
'''simple docstring'''
import math
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 ,int(math.sqrt(_UpperCAmelCase ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int = 10001 ) -> int:
try:
_a : List[str] =int(_UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
_a : list[int] =[]
_a : Any =2
while len(_UpperCAmelCase ) < nth:
if is_prime(_UpperCAmelCase ):
primes.append(_UpperCAmelCase )
num += 1
else:
num += 1
return primes[len(_UpperCAmelCase ) - 1]
if __name__ == "__main__":
print(F"{solution() = }")
| 276 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" ,[
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10 )]),
({"""num_shards""": 10, """max_num_jobs""": 10}, [range(_UpperCAmelCase ,i + 1 ) for i in range(10 )]),
({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1 )]),
({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4 ), range(4 ,7 ), range(7 ,10 )]),
({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1 ), range(1 ,2 ), range(2 ,3 )]),
] ,)
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Union[str, Any] ,_UpperCAmelCase : Dict ) -> Optional[Any]:
_a : Tuple =_distribute_shards(**_UpperCAmelCase )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, max_num_jobs, expected""" ,[
({"""foo""": 0}, 10, [{"""foo""": 0}]),
({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
] ,)
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Tuple ,_UpperCAmelCase : List[Any] ,_UpperCAmelCase : Union[str, Any] ) -> List[str]:
_a : List[str] =_split_gen_kwargs(_UpperCAmelCase ,_UpperCAmelCase )
assert out == expected
@pytest.mark.parametrize(
"""gen_kwargs, expected""" ,[
({"""foo""": 0}, 1),
({"""shards""": [0]}, 1),
({"""shards""": [0, 1, 2, 3]}, 4),
({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
] ,)
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : List[Any] ) -> Union[str, Any]:
if expected is RuntimeError:
with pytest.raises(_UpperCAmelCase ):
_number_of_shards_in_gen_kwargs(_UpperCAmelCase )
else:
_a : Dict =_number_of_shards_in_gen_kwargs(_UpperCAmelCase )
assert out == expected
| 276 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
snake_case_ = HUGGINGFACE_HUB_CACHE
snake_case_ = '''config.json'''
snake_case_ = '''diffusion_pytorch_model.bin'''
snake_case_ = '''diffusion_flax_model.msgpack'''
snake_case_ = '''model.onnx'''
snake_case_ = '''diffusion_pytorch_model.safetensors'''
snake_case_ = '''weights.pb'''
snake_case_ = '''https://huggingface.co'''
snake_case_ = default_cache_path
snake_case_ = '''diffusers_modules'''
snake_case_ = os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
snake_case_ = ['''fp16''', '''non-ema''']
snake_case_ = '''.self_attn'''
| 356 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
snake_case_ = logging.get_logger(__name__)
snake_case_ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
snake_case_ = {
'''vocab_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
},
'''merges_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
'''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
'''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
'''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
'''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
},
}
snake_case_ = {
'''gpt2''': 1_024,
'''gpt2-medium''': 1_024,
'''gpt2-large''': 1_024,
'''gpt2-xl''': 1_024,
'''distilgpt2''': 1_024,
}
class SCREAMING_SNAKE_CASE__ (__snake_case ):
__lowerCamelCase : List[str] = VOCAB_FILES_NAMES
__lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : int = ["""input_ids""", """attention_mask"""]
__lowerCamelCase : str = GPTaTokenizer
def __init__( self , a=None , a=None , a=None , a="<|endoftext|>" , a="<|endoftext|>" , a="<|endoftext|>" , a=False , **a , ):
super().__init__(
a , a , tokenizer_file=a , unk_token=a , bos_token=a , eos_token=a , add_prefix_space=a , **a , )
lowercase__ : int = kwargs.pop('add_bos_token' , a)
lowercase__ : List[str] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get('add_prefix_space' , a) != add_prefix_space:
lowercase__ : Optional[Any] = getattr(a , pre_tok_state.pop('type'))
lowercase__ : List[Any] = add_prefix_space
lowercase__ : str = pre_tok_class(**a)
lowercase__ : Tuple = add_prefix_space
def snake_case_ ( self , *a , **a):
lowercase__ : Tuple = kwargs.get('is_split_into_words' , a)
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*a , **a)
def snake_case_ ( self , *a , **a):
lowercase__ : Optional[Any] = kwargs.get('is_split_into_words' , a)
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*a , **a)
def snake_case_ ( self , a , a = None):
lowercase__ : Any = self._tokenizer.model.save(a , name=a)
return tuple(a)
def snake_case_ ( self , a):
lowercase__ : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(a , add_special_tokens=a) + [self.eos_token_id])
if len(a) > self.model_max_length:
lowercase__ : Union[str, Any] = input_ids[-self.model_max_length :]
return input_ids
| 216 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def a__ ( ):
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 108 | from numpy import exp, pi, sqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : float = 0.0 , __UpperCamelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 219 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
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 A ( _a ,unittest.TestCase ):
lowercase_ = KandinskyVaaInpaintPipeline
lowercase_ = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
lowercase_ = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
lowercase_ = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowercase_ = False
@property
def __lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
return 32
@property
def __lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
return self.time_input_dim
@property
def __lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self : Any ) -> Tuple:
"""simple docstring"""
return 1_00
@property
def __lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
_a = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''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,
}
_a = UNetaDConditionModel(**lowerCAmelCase_ )
return model
@property
def __lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["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",
],
"vq_embed_dim": 4,
}
@property
def __lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
_a = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
_a = self.dummy_unet
_a = self.dummy_movq
_a = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=lowerCAmelCase_ , set_alpha_to_one=lowerCAmelCase_ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=lowerCAmelCase_ , )
_a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any]=0 ) -> str:
"""simple docstring"""
_a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowerCAmelCase_ )
# create init_image
_a = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_a = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a = Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )
# create mask
_a = np.ones((64, 64) , dtype=np.floataa )
_a = 0
if str(lowerCAmelCase_ ).startswith('''mps''' ):
_a = torch.manual_seed(lowerCAmelCase_ )
else:
_a = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_a = {
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_a = '''cpu'''
_a = self.get_dummy_components()
_a = self.pipeline_class(**lowerCAmelCase_ )
_a = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
_a = pipe(**self.get_dummy_inputs(lowerCAmelCase_ ) )
_a = output.images
_a = pipe(
**self.get_dummy_inputs(lowerCAmelCase_ ) , return_dict=lowerCAmelCase_ , )[0]
_a = image[0, -3:, -3:, -1]
_a = image_from_tuple[0, -3:, -3:, -1]
print(F'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
_a = np.array(
[0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8] )
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()}'
def __lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
_a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''' )
_a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a = np.ones((7_68, 7_68) , dtype=np.floataa )
_a = 0
_a = '''a hat'''
_a = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(lowerCAmelCase_ )
_a = KandinskyVaaInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder-inpaint''' , torch_dtype=torch.floataa )
_a = pipeline.to(lowerCAmelCase_ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase_ )
_a = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a = pipe_prior(
lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a = pipeline(
image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , image_embeds=lowerCAmelCase_ , negative_image_embeds=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='''np''' , )
_a = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(lowerCAmelCase_ , lowerCAmelCase_ )
| 179 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_snake_case : int = 'docs/source/en/_toctree.yml'
def snake_case_ (UpperCamelCase : Optional[int] ):
'''simple docstring'''
_a = defaultdict(UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
_a = [key for key, value in counts.items() if value > 1]
_a = []
for duplicate_key in duplicates:
_a = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(UpperCamelCase ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(UpperCamelCase , key=lambda UpperCamelCase : s["title"].lower() )
def snake_case_ (UpperCamelCase : str=False ):
'''simple docstring'''
with open(UpperCamelCase , encoding='''utf-8''' ) as f:
_a = yaml.safe_load(f.read() )
# Get to the API doc
_a = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a = content[api_idx]['''sections''']
# Then to the model doc
_a = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_a = api_doc[model_idx]['''sections''']
_a = [(idx, section) for idx, section in enumerate(UpperCamelCase ) if '''sections''' in section]
_a = False
for idx, modality_doc in modalities_docs:
_a = modality_doc['''sections''']
_a = clean_model_doc_toc(UpperCamelCase )
if old_modality_doc != new_modality_doc:
_a = True
if overwrite:
_a = new_modality_doc
if diff:
if overwrite:
_a = model_doc
_a = api_doc
with open(UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(UpperCamelCase , allow_unicode=UpperCamelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
_snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
_snake_case : List[Any] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 179 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
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
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class __a ( unittest.TestCase ):
@slow
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
UpperCamelCase__ : Optional[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = TFAutoModel.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
UpperCamelCase__ : str = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = TFAutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = AutoModelForPreTraining.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : str ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = TFAutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Union[str, Any] = TFAutoModelForCausalLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : int = AutoModelForCausalLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Tuple = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : int ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : List[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = TFAutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Tuple = TFAutoModelForMaskedLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : int = AutoModelForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Optional[int] = AutoModelForMaskedLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : Any ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : List[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = TFAutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(
SCREAMING_SNAKE_CASE , output_loading_info=SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
UpperCamelCase__ : Optional[int] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = TFAutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def __lowercase ( self : Tuple ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
UpperCamelCase__ : List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = TFAutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = AutoModelForQuestionAnswering.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : int = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE ) , 1_44_10 )
UpperCamelCase__ : Any = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE ) , 1_44_10 )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCamelCase__ : List[Any] = TFAutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_pt=SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE ) , 1_44_10 )
UpperCamelCase__ : Optional[int] = AutoModelWithLMHead.from_pretrained(SCREAMING_SNAKE_CASE , from_tf=SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=SCREAMING_SNAKE_CASE ) , 1_44_10 ) | 189 |
from math import factorial
class __a :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : Tuple = real
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Union[str, Any] = [1] * rank
else:
UpperCamelCase__ : int = rank
def __repr__( self : Tuple ):
'''simple docstring'''
return (
F'{self.real}+'
F'{"+".join(str(SCREAMING_SNAKE_CASE )+"E"+str(n+1 )for n,dual in enumerate(self.duals ) )}'
)
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , SCREAMING_SNAKE_CASE )
def __add__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return Dual(self.real + other , self.duals )
UpperCamelCase__ : Optional[int] = self.duals.copy()
UpperCamelCase__ : Any = other.duals.copy()
if len(SCREAMING_SNAKE_CASE ) > len(SCREAMING_SNAKE_CASE ):
o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE ) - len(SCREAMING_SNAKE_CASE )) )
elif len(SCREAMING_SNAKE_CASE ) < len(SCREAMING_SNAKE_CASE ):
s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE ) - len(SCREAMING_SNAKE_CASE )) )
UpperCamelCase__ : Optional[int] = []
for i in range(len(SCREAMING_SNAKE_CASE ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , SCREAMING_SNAKE_CASE )
_lowerCAmelCase : Dict = __add__
def __sub__( self : Tuple , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
return self + other * -1
def __mul__( self : int , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : str = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[Any] = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , SCREAMING_SNAKE_CASE )
_lowerCAmelCase : Union[str, Any] = __mul__
def __truediv__( self : List[Any] , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : str = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , SCREAMING_SNAKE_CASE )
raise ValueError
def __floordiv__( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Dict = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , SCREAMING_SNAKE_CASE )
raise ValueError
def __pow__( self : str , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if n < 0 or isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError("power must be a positive integer" )
if n == 0:
return 1
if n == 1:
return self
UpperCamelCase__ : str = self
for _ in range(n - 1 ):
x *= self
return x
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
if not callable(__lowerCAmelCase ):
raise ValueError("differentiate() requires a function as input for func" )
if not isinstance(__lowerCAmelCase , (float, int) ):
raise ValueError("differentiate() requires a float as input for position" )
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError("differentiate() requires an int as input for order" )
UpperCamelCase__ : Optional[Any] = Dual(__lowerCAmelCase , 1 )
UpperCamelCase__ : Any = func(__lowerCAmelCase )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> List[Any]:
return y**2 * y**4
print(differentiate(f, 9, 2)) | 189 | 1 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase_ ( a_ ):
def __init__( self , snake_case__ , snake_case__ , snake_case__ ) -> Optional[int]:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=snake_case__ , unet=snake_case__ , scheduler=snake_case__ )
@torch.no_grad()
def __call__( self , snake_case__ = 1 , snake_case__ = None , snake_case__ = 0.0 , snake_case__ = 50 , snake_case__ = "pil" , snake_case__ = True , **snake_case__ , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
UpperCAmelCase = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=snake_case__ , )
UpperCAmelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(snake_case__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
UpperCAmelCase = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase = {}
if accepts_eta:
UpperCAmelCase = eta
for t in self.progress_bar(self.scheduler.timesteps ):
UpperCAmelCase = self.scheduler.scale_model_input(snake_case__ , snake_case__ )
# predict the noise residual
UpperCAmelCase = self.unet(snake_case__ , snake_case__ ).sample
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase = self.scheduler.step(snake_case__ , snake_case__ , snake_case__ , **snake_case__ ).prev_sample
# decode the image latents with the VAE
UpperCAmelCase = self.vqvae.decode(snake_case__ ).sample
UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase = self.numpy_to_pil(snake_case__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=snake_case__ )
| 248 |
"""simple docstring"""
def _lowerCAmelCase ( ):
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'{solution() = }')
| 248 | 1 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Optional[int]:
try:
with open(__UpperCamelCase , """rb""" ) as flax_state_f:
UpperCamelCase = from_bytes(__UpperCamelCase , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(__UpperCamelCase ) as f:
if f.read().startswith("""version""" ):
raise OSError(
"""You seem to have cloned a repository without having git-lfs installed. Please"""
""" install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
""" folder you cloned.""" )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(__UpperCamelCase , __UpperCamelCase )
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> Tuple:
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading 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
UpperCamelCase = flatten_dict(jax.tree_util.tree_map(lambda __UpperCamelCase : x.dtype == jnp.bfloataa , __UpperCamelCase ) ).values()
if any(__UpperCamelCase ):
# convert all weights to fp32 if they 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.""" )
UpperCamelCase = jax.tree_util.tree_map(
lambda __UpperCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __UpperCamelCase )
UpperCamelCase = """"""
UpperCamelCase = flatten_dict(__UpperCamelCase , sep=""".""" )
UpperCamelCase = pt_model.state_dict()
# keep track of unexpected & missing keys
UpperCamelCase = []
UpperCamelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCamelCase = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
UpperCamelCase = jnp.transpose(__UpperCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
UpperCamelCase = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
UpperCamelCase = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(__UpperCamelCase ):
UpperCamelCase = (
flax_key_tuple_string.replace("""_0""" , """.0""" )
.replace("""_1""" , """.1""" )
.replace("""_2""" , """.2""" )
.replace("""_3""" , """.3""" )
.replace("""_4""" , """.4""" )
.replace("""_5""" , """.5""" )
.replace("""_6""" , """.6""" )
.replace("""_7""" , """.7""" )
.replace("""_8""" , """.8""" )
.replace("""_9""" , """.9""" )
)
UpperCamelCase = """.""".join(__UpperCamelCase )
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
UpperCamelCase = np.asarray(__UpperCamelCase ) if not isinstance(__UpperCamelCase , np.ndarray ) else flax_tensor
UpperCamelCase = 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
UpperCamelCase = 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).""" )
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.""" )
return pt_model
| 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase = 1
UpperCamelCase = 1
while repunit:
UpperCamelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowercase__ ( __UpperCamelCase = 1000000 )-> int:
UpperCamelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__UpperCamelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 321 | 1 |
"""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 UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = []
for line in lines:
a_ = re.sub(r"#.*" , "" , UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(UpperCAmelCase )
a_ = "\n".join(UpperCAmelCase )
# Make a hash from all this code
a_ = full_str.encode("utf-8" )
return shaaaa(UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase_ = {
'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
UpperCamelCase_ = {
'.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})
UpperCamelCase_ = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
UpperCamelCase_ = {}
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') | 362 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , __UpperCAmelCase = "▁" , __UpperCAmelCase = True , __UpperCAmelCase = "<unk>" , __UpperCAmelCase = "</s>" , __UpperCAmelCase = "<pad>" , ) ->str:
a_ = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
a_ = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
a_ = token_dict["token"]
a_ = Tokenizer(Unigram())
a_ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}") , " "),
normalizers.Lowercase(),
])
a_ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase),
pre_tokenizers.Digits(individual_digits=__UpperCAmelCase),
pre_tokenizers.Punctuation(),
])
a_ = decoders.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase)
a_ = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
a_ = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(__UpperCAmelCase , __UpperCAmelCase)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = 80_00 , __UpperCAmelCase = True , ) ->Optional[Any]:
a_ = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a_ = [files]
self._tokenizer.train(__UpperCAmelCase , trainer=__UpperCAmelCase)
self.add_unk_id()
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = 80_00 , __UpperCAmelCase = True , ) ->int:
a_ = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
self._tokenizer.train_from_iterator(__UpperCAmelCase , trainer=__UpperCAmelCase)
self.add_unk_id()
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ = json.loads(self._tokenizer.to_str())
a_ = self.special_tokens["unk"]["id"]
a_ = Tokenizer.from_str(json.dumps(__UpperCAmelCase)) | 303 | 0 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 55 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : int ="dpr"
def __init__( self , snake_case__=30_522 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=2 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__="absolute" , snake_case__ = 0 , **snake_case__ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
lowerCAmelCase : Union[str, Any] = vocab_size
lowerCAmelCase : str = hidden_size
lowerCAmelCase : Any = num_hidden_layers
lowerCAmelCase : Optional[int] = num_attention_heads
lowerCAmelCase : Union[str, Any] = hidden_act
lowerCAmelCase : Dict = intermediate_size
lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase : Dict = attention_probs_dropout_prob
lowerCAmelCase : Dict = max_position_embeddings
lowerCAmelCase : Tuple = type_vocab_size
lowerCAmelCase : Any = initializer_range
lowerCAmelCase : Any = layer_norm_eps
lowerCAmelCase : Dict = projection_dim
lowerCAmelCase : Dict = position_embedding_type
| 108 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''sew-d'''
def __init__( self , lowerCamelCase__=32 , lowerCamelCase__=768 , lowerCamelCase__=12 , lowerCamelCase__=12 , lowerCamelCase__=3_072 , lowerCamelCase__=2 , lowerCamelCase__=512 , lowerCamelCase__=256 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=("p2c", "c2p") , lowerCamelCase__="layer_norm" , lowerCamelCase__="gelu_python" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=0.0 , lowerCamelCase__=0.1 , lowerCamelCase__=0.02 , lowerCamelCase__=1e-7 , lowerCamelCase__=1e-5 , lowerCamelCase__="group" , lowerCamelCase__="gelu" , lowerCamelCase__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , lowerCamelCase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , lowerCamelCase__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , lowerCamelCase__=False , lowerCamelCase__=128 , lowerCamelCase__=16 , lowerCamelCase__=True , lowerCamelCase__=0.05 , lowerCamelCase__=10 , lowerCamelCase__=2 , lowerCamelCase__=0.0 , lowerCamelCase__=10 , lowerCamelCase__=0 , lowerCamelCase__="mean" , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=256 , lowerCamelCase__=0 , lowerCamelCase__=1 , lowerCamelCase__=2 , **lowerCamelCase__ , ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_norm
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = squeeze_factor
__lowerCamelCase = max_position_embeddings
__lowerCamelCase = position_buckets
__lowerCamelCase = share_att_key
__lowerCamelCase = relative_attention
__lowerCamelCase = norm_rel_ebd
__lowerCamelCase = list(lowerCamelCase__ )
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = feature_layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
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)`,'
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCamelCase = apply_spec_augment
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# sequence classification
__lowerCamelCase = use_weighted_layer_sum
__lowerCamelCase = classifier_proj_size
@property
def lowercase_ ( self ) -> Any:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 348 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : Dict = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = '''roberta'''
def __init__( self , _UpperCAmelCase=5_0265 , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=1 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , _UpperCAmelCase="absolute" , _UpperCAmelCase=True , _UpperCAmelCase=None , **_UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase)
__A : Optional[int] = vocab_size
__A : int = hidden_size
__A : Union[str, Any] = num_hidden_layers
__A : List[str] = num_attention_heads
__A : Optional[int] = hidden_act
__A : str = intermediate_size
__A : Union[str, Any] = hidden_dropout_prob
__A : Dict = attention_probs_dropout_prob
__A : int = max_position_embeddings
__A : str = type_vocab_size
__A : Any = initializer_range
__A : int = layer_norm_eps
__A : Optional[int] = position_embedding_type
__A : int = use_cache
__A : Union[str, Any] = classifier_dropout
class SCREAMING_SNAKE_CASE (a__ ):
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
if self.task == "multiple-choice":
__A : str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__A : List[str] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
]) | 190 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : int = {
'''andreasmadsen/efficient_mlm_m0.40''': (
'''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = '''roberta-prelayernorm'''
def __init__( self , _UpperCAmelCase=5_0265 , _UpperCAmelCase=768 , _UpperCAmelCase=12 , _UpperCAmelCase=12 , _UpperCAmelCase=3072 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=1e-1_2 , _UpperCAmelCase=1 , _UpperCAmelCase=0 , _UpperCAmelCase=2 , _UpperCAmelCase="absolute" , _UpperCAmelCase=True , _UpperCAmelCase=None , **_UpperCAmelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase)
__A : Optional[int] = vocab_size
__A : List[Any] = hidden_size
__A : Optional[int] = num_hidden_layers
__A : Optional[Any] = num_attention_heads
__A : List[str] = hidden_act
__A : Dict = intermediate_size
__A : Optional[int] = hidden_dropout_prob
__A : Optional[Any] = attention_probs_dropout_prob
__A : Tuple = max_position_embeddings
__A : Union[str, Any] = type_vocab_size
__A : Any = initializer_range
__A : str = layer_norm_eps
__A : int = position_embedding_type
__A : Optional[Any] = use_cache
__A : Any = classifier_dropout
class SCREAMING_SNAKE_CASE (a__ ):
@property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
if self.task == "multiple-choice":
__A : Dict = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__A : Union[str, Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
]) | 190 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
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_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_lowerCamelCase = logging.get_logger(__name__)
class _snake_case (__SCREAMING_SNAKE_CASE):
__A : Optional[Any] =["pixel_values"]
def __init__( self ,_snake_case = True ,_snake_case = None ,_snake_case = PILImageResampling.BILINEAR ,_snake_case = True ,_snake_case = None ,_snake_case = True ,_snake_case = 1 / 2_55 ,_snake_case = True ,_snake_case = None ,_snake_case = None ,**_snake_case ,):
super().__init__(**_snake_case )
UpperCAmelCase_ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase_ : Optional[Any] = get_size_dict(_snake_case ,default_to_square=_snake_case )
UpperCAmelCase_ : Optional[Any] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase_ : Dict = get_size_dict(_snake_case ,param_name="crop_size" )
UpperCAmelCase_ : str = do_resize
UpperCAmelCase_ : Tuple = size
UpperCAmelCase_ : Optional[Any] = resample
UpperCAmelCase_ : Tuple = do_center_crop
UpperCAmelCase_ : List[str] = crop_size
UpperCAmelCase_ : List[str] = do_rescale
UpperCAmelCase_ : List[str] = rescale_factor
UpperCAmelCase_ : Tuple = do_normalize
UpperCAmelCase_ : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ : List[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case = PILImageResampling.BICUBIC ,_snake_case = None ,**_snake_case ,):
UpperCAmelCase_ : int = get_size_dict(_snake_case ,default_to_square=_snake_case )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
UpperCAmelCase_ : Optional[int] = get_resize_output_image_size(_snake_case ,size=size["shortest_edge"] ,default_to_square=_snake_case )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case = None ,**_snake_case ,):
UpperCAmelCase_ : Optional[Any] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
return center_crop(_snake_case ,size=(size["height"], size["width"]) ,data_format=_snake_case ,**_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case = None ,**_snake_case ):
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,**_snake_case ,):
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = None ,_snake_case = ChannelDimension.FIRST ,**_snake_case ,):
UpperCAmelCase_ : Tuple = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ : str = size if size is not None else self.size
UpperCAmelCase_ : List[Any] = get_size_dict(_snake_case ,default_to_square=_snake_case )
UpperCAmelCase_ : List[str] = resample if resample is not None else self.resample
UpperCAmelCase_ : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ : List[Any] = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ : Optional[int] = get_size_dict(_snake_case ,param_name="crop_size" )
UpperCAmelCase_ : Tuple = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ : List[Any] = image_std if image_std is not None else self.image_std
UpperCAmelCase_ : Optional[Any] = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
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." )
# All transformations expect numpy arrays.
UpperCAmelCase_ : Any = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
UpperCAmelCase_ : List[Any] = [self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case ) for image in images]
if do_center_crop:
UpperCAmelCase_ : List[Any] = [self.center_crop(image=_snake_case ,size=_snake_case ) for image in images]
if do_rescale:
UpperCAmelCase_ : str = [self.rescale(image=_snake_case ,scale=_snake_case ) for image in images]
if do_normalize:
UpperCAmelCase_ : Optional[Any] = [self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case ) for image in images]
UpperCAmelCase_ : Union[str, Any] = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
UpperCAmelCase_ : List[Any] = {"pixel_values": images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
def UpperCamelCase__ ( self ,_snake_case ,_snake_case = None ):
UpperCAmelCase_ : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_snake_case ) != len(_snake_case ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(_snake_case ):
UpperCAmelCase_ : int = target_sizes.numpy()
UpperCAmelCase_ : Dict = []
for idx in range(len(_snake_case ) ):
UpperCAmelCase_ : Union[str, Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) ,size=target_sizes[idx] ,mode="bilinear" ,align_corners=_snake_case )
UpperCAmelCase_ : int = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_snake_case )
else:
UpperCAmelCase_ : Optional[int] = logits.argmax(dim=1 )
UpperCAmelCase_ : Optional[int] = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 67 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = state_dict.pop(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = val
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ : Dict = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase_ : Optional[int] = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
UpperCAmelCase_ : Union[str, Any] = value
else:
UpperCAmelCase_ : int = value
return new_state_dict
def a__ ( _SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : Dict = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase_ : str = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Optional[int] = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : List[Any] = in_proj_weight[:2_56, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:2_56]
UpperCAmelCase_ : Dict = in_proj_weight[2_56:5_12, :]
UpperCAmelCase_ : Dict = in_proj_bias[2_56:5_12]
UpperCAmelCase_ : int = in_proj_weight[-2_56:, :]
UpperCAmelCase_ : Dict = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase_ : Optional[int] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : str = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[:2_56, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:2_56]
UpperCAmelCase_ : Optional[Any] = in_proj_weight[2_56:5_12, :]
UpperCAmelCase_ : List[str] = in_proj_bias[2_56:5_12]
UpperCAmelCase_ : Optional[int] = in_proj_weight[-2_56:, :]
UpperCAmelCase_ : List[Any] = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ : int = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase_ : Union[str, Any] = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase_ : List[str] = in_proj_weight_cross_attn[:2_56, :]
UpperCAmelCase_ : Dict = in_proj_bias_cross_attn[:2_56]
UpperCAmelCase_ : List[Any] = in_proj_weight_cross_attn[2_56:5_12, :]
UpperCAmelCase_ : int = in_proj_bias_cross_attn[2_56:5_12]
UpperCAmelCase_ : int = in_proj_weight_cross_attn[-2_56:, :]
UpperCAmelCase_ : str = in_proj_bias_cross_attn[-2_56:]
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = image.size
UpperCAmelCase_ : int = max(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = 8_00 if "detection" in checkpoint_url else 10_00
UpperCAmelCase_ : str = target_max_size / current_max_size
UpperCAmelCase_ : Tuple = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a__ ( _SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ : Any = F.to_tensor(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = F.normalize(_SCREAMING_SNAKE_CASE , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a__ ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : str ) -> Any:
"""simple docstring"""
logger.info("Converting model..." )
# load original state dict
UpperCAmelCase_ : Union[str, Any] = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = rename_backbone_keys(_SCREAMING_SNAKE_CASE )
# query, key and value matrices need special treatment
read_in_q_k_v(_SCREAMING_SNAKE_CASE )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ : Any = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
UpperCAmelCase_ : Optional[Any] = state_dict.pop(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[int] = val
# create HuggingFace model and load state dict
UpperCAmelCase_ : str = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCAmelCase_ : str = 15
UpperCAmelCase_ : str = 2
UpperCAmelCase_ : Union[str, Any] = {0: "table", 1: "table rotated"}
UpperCAmelCase_ : Tuple = idalabel
UpperCAmelCase_ : List[str] = {v: k for k, v in idalabel.items()}
else:
UpperCAmelCase_ : Tuple = 1_25
UpperCAmelCase_ : Tuple = 6
UpperCAmelCase_ : Union[str, Any] = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
UpperCAmelCase_ : str = idalabel
UpperCAmelCase_ : Any = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : List[Any] = DetrImageProcessor(
format="coco_detection" , max_size=8_00 if "detection" in checkpoint_url else 10_00 )
UpperCAmelCase_ : Optional[int] = TableTransformerForObjectDetection(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
# verify our conversion
UpperCAmelCase_ : Optional[Any] = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
UpperCAmelCase_ : Dict = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = Image.open(_SCREAMING_SNAKE_CASE ).convert("RGB" )
UpperCAmelCase_ : int = normalize(resize(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ).unsqueeze(0 )
UpperCAmelCase_ : Optional[int] = model(_SCREAMING_SNAKE_CASE )
if "detection" in checkpoint_url:
UpperCAmelCase_ : Any = (1, 15, 3)
UpperCAmelCase_ : Optional[int] = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
UpperCAmelCase_ : Dict = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
UpperCAmelCase_ : Union[str, Any] = (1, 1_25, 7)
UpperCAmelCase_ : List[str] = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
UpperCAmelCase_ : Any = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
UpperCAmelCase_ : List[str] = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(_SCREAMING_SNAKE_CASE )
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowerCamelCase = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 67 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase = '''docs/source/en/_toctree.yml'''
def lowercase_ ( lowerCAmelCase__ : Tuple ):
"""simple docstring"""
__UpperCAmelCase : Tuple = defaultdict(lowerCAmelCase__ )
__UpperCAmelCase : Union[str, Any] = []
__UpperCAmelCase : Any = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]} )
else:
new_doc_list.append(lowerCAmelCase__ )
__UpperCAmelCase : List[str] = new_doc_list
__UpperCAmelCase : int = [key for key, value in counts.items() if value > 1]
__UpperCAmelCase : Dict = []
for duplicate_key in duplicates:
__UpperCAmelCase : List[Any] = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(lowerCAmelCase__ ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1] )
__UpperCAmelCase : List[str] = sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(lowerCAmelCase__ ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(lowerCAmelCase__ )
# Sort
return overview_doc
def lowercase_ ( lowerCAmelCase__ : Optional[int]=False ):
"""simple docstring"""
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
__UpperCAmelCase : List[str] = yaml.safe_load(f.read() )
# Get to the API doc
__UpperCAmelCase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__UpperCAmelCase : Any = content[api_idx]["""sections"""]
# Then to the model doc
__UpperCAmelCase : int = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
__UpperCAmelCase : int = api_doc[scheduler_idx]["""sections"""]
__UpperCAmelCase : str = clean_doc_toc(lowerCAmelCase__ )
__UpperCAmelCase : Tuple = False
if new_scheduler_doc != scheduler_doc:
__UpperCAmelCase : Optional[int] = True
if overwrite:
__UpperCAmelCase : Dict = new_scheduler_doc
if diff:
if overwrite:
__UpperCAmelCase : Union[str, Any] = api_doc
with open(lowerCAmelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(lowerCAmelCase__ , allow_unicode=lowerCAmelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def lowercase_ ( lowerCAmelCase__ : Any=False ):
"""simple docstring"""
with open(lowerCAmelCase__ , encoding="""utf-8""" ) as f:
__UpperCAmelCase : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
__UpperCAmelCase : str = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__UpperCAmelCase : Optional[Any] = content[api_idx]["""sections"""]
# Then to the model doc
__UpperCAmelCase : int = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : List[str] = api_doc[pipeline_idx]["""sections"""]
__UpperCAmelCase : int = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
__UpperCAmelCase : Optional[Any] = pipeline_doc["""section"""]
__UpperCAmelCase : Optional[Any] = clean_doc_toc(lowerCAmelCase__ )
if overwrite:
__UpperCAmelCase : Optional[Any] = new_sub_pipeline_doc
new_pipeline_docs.append(lowerCAmelCase__ )
# sort overall pipeline doc
__UpperCAmelCase : List[Any] = clean_doc_toc(lowerCAmelCase__ )
if new_pipeline_docs != pipeline_docs:
__UpperCAmelCase : int = True
if overwrite:
__UpperCAmelCase : str = new_pipeline_docs
if diff:
if overwrite:
__UpperCAmelCase : Any = api_doc
with open(lowerCAmelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(lowerCAmelCase__ , allow_unicode=lowerCAmelCase__ ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_UpperCamelCase = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 254 |
'''simple docstring'''
import qiskit
def lowercase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__UpperCAmelCase : Any = qiskit.QuantumCircuit(lowerCAmelCase__ , lowerCAmelCase__ )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
__UpperCAmelCase : int = qiskit.execute(lowerCAmelCase__ , lowerCAmelCase__ , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(lowerCAmelCase__ )
if __name__ == "__main__":
print(F'Total count for various states are: {single_qubit_measure(1, 1)}')
| 254 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ , snake_case__ = False ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
A : List[Any] = F'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
if not isinstance(snake_case__ , snake_case__ ):
A : List[Any] = F'Expected boolean as use_pascal parameter, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
A : List[Any] = input_str.split('''_''' )
A : Optional[Any] = 0 if use_pascal else 1
A : List[str] = words[start_index:]
A : int = [word[0].upper() + word[1:] for word in words_to_capitalize]
A : Any = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 311 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : Dict = SwinConfig()
A : List[Any] = swin_name.split('''_''' )
A : Tuple = name_split[1]
A : Union[str, Any] = int(name_split[4] )
A : str = int(name_split[3][-1] )
if model_size == "tiny":
A : Optional[int] = 96
A : Optional[Any] = (2, 2, 6, 2)
A : Any = (3, 6, 12, 24)
elif model_size == "small":
A : Optional[int] = 96
A : str = (2, 2, 18, 2)
A : Tuple = (3, 6, 12, 24)
elif model_size == "base":
A : int = 128
A : Optional[Any] = (2, 2, 18, 2)
A : List[str] = (4, 8, 16, 32)
else:
A : Dict = 192
A : Optional[Any] = (2, 2, 18, 2)
A : Optional[Any] = (6, 12, 24, 48)
if "in22k" in swin_name:
A : Dict = 2_1841
else:
A : str = 1000
A : List[str] = '''huggingface/label-files'''
A : Any = '''imagenet-1k-id2label.json'''
A : Any = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='''dataset''' ) , '''r''' ) )
A : str = {int(snake_case__ ): v for k, v in idalabel.items()}
A : Tuple = idalabel
A : Tuple = {v: k for k, v in idalabel.items()}
A : Tuple = img_size
A : Dict = num_classes
A : Optional[Any] = embed_dim
A : str = depths
A : str = num_heads
A : Optional[int] = window_size
return config
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if "patch_embed.proj" in name:
A : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
A : Tuple = name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
A : Optional[int] = '''encoder.''' + name
if "attn.proj" in name:
A : List[str] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
A : List[str] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
A : Any = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
A : Tuple = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
A : Dict = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
A : str = name.replace('''mlp.fc2''' , '''output.dense''' )
if name == "norm.weight":
A : Tuple = '''layernorm.weight'''
if name == "norm.bias":
A : Tuple = '''layernorm.bias'''
if "head" in name:
A : Any = name.replace('''head''' , '''classifier''' )
else:
A : List[Any] = '''swin.''' + name
return name
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
A : Dict = orig_state_dict.pop(snake_case__ )
if "mask" in key:
continue
elif "qkv" in key:
A : Dict = key.split('''.''' )
A : Optional[int] = int(key_split[1] )
A : List[str] = int(key_split[3] )
A : Optional[int] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
A : Any = val[:dim, :]
A : Dict = val[
dim : dim * 2, :
]
A : List[str] = val[-dim:, :]
else:
A : Any = val[
:dim
]
A : Optional[int] = val[
dim : dim * 2
]
A : Any = val[
-dim:
]
else:
A : str = val
return orig_state_dict
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Tuple = timm.create_model(snake_case__ , pretrained=snake_case__ )
timm_model.eval()
A : Optional[Any] = get_swin_config(snake_case__ )
A : Optional[int] = SwinForImageClassification(snake_case__ )
model.eval()
A : List[str] = convert_state_dict(timm_model.state_dict() , snake_case__ )
model.load_state_dict(snake_case__ )
A : Optional[int] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
A : Any = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''' , '''-''' ) ) )
A : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
A : List[Any] = image_processor(images=snake_case__ , return_tensors='''pt''' )
A : Any = timm_model(inputs['''pixel_values'''] )
A : Optional[Any] = model(**snake_case__ ).logits
assert torch.allclose(snake_case__ , snake_case__ , atol=1E-3 )
print(F'Saving model {swin_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case__ )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(snake_case__ )
if __name__ == "__main__":
lowercase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swin_name',
default='swin_tiny_patch4_window7_224',
type=str,
help='Name of the Swin timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
lowercase : int = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 311 | 1 |
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueError('''max_weight must greater than zero.''' )
if any(p < 0 for p in profit ):
raise ValueError('''Profit can not be negative.''' )
if any(w < 0 for w in weight ):
raise ValueError('''Weight can not be negative.''' )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
lowercase__ = [p / w for p, w in zip(lowerCamelCase_ , lowerCamelCase_ )]
# Creating a copy of the list and sorting profit/weight in ascending order
lowercase__ = sorted(lowerCamelCase_ )
# declaring useful variables
lowercase__ = len(lowerCamelCase_ )
lowercase__ = 0
lowercase__ = 0
lowercase__ = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
lowercase__ = sorted_profit_by_weight[length - i - 1]
lowercase__ = profit_by_weight.index(lowerCamelCase_ )
lowercase__ = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
'Input profits, weights, and then max_weight (all positive ints) separated by '
'spaces.'
)
A__ : Optional[int] = [int(x) for x in input('Input profits separated by spaces: ').split()]
A__ : Dict = [int(x) for x in input('Input weights separated by spaces: ').split()]
A__ : Optional[Any] = int(input('Max weight allowed: '))
# Function Call
calc_profit(profit, weight, max_weight)
| 207 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A__ : List[Any] = logging.get_logger(__name__)
A__ : str = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : int = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
A__ : Optional[Any] = {
'distilbert-base-uncased': 5_12,
'distilbert-base-uncased-distilled-squad': 5_12,
'distilbert-base-cased': 5_12,
'distilbert-base-cased-distilled-squad': 5_12,
'distilbert-base-german-cased': 5_12,
'distilbert-base-multilingual-cased': 5_12,
}
A__ : List[str] = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = DistilBertTokenizer
def __init__( self : List[Any], lowerCamelCase : List[Any]=None, lowerCamelCase : Dict=None, lowerCamelCase : str=True, lowerCamelCase : Optional[int]="[UNK]", lowerCamelCase : Optional[Any]="[SEP]", lowerCamelCase : List[Any]="[PAD]", lowerCamelCase : Any="[CLS]", lowerCamelCase : Union[str, Any]="[MASK]", lowerCamelCase : str=True, lowerCamelCase : int=None, **lowerCamelCase : Union[str, Any], ):
'''simple docstring'''
super().__init__(
lowerCamelCase, tokenizer_file=lowerCamelCase, do_lower_case=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, pad_token=lowerCamelCase, cls_token=lowerCamelCase, mask_token=lowerCamelCase, tokenize_chinese_chars=lowerCamelCase, strip_accents=lowerCamelCase, **lowerCamelCase, )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''', lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''', lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''', lowerCamelCase ) != tokenize_chinese_chars
):
lowercase__ = getattr(lowerCamelCase, normalizer_state.pop('''type''' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**lowerCamelCase )
lowercase__ = do_lower_case
def lowercase__ ( self : str, lowerCamelCase : Optional[Any], lowerCamelCase : List[Any]=None ):
'''simple docstring'''
lowercase__ = [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 lowercase__ ( self : Union[str, Any], lowerCamelCase : List[int], lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [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 lowercase__ ( self : str, lowerCamelCase : str, lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowercase__ = self._tokenizer.model.save(lowerCamelCase, name=lowerCamelCase )
return tuple(lowerCamelCase )
| 207 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : int = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}
class lowerCamelCase__ ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__a = """focalnet"""
def __init__( self : Tuple , UpperCamelCase : str=224 , UpperCamelCase : Dict=4 , UpperCamelCase : str=3 , UpperCamelCase : Union[str, Any]=96 , UpperCamelCase : List[str]=False , UpperCamelCase : int=[192, 384, 768, 768] , UpperCamelCase : List[str]=[2, 2, 6, 2] , UpperCamelCase : int=[2, 2, 2, 2] , UpperCamelCase : Tuple=[3, 3, 3, 3] , UpperCamelCase : List[str]="gelu" , UpperCamelCase : List[Any]=4.0 , UpperCamelCase : Optional[Any]=0.0 , UpperCamelCase : Optional[int]=0.1 , UpperCamelCase : List[Any]=False , UpperCamelCase : Any=1e-4 , UpperCamelCase : Optional[Any]=False , UpperCamelCase : int=False , UpperCamelCase : Dict=False , UpperCamelCase : str=0.02 , UpperCamelCase : int=1e-5 , UpperCamelCase : Optional[Any]=32 , UpperCamelCase : Dict=None , UpperCamelCase : Dict=None , **UpperCamelCase : List[str] , ):
'''simple docstring'''
super().__init__(**__snake_case )
__UpperCAmelCase : Optional[int] = image_size
__UpperCAmelCase : Union[str, Any] = patch_size
__UpperCAmelCase : int = num_channels
__UpperCAmelCase : List[str] = embed_dim
__UpperCAmelCase : Union[str, Any] = use_conv_embed
__UpperCAmelCase : Any = hidden_sizes
__UpperCAmelCase : List[Any] = depths
__UpperCAmelCase : Any = focal_levels
__UpperCAmelCase : Any = focal_windows
__UpperCAmelCase : Optional[int] = hidden_act
__UpperCAmelCase : List[Any] = mlp_ratio
__UpperCAmelCase : Optional[Any] = hidden_dropout_prob
__UpperCAmelCase : Dict = drop_path_rate
__UpperCAmelCase : Optional[int] = use_layerscale
__UpperCAmelCase : Tuple = layerscale_value
__UpperCAmelCase : Optional[Any] = use_post_layernorm
__UpperCAmelCase : List[str] = use_post_layernorm_in_modulation
__UpperCAmelCase : Optional[int] = normalize_modulator
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Optional[Any] = layer_norm_eps
__UpperCAmelCase : Optional[Any] = encoder_stride
__UpperCAmelCase : str = ['stem'] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
__UpperCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__snake_case , out_indices=__snake_case , stage_names=self.stage_names )
| 358 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase__ :
"""simple docstring"""
@staticmethod
def lowerCamelCase__ ( *UpperCamelCase : Optional[Any] , **UpperCamelCase : Dict ):
'''simple docstring'''
pass
def lowerCamelCase ( _UpperCamelCase : Image ) -> str:
'''simple docstring'''
__UpperCAmelCase : Tuple = hashlib.mda(image.tobytes() )
return m.hexdigest()[:1_0]
def lowerCamelCase ( _UpperCamelCase : Image ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Tuple = np.array(_UpperCamelCase )
__UpperCAmelCase : List[Any] = npimg.shape
return {"hash": hashimage(_UpperCamelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
__a = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__a = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = MaskGenerationPipeline(model=UpperCamelCase , image_processor=UpperCamelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase__ ( self : Optional[int] , UpperCamelCase : Dict , UpperCamelCase : List[Any] ):
'''simple docstring'''
pass
@require_tf
@unittest.skip("""Image segmentation not implemented in TF""" )
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
pass
@slow
@require_torch
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Tuple = pipeline("""mask-generation""" , model="""facebook/sam-vit-huge""" )
__UpperCAmelCase : Any = image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg""" , points_per_batch=256 )
# Shortening by hashing
__UpperCAmelCase : int = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
{"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
{"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
{"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
{"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
{"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
{"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
{"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
{"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
{"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
{"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
{"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
{"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
{"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
{"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
{"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
{"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
{"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
{"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
{"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
{"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
{"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
{"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
{"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
{"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
{"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
] , )
# fmt: on
@require_torch
@slow
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = """facebook/sam-vit-huge"""
__UpperCAmelCase : str = pipeline("""mask-generation""" , model=UpperCamelCase )
__UpperCAmelCase : int = image_segmenter(
"""http://images.cocodataset.org/val2017/000000039769.jpg""" , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__UpperCAmelCase : Dict = []
for i, o in enumerate(outputs["""masks"""] ):
new_outupt += [{"mask": mask_to_test_readable(UpperCamelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
{"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
{"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
{"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
{"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
] , )
| 320 | 0 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
UpperCAmelCase__ = """src/diffusers"""
UpperCAmelCase__ = """."""
# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase__ = importlib.util.spec_from_file_location(
"""diffusers""",
os.path.join(DIFFUSERS_PATH, """__init__.py"""),
submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase__ = spec.loader.load_module()
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
return line.startswith(lowercase ) or len(lowercase ) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""" ,lowercase ) is not None
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = object_name.split(""".""" )
_UpperCAmelCase = 0
# First let's find the module where our object lives.
_UpperCAmelCase = parts[i]
while i < len(lowercase ) and not os.path.isfile(os.path.join(lowercase ,f'''{module}.py''' ) ):
i += 1
if i < len(lowercase ):
_UpperCAmelCase = os.path.join(lowercase ,parts[i] )
if i >= len(lowercase ):
raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(lowercase ,f'''{module}.py''' ) ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
_UpperCAmelCase = f.readlines()
# Now let's find the class / func in the code!
_UpperCAmelCase = """"""
_UpperCAmelCase = 0
for name in parts[i + 1 :]:
while (
line_index < len(lowercase ) and re.search(Rf'''^{indent}(class|def)\s+{name}(\(|\:)''' ,lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(lowercase ):
raise ValueError(f''' {object_name} does not match any function or class in {module}.''' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
_UpperCAmelCase = line_index
while line_index < len(lowercase ) and _should_continue(lines[line_index] ,lowercase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_UpperCAmelCase = lines[start_index:line_index]
return "".join(lowercase )
UpperCAmelCase__ = re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
UpperCAmelCase__ = re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
UpperCAmelCase__ = re.compile(r"""<FILL\s+[^>]*>""")
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = code.split("""\n""" )
_UpperCAmelCase = 0
while idx < len(lowercase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(lowercase ):
return re.search(R"""^(\s*)\S""" ,lines[idx] ).groups()[0]
return ""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = len(get_indent(lowercase ) ) > 0
if has_indent:
_UpperCAmelCase = f'''class Bla:\n{code}'''
_UpperCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} ,line_length=1_19 ,preview=lowercase )
_UpperCAmelCase = black.format_str(lowercase ,mode=lowercase )
_UpperCAmelCase , _UpperCAmelCase = style_docstrings_in_code(lowercase )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def __UpperCAmelCase ( lowercase ,lowercase=False ):
"""simple docstring"""
with open(lowercase ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = []
_UpperCAmelCase = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(lowercase ):
_UpperCAmelCase = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = search.groups()
_UpperCAmelCase = find_code_in_diffusers(lowercase )
_UpperCAmelCase = get_indent(lowercase )
_UpperCAmelCase = line_index + 1 if indent == theoretical_indent else line_index + 2
_UpperCAmelCase = theoretical_indent
_UpperCAmelCase = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
_UpperCAmelCase = True
while line_index < len(lowercase ) and should_continue:
line_index += 1
if line_index >= len(lowercase ):
break
_UpperCAmelCase = lines[line_index]
_UpperCAmelCase = _should_continue(lowercase ,lowercase ) and re.search(f'''^{indent}# End copy''' ,lowercase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_UpperCAmelCase = lines[start_index:line_index]
_UpperCAmelCase = """""".join(lowercase )
# Remove any nested `Copied from` comments to avoid circular copies
_UpperCAmelCase = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(lowercase ) is None]
_UpperCAmelCase = """\n""".join(lowercase )
# Before comparing, use the `replace_pattern` on the original code.
if len(lowercase ) > 0:
_UpperCAmelCase = replace_pattern.replace("""with""" ,"""""" ).split(""",""" )
_UpperCAmelCase = [_re_replace_pattern.search(lowercase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = pattern.groups()
_UpperCAmelCase = re.sub(lowercase ,lowercase ,lowercase )
if option.strip() == "all-casing":
_UpperCAmelCase = re.sub(obja.lower() ,obja.lower() ,lowercase )
_UpperCAmelCase = re.sub(obja.upper() ,obja.upper() ,lowercase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
_UpperCAmelCase = blackify(lines[start_index - 1] + theoretical_code )
_UpperCAmelCase = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
_UpperCAmelCase = lines[:start_index] + [theoretical_code] + lines[line_index:]
_UpperCAmelCase = start_index + 1
if overwrite and len(lowercase ) > 0:
# Warn the user a file has been modified.
print(f'''Detected changes, rewriting {filename}.''' )
with open(lowercase ,"""w""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
f.writelines(lowercase )
return diffs
def __UpperCAmelCase ( lowercase = False ):
"""simple docstring"""
_UpperCAmelCase = glob.glob(os.path.join(lowercase ,"""**/*.py""" ) ,recursive=lowercase )
_UpperCAmelCase = []
for filename in all_files:
_UpperCAmelCase = is_copy_consistent(lowercase ,lowercase )
diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(lowercase ) > 0:
_UpperCAmelCase = """\n""".join(lowercase )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
UpperCAmelCase__ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 289 | """simple docstring"""
from math import pow
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,):
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
_UpperCAmelCase = int(pow(lowercase ,lowercase ) )
if current_sum + i_to_n <= needed_sum:
# If the sum of the powers is less than needed_sum, then continue adding powers.
current_sum += i_to_n
_UpperCAmelCase , _UpperCAmelCase = backtrack(
lowercase ,lowercase ,current_number + 1 ,lowercase ,lowercase )
current_sum -= i_to_n
if i_to_n < needed_sum:
# If the power of i is less than needed_sum, then try with the next power.
_UpperCAmelCase , _UpperCAmelCase = backtrack(
lowercase ,lowercase ,current_number + 1 ,lowercase ,lowercase )
return current_sum, solutions_count
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
raise ValueError(
"""Invalid input\n"""
"""needed_sum must be between 1 and 1000, power between 2 and 10.""" )
return backtrack(lowercase ,lowercase ,1 ,0 ,0 )[1] # Return the solutions_count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 289 | 1 |
"""simple docstring"""
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 : Union[str, Any] = {
'''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''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''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''': 10,
'''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''': 10,
'''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 lowerCamelCase ( unittest.TestCase ):
@classmethod
def a_ ( cls ):
UpperCamelCase : List[Any] = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE_ )
@classmethod
def a_ ( cls ):
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 a_ ( self ):
UpperCamelCase : Any = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("""test-config""" , use_auth_token=self._token )
UpperCamelCase : str = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# 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(SCREAMING_SNAKE_CASE_ , repo_id="""test-config""" , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase : Optional[int] = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("""valid_org/test-config-org""" , use_auth_token=self._token )
UpperCamelCase : Optional[int] = BertConfig.from_pretrained("""valid_org/test-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# 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(
SCREAMING_SNAKE_CASE_ , repo_id="""valid_org/test-config-org""" , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase : Union[str, Any] = BertConfig.from_pretrained("""valid_org/test-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
CustomConfig.register_for_auto_class()
UpperCamelCase : Any = CustomConfig(attribute=42 )
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"""} )
UpperCamelCase : Dict = AutoConfig.from_pretrained(f'{USER}/test-dynamic-config' , trust_remote_code=SCREAMING_SNAKE_CASE_ )
# 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 , 42 )
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
UpperCamelCase : Dict = c.n_embd + 1 # int
UpperCamelCase : Dict = c.resid_pdrop + 1.0 # float
UpperCamelCase : Union[str, Any] = not c.scale_attn_weights # bool
UpperCamelCase : Tuple = 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(SCREAMING_SNAKE_CASE_ , c.n_embd , """mismatch for key: n_embd""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.resid_pdrop , """mismatch for key: resid_pdrop""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.scale_attn_weights , """mismatch for key: scale_attn_weights""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.summary_type , """mismatch for key: summary_type""" )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = PretrainedConfig()
UpperCamelCase : 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(
SCREAMING_SNAKE_CASE_ , ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""] )
UpperCamelCase : Tuple = [key for key, value in config_common_kwargs.items() if value == getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
if len(SCREAMING_SNAKE_CASE_ ) > 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(SCREAMING_SNAKE_CASE_ )}.' )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
# config is in subfolder, the following should not work without specifying the subfolder
UpperCamelCase : List[Any] = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""" )
UpperCamelCase : Dict = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""" , subfolder="""bert""" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
# A mock response for an HTTP head request to emulate server down
UpperCamelCase : str = mock.Mock()
UpperCamelCase : Union[str, Any] = 500
UpperCamelCase : Optional[int] = {}
UpperCamelCase : List[Any] = HTTPError
UpperCamelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
UpperCamelCase : Optional[Any] = 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=SCREAMING_SNAKE_CASE_ ) as mock_head:
UpperCamelCase : Tuple = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# This check we did call the fake head request
mock_head.assert_called()
def a_ ( self ):
# This test is for deprecated behavior and can be removed in v5
UpperCamelCase : int = BertConfig.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json""" )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[Any] = ["""config.4.0.0.json"""]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 2
json.dump(configuration.to_dict() , open(os.path.join(SCREAMING_SNAKE_CASE_ , """config.4.0.0.json""" ) , """w""" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
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
UpperCamelCase : List[Any] = ["""config.42.0.0.json"""]
UpperCamelCase : Optional[int] = 768
configuration.save_pretrained(SCREAMING_SNAKE_CASE_ )
shutil.move(os.path.join(SCREAMING_SNAKE_CASE_ , """config.4.0.0.json""" ) , os.path.join(SCREAMING_SNAKE_CASE_ , """config.42.0.0.json""" ) )
UpperCamelCase : List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertEqual(new_configuration.hidden_size , 768 )
def a_ ( self ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
UpperCamelCase : List[Any] = """hf-internal-testing/test-two-configs"""
import transformers as new_transformers
UpperCamelCase : Optional[Any] = """v4.0.0"""
UpperCamelCase : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
SCREAMING_SNAKE_CASE_ , return_unused_kwargs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(SCREAMING_SNAKE_CASE_ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
UpperCamelCase : Dict = """v3.0.0"""
UpperCamelCase : int = old_transformers.models.auto.AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertEqual(old_configuration.hidden_size , 768 )
| 362 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowercase__ =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')
lowercase__ =parser.parse_args()
lowercase__ ='cpu'
lowercase__ ='a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'
lowercase__ ='path-to-your-trained-model'
lowercase__ =StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
lowercase__ =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase__ =pipe.to(device)
# to channels last
lowercase__ =pipe.unet.to(memory_format=torch.channels_last)
lowercase__ =pipe.vae.to(memory_format=torch.channels_last)
lowercase__ =pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
lowercase__ =pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
lowercase__ =torch.randn(2, 4, 64, 64)
lowercase__ =torch.rand(1) * 999
lowercase__ =torch.randn(2, 77, 768)
lowercase__ =(sample, timestep, encoder_hidden_status)
try:
lowercase__ =ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
lowercase__ =ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__ =ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__ =ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
lowercase__ =ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
lowercase__ =666
lowercase__ =torch.Generator(device).manual_seed(seed)
lowercase__ ={'generator': generator}
if args.steps is not None:
lowercase__ =args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
lowercase__ =pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('generated.png')
| 216 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 5_0_0_0_0_0_0_0 ):
__a : int = set()
__a : str = int((limit - 2_4) ** (1 / 2) )
__a : int = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowerCAmelCase__ ) ) )
for primea in primes:
__a : Union[str, Any] = primea * primea
for primea in primes:
__a : Union[str, Any] = primea * primea * primea
if square + cube >= limit - 1_6:
break
for primea in primes:
__a : int = primea * primea * primea * primea
__a : Union[str, Any] = square + cube + tetr
if total >= limit:
break
ret.add(lowerCAmelCase__ )
return len(lowerCAmelCase__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 216 | 1 |
'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase__ = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCAmelCase__ ( datasets.BuilderConfig ):
lowerCAmelCase : Optional[datasets.Features] = None
lowerCAmelCase : str = "utf-8"
lowerCAmelCase : Optional[str] = None
lowerCAmelCase : Optional[str] = None
lowerCAmelCase : bool = True # deprecated
lowerCAmelCase : Optional[int] = None # deprecated
lowerCAmelCase : int = 10 << 20 # 10MB
lowerCAmelCase : Optional[bool] = None
class lowerCAmelCase__ ( datasets.ArrowBasedBuilder ):
lowerCAmelCase : List[str] = JsonConfig
def lowerCAmelCase__ ( self : Any ) ->Union[str, Any]:
'''simple docstring'''
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
_UpperCAmelCase : Optional[Any] = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
if self.config.newlines_in_values is not None:
raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
return datasets.DatasetInfo(features=self.config.features )
def lowerCAmelCase__ ( self : str , lowerCamelCase__ : Any ) ->List[Any]:
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_UpperCAmelCase : Optional[int] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCamelCase__ , (str, list, tuple) ):
_UpperCAmelCase : List[str] = data_files
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
_UpperCAmelCase : str = [files]
_UpperCAmelCase : List[str] = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_UpperCAmelCase : int = []
for split_name, files in data_files.items():
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
_UpperCAmelCase : List[str] = [files]
_UpperCAmelCase : Tuple = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCamelCase__ , gen_kwargs={"files": files} ) )
return splits
def lowerCAmelCase__ ( self : Optional[Any] , lowerCamelCase__ : pa.Table ) ->pa.Table:
'''simple docstring'''
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
_UpperCAmelCase : List[str] = self.config.features.arrow_schema.field(lowerCamelCase__ ).type
_UpperCAmelCase : Optional[int] = pa_table.append_column(lowerCamelCase__ , pa.array([None] * len(lowerCamelCase__ ) , type=lowerCamelCase__ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
_UpperCAmelCase : List[str] = table_cast(lowerCamelCase__ , self.config.features.arrow_schema )
return pa_table
def lowerCAmelCase__ ( self : Optional[Any] , lowerCamelCase__ : Union[str, Any] ) ->str:
'''simple docstring'''
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(lowerCamelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
_UpperCAmelCase : int = json.load(lowerCamelCase__ )
# We keep only the field we are interested in
_UpperCAmelCase : Optional[Any] = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(lowerCamelCase__ , (list, tuple) ):
_UpperCAmelCase : Union[str, Any] = set().union(*[row.keys() for row in dataset] )
_UpperCAmelCase : int = {col: [row.get(lowerCamelCase__ ) for row in dataset] for col in keys}
else:
_UpperCAmelCase : List[Any] = dataset
_UpperCAmelCase : str = pa.Table.from_pydict(lowerCamelCase__ )
yield file_idx, self._cast_table(lowerCamelCase__ )
# If the file has one json object per line
else:
with open(lowerCamelCase__ , "rb" ) as f:
_UpperCAmelCase : Union[str, Any] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
_UpperCAmelCase : int = max(self.config.chunksize // 32 , 16 << 10 )
_UpperCAmelCase : Dict = (
self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
)
while True:
_UpperCAmelCase : Tuple = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(lowerCamelCase__ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
_UpperCAmelCase : Optional[Any] = batch.decode(self.config.encoding , errors=lowerCamelCase__ ).encode("utf-8" )
try:
while True:
try:
_UpperCAmelCase : Union[str, Any] = paj.read_json(
io.BytesIO(lowerCamelCase__ ) , read_options=paj.ReadOptions(block_size=lowerCamelCase__ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(lowerCamelCase__ , pa.ArrowInvalid )
and "straddling" not in str(lowerCamelCase__ )
or block_size > len(lowerCamelCase__ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F"""Batch of {len(lowerCamelCase__ )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""" )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
lowerCamelCase__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
_UpperCAmelCase : List[str] = json.load(lowerCamelCase__ )
except json.JSONDecodeError:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__ )}: {e}""" )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(lowerCamelCase__ , lowerCamelCase__ ): # list is the only sequence type supported in JSON
try:
_UpperCAmelCase : int = set().union(*[row.keys() for row in dataset] )
_UpperCAmelCase : Tuple = {col: [row.get(lowerCamelCase__ ) for row in dataset] for col in keys}
_UpperCAmelCase : Optional[int] = pa.Table.from_pydict(lowerCamelCase__ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__ )}: {e}""" )
raise ValueError(F"""Not able to read records in the JSON file at {file}.""" ) from None
yield file_idx, self._cast_table(lowerCamelCase__ )
break
else:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__ )}: {e}""" )
raise ValueError(
F"""Not able to read records in the JSON file at {file}. """
F"""You should probably indicate the field of the JSON file containing your records. """
F"""This JSON file contain the following fields: {str(list(dataset.keys() ) )}. """
F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """ ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCamelCase__ )
batch_idx += 1
| 322 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : List[Any] = len(__lowerCAmelCase )
_UpperCAmelCase : Tuple = sum(__lowerCAmelCase )
_UpperCAmelCase : List[Any] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
_UpperCAmelCase : Any = True
for i in range(1 , s + 1 ):
_UpperCAmelCase : List[Any] = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
_UpperCAmelCase : Optional[int] = dp[i][j - 1]
if arr[i - 1] <= j:
_UpperCAmelCase : 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:
_UpperCAmelCase : List[Any] = s - 2 * j
break
return diff
| 322 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
UpperCAmelCase : Any = False
class __lowerCAmelCase ( unittest.TestCase):
def _lowercase ( self ) -> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
return 1_2
@property
def _lowercase ( self ) -> str:
'''simple docstring'''
return 1_2
@property
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
return 3_2
@property
def _lowercase ( self ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
a__ : Dict =VQModel(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def _lowercase ( self ) -> Dict:
'''simple docstring'''
torch.manual_seed(0 )
a__ : Union[str, Any] =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
return CLIPTextModel(lowerCAmelCase__ )
@property
def _lowercase ( self ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
a__ : str =1_2
a__ : List[str] =1_2
a__ : List[Any] ={
"attention_bias": True,
"cross_attention_dim": 3_2,
"attention_head_dim": height * width,
"num_attention_heads": 1,
"num_vector_embeds": self.num_embed,
"num_embeds_ada_norm": self.num_embeds_ada_norm,
"norm_num_groups": 3_2,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
a__ : List[str] =TransformeraDModel(**lowerCAmelCase__ )
return model
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
a__ : Optional[int] ="cpu"
a__ : int =self.dummy_vqvae
a__ : Union[str, Any] =self.dummy_text_encoder
a__ : List[Any] =self.dummy_tokenizer
a__ : Optional[int] =self.dummy_transformer
a__ : Tuple =VQDiffusionScheduler(self.num_embed )
a__ : int =LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__ )
a__ : Any =VQDiffusionPipeline(
vqvae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , transformer=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , learned_classifier_free_sampling_embeddings=lowerCAmelCase__ , )
a__ : str =pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a__ : int ="teddy bear playing in the pool"
a__ : int =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : Union[str, Any] =pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type="np" )
a__ : Union[str, Any] =output.images
a__ : List[str] =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : Optional[Any] =pipe(
[prompt] , generator=lowerCAmelCase__ , output_type="np" , return_dict=lowerCAmelCase__ , num_inference_steps=2 )[0]
a__ : Dict =image[0, -3:, -3:, -1]
a__ : Tuple =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
a__ : Dict =np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Any ="cpu"
a__ : str =self.dummy_vqvae
a__ : str =self.dummy_text_encoder
a__ : Any =self.dummy_tokenizer
a__ : Union[str, Any] =self.dummy_transformer
a__ : str =VQDiffusionScheduler(self.num_embed )
a__ : Tuple =LearnedClassifierFreeSamplingEmbeddings(
learnable=lowerCAmelCase__ , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
a__ : List[str] =VQDiffusionPipeline(
vqvae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , transformer=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , learned_classifier_free_sampling_embeddings=lowerCAmelCase__ , )
a__ : str =pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a__ : Optional[Any] ="teddy bear playing in the pool"
a__ : Tuple =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : int =pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type="np" )
a__ : Any =output.images
a__ : str =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : List[str] =pipe(
[prompt] , generator=lowerCAmelCase__ , output_type="np" , return_dict=lowerCAmelCase__ , num_inference_steps=2 )[0]
a__ : List[str] =image[0, -3:, -3:, -1]
a__ : Optional[Any] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 2_4, 2_4, 3)
a__ : Any =np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase):
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Dict:
'''simple docstring'''
a__ : Union[str, Any] =load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy" )
a__ : Tuple =VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq" )
a__ : Optional[int] =pipeline.to(lowerCAmelCase__ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase__ )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
a__ : Tuple =torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
a__ : List[str] =pipeline(
"teddy bear playing in the pool" , num_images_per_prompt=1 , generator=lowerCAmelCase__ , output_type="np" , )
a__ : int =output.images[0]
assert image.shape == (2_5_6, 2_5_6, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"""caidas/swin2sr-classicalsr-x2-64""": (
"""https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = """swin2sr"""
_lowercase : Tuple = {
"""hidden_size""": """embed_dim""",
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , lowerCAmelCase__=6_4 , lowerCAmelCase__=1 , lowerCAmelCase__=3 , lowerCAmelCase__=1_8_0 , lowerCAmelCase__=[6, 6, 6, 6, 6, 6] , lowerCAmelCase__=[6, 6, 6, 6, 6, 6] , lowerCAmelCase__=8 , lowerCAmelCase__=2.0 , lowerCAmelCase__=True , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__="gelu" , lowerCAmelCase__=False , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-5 , lowerCAmelCase__=2 , lowerCAmelCase__=1.0 , lowerCAmelCase__="1conv" , lowerCAmelCase__="pixelshuffle" , **lowerCAmelCase__ , ) -> int:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
a__ : Optional[Any] =image_size
a__ : Dict =patch_size
a__ : Tuple =num_channels
a__ : Union[str, Any] =embed_dim
a__ : Optional[Any] =depths
a__ : List[str] =len(lowerCAmelCase__ )
a__ : Any =num_heads
a__ : Any =window_size
a__ : str =mlp_ratio
a__ : List[str] =qkv_bias
a__ : Dict =hidden_dropout_prob
a__ : List[str] =attention_probs_dropout_prob
a__ : Dict =drop_path_rate
a__ : Optional[Any] =hidden_act
a__ : Union[str, Any] =use_absolute_embeddings
a__ : Optional[Any] =layer_norm_eps
a__ : List[Any] =initializer_range
a__ : int =upscale
a__ : Optional[int] =img_range
a__ : Any =resi_connection
a__ : Optional[Any] =upsampler
| 95 | 1 |
'''simple docstring'''
import math
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : int = []
lowercase__ : Optional[int] = 2
lowercase__ : Dict = int(math.sqrt(UpperCAmelCase ) ) # Size of every segment
lowercase__ : Optional[Any] = [True] * (end + 1)
lowercase__ : List[Any] = []
while start <= end:
if temp[start] is True:
in_prime.append(UpperCAmelCase )
for i in range(start * start , end + 1 , UpperCAmelCase ):
lowercase__ : Dict = False
start += 1
prime += in_prime
lowercase__ : List[Any] = end + 1
lowercase__ : str = min(2 * end , UpperCAmelCase )
while low <= n:
lowercase__ : Optional[int] = [True] * (high - low + 1)
for each in in_prime:
lowercase__ : Tuple = math.floor(low / each ) * each
if t < low:
t += each
for j in range(UpperCAmelCase , high + 1 , UpperCAmelCase ):
lowercase__ : List[Any] = False
for j in range(len(UpperCAmelCase ) ):
if temp[j] is True:
prime.append(j + low )
lowercase__ : str = high + 1
lowercase__ : Optional[int] = min(high + end , UpperCAmelCase )
return prime
print(sieve(10**6))
| 214 | '''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__a: Tuple = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = "utf-8"
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = True # deprecated
SCREAMING_SNAKE_CASE = None # deprecated
SCREAMING_SNAKE_CASE = 1_0 << 2_0 # 10MB
SCREAMING_SNAKE_CASE = None
class UpperCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = JsonConfig
def _lowerCAmelCase( self ) -> Any:
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
lowercase__ : Tuple = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def _lowerCAmelCase( self , __lowerCAmelCase ) -> Optional[Any]:
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowercase__ : List[str] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__lowerCAmelCase , (str, list, tuple) ):
lowercase__ : List[str] = data_files
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase__ : Union[str, Any] = [files]
lowercase__ : Dict = [dl_manager.iter_files(__lowerCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
lowercase__ : str = []
for split_name, files in data_files.items():
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowercase__ : List[str] = [files]
lowercase__ : Optional[Any] = [dl_manager.iter_files(__lowerCAmelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=__lowerCAmelCase , gen_kwargs={'''files''': files} ) )
return splits
def _lowerCAmelCase( self , __lowerCAmelCase ) -> pa.Table:
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
lowercase__ : Optional[int] = self.config.features.arrow_schema.field(__lowerCAmelCase ).type
lowercase__ : Union[str, Any] = pa_table.append_column(__lowerCAmelCase , pa.array([None] * len(__lowerCAmelCase ) , type=__lowerCAmelCase ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
lowercase__ : Dict = table_cast(__lowerCAmelCase , self.config.features.arrow_schema )
return pa_table
def _lowerCAmelCase( self , __lowerCAmelCase ) -> Optional[int]:
for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(__lowerCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase__ : Dict = json.load(__lowerCAmelCase )
# We keep only the field we are interested in
lowercase__ : Any = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(__lowerCAmelCase , (list, tuple) ):
lowercase__ : List[Any] = set().union(*[row.keys() for row in dataset] )
lowercase__ : List[Any] = {col: [row.get(__lowerCAmelCase ) for row in dataset] for col in keys}
else:
lowercase__ : Union[str, Any] = dataset
lowercase__ : Optional[int] = pa.Table.from_pydict(__lowerCAmelCase )
yield file_idx, self._cast_table(__lowerCAmelCase )
# If the file has one json object per line
else:
with open(__lowerCAmelCase , '''rb''' ) as f:
lowercase__ : Optional[int] = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
lowercase__ : List[str] = max(self.config.chunksize // 32 , 16 << 10 )
lowercase__ : Union[str, Any] = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
lowercase__ : Tuple = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(__lowerCAmelCase )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
lowercase__ : Dict = batch.decode(self.config.encoding , errors=__lowerCAmelCase ).encode('''utf-8''' )
try:
while True:
try:
lowercase__ : str = paj.read_json(
io.BytesIO(__lowerCAmelCase ) , read_options=paj.ReadOptions(block_size=__lowerCAmelCase ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(__lowerCAmelCase , pa.ArrowInvalid )
and "straddling" not in str(__lowerCAmelCase )
or block_size > len(__lowerCAmelCase )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F"""Batch of {len(__lowerCAmelCase )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.""" )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
__lowerCAmelCase , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
lowercase__ : Optional[int] = json.load(__lowerCAmelCase )
except json.JSONDecodeError:
logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase )}: {e}""" )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(__lowerCAmelCase , __lowerCAmelCase ): # list is the only sequence type supported in JSON
try:
lowercase__ : List[Any] = set().union(*[row.keys() for row in dataset] )
lowercase__ : str = {col: [row.get(__lowerCAmelCase ) for row in dataset] for col in keys}
lowercase__ : Optional[int] = pa.Table.from_pydict(__lowerCAmelCase )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase )}: {e}""" )
raise ValueError(F"""Not able to read records in the JSON file at {file}.""" ) from None
yield file_idx, self._cast_table(__lowerCAmelCase )
break
else:
logger.error(F"""Failed to read file '{file}' with error {type(__lowerCAmelCase )}: {e}""" )
raise ValueError(
F"""Not able to read records in the JSON file at {file}. """
F"""You should probably indicate the field of the JSON file containing your records. """
F"""This JSON file contain the following fields: {str(list(dataset.keys() ) )}. """
F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """ ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase )
batch_idx += 1
| 214 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowerCAmelCase__ = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
lowerCAmelCase__ = logging.WARNING
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = os.getenv("DATASETS_VERBOSITY" , A_ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def a__ ( ):
'''simple docstring'''
return __name__.split("." )[0]
def a__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def a__ ( SCREAMING_SNAKE_CASE : Union[str, Any] = None ):
'''simple docstring'''
if name is None:
lowerCAmelCase : Optional[Any] = _get_library_name()
return logging.getLogger(A_ )
def a__ ( ):
'''simple docstring'''
return _get_library_root_logger().getEffectiveLevel()
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_get_library_root_logger().setLevel(A_ )
def a__ ( ):
'''simple docstring'''
return set_verbosity(A_ )
def a__ ( ):
'''simple docstring'''
return set_verbosity(A_ )
def a__ ( ):
'''simple docstring'''
return set_verbosity(A_ )
def a__ ( ):
'''simple docstring'''
return set_verbosity(A_ )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : List[Any] = False
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : List[str] = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , *snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
"""simple docstring"""
lowerCAmelCase : int = args[0] if args else None
def __iter__( self ):
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self , snake_case__ ):
"""simple docstring"""
def empty_fn(*snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
"""simple docstring"""
return self
def __exit__( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
return
lowerCAmelCase__ = True
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __call__( self , *snake_case__ , snake_case__=False , **snake_case__ ):
"""simple docstring"""
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*__lowerCamelCase , **__lowerCamelCase )
else:
return EmptyTqdm(*__lowerCamelCase , **__lowerCamelCase )
def lowercase__ ( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__lowerCamelCase , **__lowerCamelCase )
def lowercase__ ( self ):
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
lowerCAmelCase__ = _tqdm_cls()
def a__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : int = True
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : str = False
| 108 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""flax""", """transformers"""]
def __init__( self: Optional[int] , *__lowerCamelCase: Union[str, Any] , **__lowerCamelCase: List[str] ):
'''simple docstring'''
requires_backends(self , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Optional[int] , *__lowerCamelCase: str , **__lowerCamelCase: Any ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Any , *__lowerCamelCase: List[str] , **__lowerCamelCase: Any ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
class _a ( metaclass=UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""flax""", """transformers"""]
def __init__( self: List[str] , *__lowerCamelCase: str , **__lowerCamelCase: int ):
'''simple docstring'''
requires_backends(self , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Any , *__lowerCamelCase: Union[str, Any] , **__lowerCamelCase: Optional[int] ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: str , *__lowerCamelCase: List[str] , **__lowerCamelCase: str ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
class _a ( metaclass=UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""flax""", """transformers"""]
def __init__( self: List[Any] , *__lowerCamelCase: Optional[int] , **__lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
requires_backends(self , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Optional[Any] , *__lowerCamelCase: Dict , **__lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Any , *__lowerCamelCase: Dict , **__lowerCamelCase: str ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
class _a ( metaclass=UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""flax""", """transformers"""]
def __init__( self: Any , *__lowerCamelCase: Any , **__lowerCamelCase: List[Any] ):
'''simple docstring'''
requires_backends(self , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: List[Any] , *__lowerCamelCase: Optional[int] , **__lowerCamelCase: Any ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def UpperCAmelCase_ ( cls: Tuple , *__lowerCamelCase: int , **__lowerCamelCase: Any ):
'''simple docstring'''
requires_backends(cls , ["flax", "transformers"] )
| 149 | 0 |
"""simple docstring"""
import numpy as np
from transformers import Pipeline
def lowerCamelCase_ (UpperCamelCase__ : Union[str, Any] ):
_UpperCAmelCase : List[str] = np.max(UpperCamelCase__ , axis=-1 , keepdims=UpperCamelCase__ )
_UpperCAmelCase : int = np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=UpperCamelCase__ )
class _UpperCAmelCase ( a ):
'''simple docstring'''
def __lowerCAmelCase ( self , **A ) -> Union[str, Any]:
_UpperCAmelCase : int = {}
if "second_text" in kwargs:
_UpperCAmelCase : Any = kwargs['''second_text''']
return preprocess_kwargs, {}, {}
def __lowerCAmelCase ( self , A , A=None ) -> List[Any]:
return self.tokenizer(A , text_pair=A , return_tensors=self.framework )
def __lowerCAmelCase ( self , A ) -> List[Any]:
return self.model(**A )
def __lowerCAmelCase ( self , A ) -> Tuple:
_UpperCAmelCase : int = model_outputs.logits[0].numpy()
_UpperCAmelCase : Optional[Any] = softmax(A )
_UpperCAmelCase : str = np.argmax(A )
_UpperCAmelCase : int = self.model.config.idalabel[best_class]
_UpperCAmelCase : List[str] = probabilities[best_class].item()
_UpperCAmelCase : Dict = logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 68 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : List[str] = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
_UpperCAmelCase : Optional[Any] = get_activation('''gelu''' )
self.assertTrue(torch.allclose(gelu_python(A ) , torch_builtin(A ) ) )
self.assertFalse(torch.allclose(gelu_python(A ) , gelu_new(A ) ) )
def __lowerCAmelCase ( self ) -> str:
_UpperCAmelCase : Union[str, Any] = torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
_UpperCAmelCase : Tuple = get_activation('''gelu''' )
_UpperCAmelCase : int = get_activation('''gelu_10''' )
_UpperCAmelCase : Optional[int] = torch_builtin(A )
_UpperCAmelCase : Optional[int] = geluaa(A )
_UpperCAmelCase : Optional[Any] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(A ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
get_activation('''gelu''' )
get_activation('''gelu_10''' )
get_activation('''gelu_fast''' )
get_activation('''gelu_new''' )
get_activation('''gelu_python''' )
get_activation('''gelu_pytorch_tanh''' )
get_activation('''linear''' )
get_activation('''mish''' )
get_activation('''quick_gelu''' )
get_activation('''relu''' )
get_activation('''sigmoid''' )
get_activation('''silu''' )
get_activation('''swish''' )
get_activation('''tanh''' )
with self.assertRaises(A ):
get_activation('''bogus''' )
with self.assertRaises(A ):
get_activation(A )
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Dict = get_activation('''gelu''' )
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase : int = get_activation('''gelu''' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(A ):
_UpperCAmelCase : Any = acta.a
| 68 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> Any:
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowerCamelCase_ ( ) -> Tuple:
"""simple docstring"""
print('Truth Table of NOR Gate:' )
print('| Input 1 | Input 2 | Output |' )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(F"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(F"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(F"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 90 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AutoformerForPrediction''',
'''AutoformerModel''',
'''AutoformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 72 | 0 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int ):
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
_a = 4
_a = (1 << p) - 1
for _ in range(p - 2 ):
_a = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(11)) | 153 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : List[str], _lowerCAmelCase : List[str] ):
"""simple docstring"""
_a = BertConfig.from_json_file(_lowerCAmelCase )
print(f'Building PyTorch model from configuration: {config}' )
_a = BertForPreTraining(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tf_weights_in_bert(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict(), _lowerCAmelCase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path) | 153 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def UpperCamelCase ( _lowerCamelCase : int = 2_00_00_00 ):
A__ = [0]
A__ = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
A__ = 0
# the area corresponding to the grid that gives the product closest to target
A__ = 0
# an estimate of b, using the quadratic formula
A__ = 42
# the largest integer less than b_estimate
A__ = 42
# the largest integer less than b_estimate
A__ = 42
# the triangle number corresponding to b_floor
A__ = 42
# the triangle number corresponding to b_ceil
A__ = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
A__ = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
A__ = floor(lowerCAmelCase__ )
A__ = ceil(lowerCAmelCase__ )
A__ = triangle_numbers[b_floor]
A__ = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
A__ = triangle_b_first_guess * triangle_a
A__ = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
A__ = triangle_b_second_guess * triangle_a
A__ = idx_a * b_ceil
return area
if __name__ == "__main__":
print(f"""{solution() = }""")
| 237 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str = "cpu" , lowerCAmelCase__ : Union[str, None] = None ) -> None:
"""simple docstring"""
lowerCAmelCase_ : Any = torch.load(lowerCAmelCase__ , map_location=lowerCAmelCase__ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerCAmelCase__ , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
lowerCAmelCase_ : str = v.half()
if save_path is None: # overwrite src_path
lowerCAmelCase_ : Dict = src_path
torch.save(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
fire.Fire(convert)
| 224 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_lowercase : List[Any] = logging.get_logger(__name__)
class __magic_name__ ( __lowercase):
UpperCamelCase__ = "AutoTokenizer"
UpperCamelCase__ = ["tokenizer"]
UpperCamelCase__ = {
"semantic_prompt": 1,
"coarse_prompt": 2,
"fine_prompt": 2,
}
def __init__( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : List[Any]=None ):
super().__init__(_a )
lowercase_ : List[Any] = speaker_embeddings
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , lowercase_ : Optional[Any] , lowercase_ : Dict="speaker_embeddings_path.json" , **lowercase_ : Optional[Any] ):
if speaker_embeddings_dict_path is not None:
lowercase_ : List[Any] = get_file_from_repo(
_a , _a , subfolder=kwargs.pop("""subfolder""" , _a ) , cache_dir=kwargs.pop("""cache_dir""" , _a ) , force_download=kwargs.pop("""force_download""" , _a ) , proxies=kwargs.pop("""proxies""" , _a ) , resume_download=kwargs.pop("""resume_download""" , _a ) , local_files_only=kwargs.pop("""local_files_only""" , _a ) , use_auth_token=kwargs.pop("""use_auth_token""" , _a ) , revision=kwargs.pop("""revision""" , _a ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_a , _a )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase_ : Optional[int] = None
else:
with open(_a ) as speaker_embeddings_json:
lowercase_ : Tuple = json.load(_a )
else:
lowercase_ : Dict = None
lowercase_ : List[Any] = AutoTokenizer.from_pretrained(_a , **_a )
return cls(tokenizer=_a , speaker_embeddings=_a )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , lowercase_ : Tuple , lowercase_ : Optional[Any]="speaker_embeddings_path.json" , lowercase_ : Dict="speaker_embeddings" , lowercase_ : Tuple = False , **lowercase_ : Dict , ):
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_a , _a , """v2""" ) , exist_ok=_a )
lowercase_ : Any = {}
lowercase_ : Tuple = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase_ : Union[str, Any] = self._load_voice_preset(_a )
lowercase_ : List[Any] = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["""repo_or_path"""] , _a , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_a , )
lowercase_ : int = os.path.join(_a , f'''{prompt_key}_{key}.npy''' )
lowercase_ : Any = tmp_dict
with open(os.path.join(_a , _a ) , """w""" ) as fp:
json.dump(_a , _a )
super().save_pretrained(_a , _a , **_a )
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : List[str] = None , **lowercase_ : Dict ):
lowercase_ : Any = self.speaker_embeddings[voice_preset]
lowercase_ : int = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase_ : Optional[int] = get_file_from_repo(
self.speaker_embeddings.get("""repo_or_path""" , """/""" ) , voice_preset_paths[key] , subfolder=kwargs.pop("""subfolder""" , _a ) , cache_dir=kwargs.pop("""cache_dir""" , _a ) , force_download=kwargs.pop("""force_download""" , _a ) , proxies=kwargs.pop("""proxies""" , _a ) , resume_download=kwargs.pop("""resume_download""" , _a ) , local_files_only=kwargs.pop("""local_files_only""" , _a ) , use_auth_token=kwargs.pop("""use_auth_token""" , _a ) , revision=kwargs.pop("""revision""" , _a ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase_ : int = np.load(_a )
return voice_preset_dict
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , lowercase_ : Union[str, Any] = None ):
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self : Optional[int] , lowercase_ : Optional[int]=None , lowercase_ : Union[str, Any]=None , lowercase_ : Optional[Any]="pt" , lowercase_ : List[Any]=256 , lowercase_ : List[str]=False , lowercase_ : int=True , lowercase_ : List[str]=False , **lowercase_ : Tuple , ):
if voice_preset is not None and not isinstance(_a , _a ):
if (
isinstance(_a , _a )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase_ : Optional[Any] = self._load_voice_preset(_a )
else:
if isinstance(_a , _a ) and not voice_preset.endswith(""".npz""" ):
lowercase_ : List[Any] = voice_preset + '''.npz'''
lowercase_ : Union[str, Any] = np.load(_a )
if voice_preset is not None:
self._validate_voice_preset_dict(_a , **_a )
lowercase_ : Dict = BatchFeature(data=_a , tensor_type=_a )
lowercase_ : List[str] = self.tokenizer(
_a , return_tensors=_a , padding="""max_length""" , max_length=_a , return_attention_mask=_a , return_token_type_ids=_a , add_special_tokens=_a , **_a , )
if voice_preset is not None:
lowercase_ : Optional[Any] = voice_preset
return encoded_text
| 365 | '''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure):
# _fields is a specific attr expected by ctypes
UpperCamelCase__ = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def lowerCamelCase ( ) -> List[Any]:
if os.name == "nt":
lowercase_ : List[Any] = CursorInfo()
lowercase_ : int = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : List[str] = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def lowerCamelCase ( ) -> str:
if os.name == "nt":
lowercase_ : int = CursorInfo()
lowercase_ : Optional[Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
lowercase_ : Optional[int] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCAmelCase__ , ctypes.byref(UpperCAmelCase__ ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def lowerCamelCase ( ) -> Any:
try:
hide_cursor()
yield
finally:
show_cursor()
| 21 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase__ :
def __init__( self : Dict ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[Any]=13 ,lowerCamelCase__ : str=30 ,lowerCamelCase__ : int=2 ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Any=32 ,lowerCamelCase__ : int=2 ,lowerCamelCase__ : List[str]=4 ,lowerCamelCase__ : List[Any]=37 ,lowerCamelCase__ : Any="gelu" ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : Optional[int]=10 ,lowerCamelCase__ : List[str]=0.0_2 ,lowerCamelCase__ : str=3 ,lowerCamelCase__ : str=None ,):
'''simple docstring'''
_UpperCamelCase : Tuple = parent
_UpperCamelCase : int = batch_size
_UpperCamelCase : Tuple = image_size
_UpperCamelCase : str = patch_size
_UpperCamelCase : List[Any] = num_channels
_UpperCamelCase : int = is_training
_UpperCamelCase : Union[str, Any] = use_labels
_UpperCamelCase : int = hidden_size
_UpperCamelCase : List[Any] = num_hidden_layers
_UpperCamelCase : Optional[int] = num_attention_heads
_UpperCamelCase : Dict = intermediate_size
_UpperCamelCase : List[str] = hidden_act
_UpperCamelCase : int = hidden_dropout_prob
_UpperCamelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCamelCase : Optional[int] = type_sequence_label_size
_UpperCamelCase : int = initializer_range
_UpperCamelCase : int = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCamelCase : str = (image_size // patch_size) ** 2
_UpperCamelCase : Optional[int] = num_patches + 1
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
_UpperCamelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCamelCase : Union[str, Any] = None
if self.use_labels:
_UpperCamelCase : List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCamelCase : Tuple = self.get_config()
return config, pixel_values, labels
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return 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=_A ,initializer_range=self.initializer_range ,)
def UpperCamelCase_ ( self : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase : List[str] = TFViTModel(config=_A )
_UpperCamelCase : Dict = model(_A ,training=_A )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
_UpperCamelCase : Optional[int] = self.image_size // 2
_UpperCamelCase : List[str] = pixel_values[:, :, :image_size, :image_size]
_UpperCamelCase : Optional[Any] = model(_A ,interpolate_pos_encoding=_A ,training=_A )
_UpperCamelCase : Optional[int] = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase_ ( self : Union[str, Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[str] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = self.type_sequence_label_size
_UpperCamelCase : Dict = TFViTForImageClassification(_A )
_UpperCamelCase : Tuple = model(_A ,labels=_A ,training=_A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
_UpperCamelCase : List[str] = self.image_size // 2
_UpperCamelCase : List[str] = pixel_values[:, :, :image_size, :image_size]
_UpperCamelCase : List[Any] = model(_A ,interpolate_pos_encoding=_A ,training=_A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_UpperCamelCase : Dict = 1
_UpperCamelCase : List[Any] = TFViTForImageClassification(_A )
_UpperCamelCase : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCamelCase : Any = model(_A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : str = self.prepare_config_and_inputs()
_UpperCamelCase : Dict = config_and_inputs
_UpperCamelCase : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowercase__ ( lowercase , lowercase , unittest.TestCase ):
lowercase__ = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
lowercase__ = (
{"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
if is_tf_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = TFViTModelTester(self )
_UpperCamelCase : Union[str, Any] = ConfigTester(self ,config_class=_A ,has_text_modality=_A ,hidden_size=37 )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViT does not use inputs_embeds' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='ViT does not use inputs_embeds' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : List[str] = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() ,(tf.keras.layers.Layer) )
_UpperCamelCase : List[str] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A ,tf.keras.layers.Layer ) )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase : Optional[Any] = model_class(_A )
_UpperCamelCase : Union[str, Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase : Union[str, Any] = [*signature.parameters.keys()]
_UpperCamelCase : Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,_A )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
_UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@slow
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
_UpperCamelCase : str = TFViTModel.from_pretrained('google/vit-base-patch16-224' )
self.assertIsNotNone(_A )
def A__ ( ):
_UpperCamelCase : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class lowercase__ ( unittest.TestCase ):
@cached_property
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224' ) if is_vision_available() else None
@slow
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
_UpperCamelCase : Tuple = TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224' )
_UpperCamelCase : Union[str, Any] = self.default_image_processor
_UpperCamelCase : Dict = prepare_img()
_UpperCamelCase : List[Any] = image_processor(images=_A ,return_tensors='tf' )
# forward pass
_UpperCamelCase : Optional[int] = model(**_A )
# verify the logits
_UpperCamelCase : Any = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape ,_A )
_UpperCamelCase : Tuple = tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )
tf.debugging.assert_near(outputs.logits[0, :3] ,_A ,atol=1E-4 )
| 83 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class snake_case__ ( enum.Enum):
a_ = 0
a_ = 1
a_ = 2
@add_end_docstrings(UpperCamelCase)
class snake_case__ ( UpperCamelCase):
a_ = "\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n "
def __init__( self : List[str] , *_A : Dict , **_A : int ) -> Optional[int]:
super().__init__(*_A , **_A )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
UpperCAmelCase_ : Dict = None
if self.model.config.prefix is not None:
UpperCAmelCase_ : Tuple = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
UpperCAmelCase_ : Optional[Any] = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = self._sanitize_parameters(prefix=_A , **self._forward_params )
UpperCAmelCase_ : int = {**self._preprocess_params, **preprocess_params}
UpperCAmelCase_ : List[str] = {**self._forward_params, **forward_params}
def A ( self : Union[str, Any] , _A : int=None , _A : str=None , _A : Union[str, Any]=None , _A : List[Any]=None , _A : List[Any]=None , _A : int=None , _A : Optional[int]=None , _A : List[Any]=None , **_A : List[Any] , ) -> Dict:
UpperCAmelCase_ : Union[str, Any] = {}
if prefix is not None:
UpperCAmelCase_ : List[Any] = prefix
if prefix:
UpperCAmelCase_ : Tuple = self.tokenizer(
_A , padding=_A , add_special_tokens=_A , return_tensors=self.framework )
UpperCAmelCase_ : List[Any] = prefix_inputs['''input_ids'''].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
F"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
''' [None, \'hole\']''' )
UpperCAmelCase_ : Union[str, Any] = handle_long_generation
preprocess_params.update(_A )
UpperCAmelCase_ : Optional[int] = generate_kwargs
UpperCAmelCase_ : Tuple = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_full_text`''' )
if return_tensors is not None:
raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`''' )
UpperCAmelCase_ : int = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError('''`return_text` is mutually exclusive with `return_tensors`''' )
UpperCAmelCase_ : List[Any] = ReturnType.TENSORS
if return_type is not None:
UpperCAmelCase_ : List[Any] = return_type
if clean_up_tokenization_spaces is not None:
UpperCAmelCase_ : List[Any] = clean_up_tokenization_spaces
if stop_sequence is not None:
UpperCAmelCase_ : Any = self.tokenizer.encode(_A , add_special_tokens=_A )
if len(_A ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
UpperCAmelCase_ : str = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def A ( self : Dict , *_A : Optional[Any] , **_A : Any ) -> Any:
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({'''add_space_before_punct_symbol''': True} )
return super()._parse_and_tokenize(*_A , **_A )
def __call__( self : List[Any] , _A : Union[str, Any] , **_A : List[str] ) -> Dict:
return super().__call__(_A , **_A )
def A ( self : List[Any] , _A : List[Any] , _A : Any="" , _A : Dict=None , **_A : Dict ) -> Optional[Any]:
UpperCAmelCase_ : Tuple = self.tokenizer(
prefix + prompt_text , padding=_A , add_special_tokens=_A , return_tensors=self.framework )
UpperCAmelCase_ : str = prompt_text
if handle_long_generation == "hole":
UpperCAmelCase_ : List[str] = inputs['''input_ids'''].shape[-1]
if "max_new_tokens" in generate_kwargs:
UpperCAmelCase_ : Optional[int] = generate_kwargs['''max_new_tokens''']
else:
UpperCAmelCase_ : Union[str, Any] = generate_kwargs.get('''max_length''' , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError('''We cannot infer how many new tokens are expected''' )
if cur_len + new_tokens > self.tokenizer.model_max_length:
UpperCAmelCase_ : Dict = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
'''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
''' models max length''' )
UpperCAmelCase_ : List[str] = inputs['''input_ids'''][:, -keep_length:]
if "attention_mask" in inputs:
UpperCAmelCase_ : Optional[int] = inputs['''attention_mask'''][:, -keep_length:]
return inputs
def A ( self : List[str] , _A : Optional[Any] , **_A : str ) -> Optional[int]:
UpperCAmelCase_ : Any = model_inputs['''input_ids''']
UpperCAmelCase_ : Dict = model_inputs.get('''attention_mask''' , _A )
# Allow empty prompts
if input_ids.shape[1] == 0:
UpperCAmelCase_ : Any = None
UpperCAmelCase_ : List[Any] = None
UpperCAmelCase_ : Union[str, Any] = 1
else:
UpperCAmelCase_ : Optional[int] = input_ids.shape[0]
UpperCAmelCase_ : Dict = model_inputs.pop('''prompt_text''' )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
UpperCAmelCase_ : List[str] = generate_kwargs.pop('''prefix_length''' , 0 )
if prefix_length > 0:
UpperCAmelCase_ : str = '''max_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].max_new_tokens is not None
)
if not has_max_new_tokens:
UpperCAmelCase_ : Any = generate_kwargs.get('''max_length''' ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
UpperCAmelCase_ : Optional[Any] = '''min_new_tokens''' in generate_kwargs or (
'''generation_config''' in generate_kwargs
and generate_kwargs['''generation_config'''].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
UpperCAmelCase_ : Union[str, Any] = self.model.generate(input_ids=_A , attention_mask=_A , **_A )
UpperCAmelCase_ : Any = generated_sequence.shape[0]
if self.framework == "pt":
UpperCAmelCase_ : List[str] = generated_sequence.reshape(_A , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
UpperCAmelCase_ : int = tf.reshape(_A , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def A ( self : int , _A : List[Any] , _A : Dict=ReturnType.FULL_TEXT , _A : Dict=True ) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = model_outputs['''generated_sequence'''][0]
UpperCAmelCase_ : int = model_outputs['''input_ids''']
UpperCAmelCase_ : str = model_outputs['''prompt_text''']
UpperCAmelCase_ : Any = generated_sequence.numpy().tolist()
UpperCAmelCase_ : int = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
UpperCAmelCase_ : Optional[Any] = {'''generated_token_ids''': sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
UpperCAmelCase_ : Any = self.tokenizer.decode(
_A , skip_special_tokens=_A , clean_up_tokenization_spaces=_A , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
UpperCAmelCase_ : List[str] = 0
else:
UpperCAmelCase_ : str = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_A , clean_up_tokenization_spaces=_A , ) )
if return_type == ReturnType.FULL_TEXT:
UpperCAmelCase_ : Dict = prompt_text + text[prompt_length:]
else:
UpperCAmelCase_ : Dict = text[prompt_length:]
UpperCAmelCase_ : List[str] = {'''generated_text''': all_text}
records.append(_A )
return records
| 304 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
class __lowerCAmelCase ( lowercase__):
_a = 'encoder-decoder'
_a = True
def __init__( self: Optional[Any] , **_lowerCAmelCase: Optional[Any] ):
super().__init__(**_UpperCamelCase )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
lowercase :Optional[Any] = kwargs.pop("encoder" )
lowercase :str = encoder_config.pop("model_type" )
lowercase :Optional[Any] = kwargs.pop("decoder" )
lowercase :Dict = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
lowercase :Tuple = AutoConfig.for_model(_UpperCamelCase , **_UpperCamelCase )
lowercase :Union[str, Any] = AutoConfig.for_model(_UpperCamelCase , **_UpperCamelCase )
lowercase :int = True
@classmethod
def SCREAMING_SNAKE_CASE ( cls: Optional[Any] , _lowerCAmelCase: PretrainedConfig , _lowerCAmelCase: PretrainedConfig , **_lowerCAmelCase: List[str] ):
logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
lowercase :Optional[Any] = True
lowercase :Optional[int] = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **_UpperCamelCase )
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :List[str] = copy.deepcopy(self.__dict__ )
lowercase :int = self.encoder.to_dict()
lowercase :List[str] = self.decoder.to_dict()
lowercase :int = self.__class__.model_type
return output
| 358 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
_a = StableDiffusionPanoramaPipeline
_a = TEXT_TO_IMAGE_PARAMS
_a = TEXT_TO_IMAGE_BATCH_PARAMS
_a = TEXT_TO_IMAGE_IMAGE_PARAMS
_a = TEXT_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE ( self: int ):
torch.manual_seed(0 )
lowercase :List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
lowercase :Any = DDIMScheduler()
torch.manual_seed(0 )
lowercase :Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
lowercase :Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
lowercase :Any = CLIPTextModel(_lowerCAmelCase )
lowercase :str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase :Union[str, Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def SCREAMING_SNAKE_CASE ( self: int , _lowerCAmelCase: List[Any] , _lowerCAmelCase: Dict=0 ):
lowercase :Any = torch.manual_seed(_lowerCAmelCase )
lowercase :Any = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def SCREAMING_SNAKE_CASE ( self: List[Any] ):
lowercase :List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :int = self.get_dummy_components()
lowercase :int = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase :Tuple = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :List[str] = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :List[Any] = sd_pipe(**_lowerCAmelCase ).images
lowercase :List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase :List[str] = np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def SCREAMING_SNAKE_CASE ( self: int ):
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.2_5e-3 )
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
lowercase :Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :List[str] = self.get_dummy_components()
lowercase :Optional[Any] = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase :Any = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :Optional[int] = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :List[Any] = "french fries"
lowercase :int = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
lowercase :int = output.images
lowercase :List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase :Optional[Any] = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :int = self.get_dummy_components()
lowercase :List[str] = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase :int = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :Dict = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :Any = sd_pipe(**_lowerCAmelCase , view_batch_size=2 )
lowercase :Union[str, Any] = output.images
lowercase :List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase :Optional[int] = np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: str ):
lowercase :int = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :List[Any] = self.get_dummy_components()
lowercase :Tuple = EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" )
lowercase :Tuple = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase :Any = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :Optional[Any] = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :List[Any] = sd_pipe(**_lowerCAmelCase ).images
lowercase :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase :Optional[Any] = np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: Optional[Any] ):
lowercase :Optional[int] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase :List[Any] = self.get_dummy_components()
lowercase :int = PNDMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , skip_prk_steps=_lowerCAmelCase )
lowercase :List[str] = StableDiffusionPanoramaPipeline(**_lowerCAmelCase )
lowercase :int = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowercase :Union[str, Any] = self.get_dummy_inputs(_lowerCAmelCase )
lowercase :Any = sd_pipe(**_lowerCAmelCase ).images
lowercase :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase :Tuple = np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase):
def SCREAMING_SNAKE_CASE ( self: str ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self: Any , _lowerCAmelCase: Union[str, Any]=0 ):
lowercase :Any = torch.manual_seed(_lowerCAmelCase )
lowercase :Optional[int] = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def SCREAMING_SNAKE_CASE ( self: Tuple ):
lowercase :Dict = "stabilityai/stable-diffusion-2-base"
lowercase :Optional[Any] = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="scheduler" )
lowercase :List[str] = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase :Any = self.get_inputs()
lowercase :Optional[int] = pipe(**_lowerCAmelCase ).images
lowercase :int = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
lowercase :Tuple = np.array(
[
0.36_96_83_92,
0.27_02_53_72,
0.32_44_67_66,
0.28_37_93_87,
0.36_36_32_74,
0.30_73_33_47,
0.27_10_00_27,
0.27_05_41_25,
0.25_53_60_96,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE ( self: Union[str, Any] ):
lowercase :Union[str, Any] = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=_lowerCAmelCase )
lowercase :int = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase :int = self.get_inputs()
lowercase :Optional[int] = pipe(**_lowerCAmelCase ).images
lowercase :List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_12, 20_48, 3)
lowercase :Union[str, Any] = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE ( self: List[str] ):
lowercase :Dict = 0
def callback_fn(_lowerCAmelCase: int , _lowerCAmelCase: int , _lowerCAmelCase: torch.FloatTensor ) -> None:
lowercase :Any = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase :Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
lowercase :Optional[int] = latents[0, -3:, -3:, -1]
lowercase :Any = np.array(
[
0.18_68_18_69,
0.33_90_78_16,
0.5_36_12_76,
0.14_43_28_65,
-0.02_85_66_11,
-0.73_94_11_23,
0.23_39_79_87,
0.47_32_26_82,
-0.37_82_31_64,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowercase :str = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 2_56)
lowercase :Optional[int] = latents[0, -3:, -3:, -1]
lowercase :Optional[Any] = np.array(
[
0.18_53_96_45,
0.33_98_72_48,
0.5_37_85_59,
0.14_43_71_42,
-0.02_45_52_61,
-0.7_33_83_17,
0.23_99_07_55,
0.47_35_62_72,
-0.3_78_65_05,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowercase :int = False
lowercase :Tuple = "stabilityai/stable-diffusion-2-base"
lowercase :Optional[Any] = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="scheduler" )
lowercase :Optional[int] = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase :Optional[int] = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase :int = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def SCREAMING_SNAKE_CASE ( self: str ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase :Optional[Any] = "stabilityai/stable-diffusion-2-base"
lowercase :Dict = DDIMScheduler.from_pretrained(_lowerCAmelCase , subfolder="scheduler" )
lowercase :Optional[int] = StableDiffusionPanoramaPipeline.from_pretrained(_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase )
lowercase :Union[str, Any] = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase :Optional[int] = self.get_inputs()
lowercase :Union[str, Any] = pipe(**_lowerCAmelCase )
lowercase :List[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 158 | 0 |
'''simple docstring'''
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a ( _lowerCAmelCase , unittest.TestCase ):
_lowerCAmelCase = RoFormerTokenizer
_lowerCAmelCase = RoFormerTokenizerFast
_lowerCAmelCase = True
_lowerCAmelCase = True
def __UpperCAmelCase ( self ) -> Dict:
super().setUp()
def __UpperCAmelCase ( self , **__magic_name__ ) -> Optional[Any]:
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **__magic_name__ )
def __UpperCAmelCase ( self , **__magic_name__ ) -> Optional[int]:
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **__magic_name__ )
def __UpperCAmelCase ( self ) -> Any:
_a = "永和服装饰品有限公司,今天天气非常好"
_a = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def __UpperCAmelCase ( self ) -> List[str]:
_a = self.get_tokenizer()
_a = self.get_chinese_input_output_texts()
_a = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , output_text.split() )
_a = tokens + [tokenizer.unk_token]
_a = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ )
def __UpperCAmelCase ( self ) -> Any:
_a = self.get_rust_tokenizer()
_a = self.get_chinese_input_output_texts()
_a = tokenizer.tokenize(__magic_name__ )
self.assertListEqual(__magic_name__ , output_text.split() )
_a = tokens + [tokenizer.unk_token]
_a = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ )
def __UpperCAmelCase ( self ) -> str:
pass
def __UpperCAmelCase ( self ) -> Union[str, Any]:
pass
def __UpperCAmelCase ( self ) -> Dict:
pass
| 168 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[Any] ={
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
'''XCLIPVisionConfig''',
],
'''processing_x_clip''': ['''XCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Tuple =[
'''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XCLIPModel''',
'''XCLIPPreTrainedModel''',
'''XCLIPTextModel''',
'''XCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
lowerCAmelCase : str =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 223 | 0 |
from string import ascii_lowercase, ascii_uppercase
def lowercase( UpperCamelCase_ ) -> str:
'''simple docstring'''
if not sentence:
return ""
UpperCamelCase = dict(zip(UpperCamelCase_ , UpperCamelCase_ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 165 | import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def lowercase( UpperCamelCase_ ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
UpperCamelCase = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
UpperCamelCase = 4
UpperCamelCase = 48
UpperCamelCase = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
UpperCamelCase = [6, 6, 6, 6]
UpperCamelCase = 60
UpperCamelCase = [6, 6, 6, 6]
UpperCamelCase = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
UpperCamelCase = 4
UpperCamelCase = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
UpperCamelCase = 1
UpperCamelCase = 1
UpperCamelCase = 126
UpperCamelCase = 7
UpperCamelCase = 2_5_5.0
UpperCamelCase = """"""
return config
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name and "layers" not in name:
UpperCamelCase = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
UpperCamelCase = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""" )
if "layers" in name:
UpperCamelCase = name.replace("""layers""" , """encoder.stages""" )
if "residual_group.blocks" in name:
UpperCamelCase = name.replace("""residual_group.blocks""" , """layers""" )
if "attn.proj" in name:
UpperCamelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
UpperCamelCase = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
UpperCamelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
UpperCamelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
UpperCamelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCamelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
UpperCamelCase = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
UpperCamelCase = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
UpperCamelCase = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
UpperCamelCase = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if "patch_embed.proj" in name:
UpperCamelCase = name.replace("""patch_embed.proj""" , """patch_embed.projection""" )
if name == "norm.weight":
UpperCamelCase = """layernorm.weight"""
if name == "norm.bias":
UpperCamelCase = """layernorm.bias"""
if "conv_first" in name:
UpperCamelCase = name.replace("""conv_first""" , """first_convolution""" )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
UpperCamelCase = name.replace("""conv_last""" , """final_convolution""" )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
UpperCamelCase = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""" )
if "upsample.0" in name:
UpperCamelCase = name.replace("""upsample.0""" , """upsample.convolution_0""" )
if "upsample.2" in name:
UpperCamelCase = name.replace("""upsample.2""" , """upsample.convolution_1""" )
UpperCamelCase = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
UpperCamelCase = name.replace("""upsample.0.weight""" , """upsample.conv.weight""" )
UpperCamelCase = name.replace("""upsample.0.bias""" , """upsample.conv.bias""" )
else:
pass
else:
UpperCamelCase = """swin2sr.""" + name
return name
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCamelCase = orig_state_dict.pop(UpperCamelCase_ )
if "qkv" in key:
UpperCamelCase = key.split(""".""" )
UpperCamelCase = int(key_split[1] )
UpperCamelCase = int(key_split[4] )
UpperCamelCase = config.embed_dim
if "weight" in key:
UpperCamelCase = val[:dim, :]
UpperCamelCase = val[dim : dim * 2, :]
UpperCamelCase = val[-dim:, :]
else:
UpperCamelCase = val[:dim]
UpperCamelCase = val[dim : dim * 2]
UpperCamelCase = val[-dim:]
pass
else:
UpperCamelCase = val
return orig_state_dict
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = get_config(UpperCamelCase_ )
UpperCamelCase = SwinaSRForImageSuperResolution(UpperCamelCase_ )
model.eval()
UpperCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase_ , map_location="""cpu""" )
UpperCamelCase = convert_state_dict(UpperCamelCase_ , UpperCamelCase_ )
UpperCamelCase , UpperCamelCase = model.load_state_dict(UpperCamelCase_ , strict=UpperCamelCase_ )
if len(UpperCamelCase_ ) > 0:
raise ValueError("""Missing keys when converting: {}""".format(UpperCamelCase_ ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(f"""Unexpected key {key} in state_dict""" )
# verify values
UpperCamelCase = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
UpperCamelCase = Image.open(requests.get(UpperCamelCase_ , stream=UpperCamelCase_ ).raw ).convert("""RGB""" )
UpperCamelCase = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
UpperCamelCase = 126 if """Jpeg""" in checkpoint_url else 256
UpperCamelCase = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] , std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
UpperCamelCase = transforms(UpperCamelCase_ ).unsqueeze(0 )
if config.num_channels == 1:
UpperCamelCase = pixel_values[:, 0, :, :].unsqueeze(1 )
UpperCamelCase = model(UpperCamelCase_ )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
UpperCamelCase = torch.Size([1, 3, 512, 512] )
UpperCamelCase = torch.tensor(
[[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
UpperCamelCase = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase = torch.tensor(
[[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
UpperCamelCase = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase = torch.tensor(
[[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
UpperCamelCase = torch.Size([1, 3, 512, 512] )
UpperCamelCase = torch.tensor(
[[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
UpperCamelCase = torch.Size([1, 3, 1024, 1024] )
UpperCamelCase = torch.tensor(
[[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]] )
assert (
outputs.reconstruction.shape == expected_shape
), f"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , UpperCamelCase_ , atol=1E-3 )
print("""Looks ok!""" )
UpperCamelCase = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
UpperCamelCase = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase_ )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCamelCase_ )
if push_to_hub:
model.push_to_hub(f"""caidas/{model_name}""" )
processor.push_to_hub(f"""caidas/{model_name}""" )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
type=str,
help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 165 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase = '''▁'''
_lowercase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class lowerCAmelCase_ ( snake_case_ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase: Any = BertGenerationTokenizer
_lowerCamelCase: Dict = False
_lowerCamelCase: Any = True
def _SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
super().setUp()
A = BertGenerationTokenizer(A_ ,keep_accents=A_ )
tokenizer.save_pretrained(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
A = '<s>'
A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) ,A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) ,A_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,'<unk>' )
self.assertEqual(vocab_keys[1] ,'<s>' )
self.assertEqual(vocab_keys[-1] ,'<pad>' )
self.assertEqual(len(A_ ) ,1002 )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size ,1000 )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
A = BertGenerationTokenizer(A_ ,keep_accents=A_ )
A = tokenizer.tokenize('This is a test' )
self.assertListEqual(A_ ,['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A_ ) ,[285, 46, 10, 170, 382] ,)
A = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
A_ ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] ,)
A = tokenizer.convert_tokens_to_ids(A_ )
self.assertListEqual(
A_ ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,)
A = tokenizer.convert_ids_to_tokens(A_ )
self.assertListEqual(
A_ ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] ,)
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
@slow
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
A = 'Hello World!'
A = [1_8536, 2260, 101]
self.assertListEqual(A_ ,self.big_tokenizer.encode(A_ ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : int ) -> Any:
A = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
A = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(A_ ,self.big_tokenizer.encode(A_ ) )
@require_torch
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
A = list(self.big_tokenizer.get_vocab().keys() )[:10]
A = ' '.join(A_ )
A = self.big_tokenizer.encode_plus(A_ ,return_tensors='pt' ,return_token_type_ids=A_ )
A = self.big_tokenizer.batch_encode_plus(
[sequence + ' ' + sequence] ,return_tensors='pt' ,return_token_type_ids=A_ )
A = BertGenerationConfig()
A = BertGenerationEncoder(A_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**A_ )
model(**A_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[Any]:
# fmt: off
A = {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A_ ,model_name='google/bert_for_seq_generation_L-24_bbc_encoder' ,revision='c817d1fd1be2ffa69431227a1fe320544943d4db' ,) | 74 |
from collections import deque
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = len(lowerCamelCase__ )
lowerCamelCase_ = deque()
lowerCamelCase_ = [False for _ in range(lowerCamelCase__ )]
lowerCamelCase_ = [-1 for _ in range(lowerCamelCase__ )]
lowerCamelCase_ = index_of[:]
def strong_connect(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = index # the number when this node is seen
lowerCamelCase_ = index # lowest rank node reachable from here
index += 1
stack.append(lowerCamelCase__ )
lowerCamelCase_ = True
for w in g[v]:
if index_of[w] == -1:
lowerCamelCase_ = strong_connect(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
lowerCamelCase_ = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
lowerCamelCase_ = []
lowerCamelCase_ = stack.pop()
lowerCamelCase_ = False
component.append(lowerCamelCase__ )
while w != v:
lowerCamelCase_ = stack.pop()
lowerCamelCase_ = False
component.append(lowerCamelCase__ )
components.append(lowerCamelCase__ )
return index
lowerCamelCase_ = []
for v in range(lowerCamelCase__ ):
if index_of[v] == -1:
strong_connect(lowerCamelCase__ , 0 , lowerCamelCase__ )
return components
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [[] for _ in range(lowerCamelCase__ )]
for u, v in edges:
g[u].append(lowerCamelCase__ )
return g
if __name__ == "__main__":
# Test
__A =7
__A =[0, 0, 1, 2, 3, 3, 4, 4, 6]
__A =[1, 3, 2, 0, 1, 4, 5, 6, 5]
__A =[(u, v) for u, v in zip(source, target)]
__A =create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 19 | 0 |
"""simple docstring"""
from __future__ import annotations
__A : Union[str, Any] = list[list[int]]
# assigning initial values to the grid
__A : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
__A : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def lowercase ( _SCREAMING_SNAKE_CASE : Matrix , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def lowercase ( _SCREAMING_SNAKE_CASE : Matrix ):
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def lowercase ( _SCREAMING_SNAKE_CASE : Matrix ):
'''simple docstring'''
if location := find_empty_location(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = digit
if sudoku(_SCREAMING_SNAKE_CASE ) is not None:
return grid
_UpperCAmelCase = 0
return None
def lowercase ( _SCREAMING_SNAKE_CASE : Matrix ):
'''simple docstring'''
for row in grid:
for cell in row:
print(_SCREAMING_SNAKE_CASE , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
__A : Optional[int] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 350 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
UpperCamelCase__ = None
__A : Union[str, Any] = namedtuple("CoinsDistribResult", "moves excess")
def lowercase ( _SCREAMING_SNAKE_CASE : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(_SCREAMING_SNAKE_CASE ) != count_coins(_SCREAMING_SNAKE_CASE ):
raise ValueError('''The nodes number should be same as the number of coins''' )
# Main calculation
def get_distrib(_SCREAMING_SNAKE_CASE : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.left )
_UpperCAmelCase , _UpperCAmelCase = get_distrib(node.right )
_UpperCAmelCase = 1 - left_distrib_excess
_UpperCAmelCase = 1 - right_distrib_excess
_UpperCAmelCase = (
left_distrib_moves
+ right_distrib_moves
+ abs(_SCREAMING_SNAKE_CASE )
+ abs(_SCREAMING_SNAKE_CASE )
)
_UpperCAmelCase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return get_distrib(_SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 326 | 0 |
"""simple docstring"""
from timeit import timeit
def _snake_case ( UpperCamelCase : int ):
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def _snake_case ( UpperCamelCase : int ):
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase : Dict = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def _snake_case ( ):
def do_benchmark(UpperCamelCase : int ) -> None:
UpperCAmelCase : Dict = """import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(UpperCamelCase ) = }" )
UpperCAmelCase : List[str] = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=UpperCamelCase )
print(F"timeit() runs in {timing} seconds" )
print(F"{get_set_bits_count_using_brian_kernighans_algorithm(UpperCamelCase ) = }" )
UpperCAmelCase : str = timeit(
"""z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=UpperCamelCase , )
print(F"timeit() runs in {timing} seconds" )
for number in (25, 37, 58, 0):
do_benchmark(UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 109 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( UpperCamelCase : Callable , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
UpperCAmelCase : Any = int(np.ceil((x_end - xa) / step_size ) )
UpperCAmelCase : Optional[Any] = np.zeros((n + 1,) )
UpperCAmelCase : Optional[int] = ya
UpperCAmelCase : int = xa
for k in range(UpperCamelCase ):
UpperCAmelCase : Optional[int] = y[k] + step_size * ode_func(UpperCamelCase , y[k] )
UpperCAmelCase : Optional[int] = y[k] + (
(step_size / 2) * (ode_func(UpperCamelCase , y[k] ) + ode_func(x + step_size , UpperCamelCase ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str:
"""simple docstring"""
if not (isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
UpperCAmelCase_ : Tuple = len(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Dict = len(_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : Any = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
UpperCAmelCase_ : Union[str, Any] = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
UpperCAmelCase_ : Any = i
UpperCAmelCase_ : Any = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 67 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Union[str, Any] =["torch", "torchsde"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["torch", "torchsde"] )
@classmethod
def UpperCamelCase__ ( cls ,*_snake_case ,**_snake_case ):
requires_backends(cls ,["torch", "torchsde"] )
@classmethod
def UpperCamelCase__ ( cls ,*_snake_case ,**_snake_case ):
requires_backends(cls ,["torch", "torchsde"] )
| 67 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( _lowercase : Optional[int] , _lowercase : Tuple , _lowercase : Any ) ->Optional[Any]:
'''simple docstring'''
a : Any = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
a : Union[str, Any] = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
a : List[Any] = F"""{src_lang}-{tgt_lang}"""
a : Optional[int] = F"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR's WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
"""
os.makedirs(_lowercase , exist_ok=_lowercase )
a : Tuple = os.path.join(_lowercase , "README.md" )
print(F"""Generating {path}""" )
with open(_lowercase , "w" , encoding="utf-8" ) as f:
f.write(_lowercase )
# make sure we are under the root of the project
a : int = Path(__file__).resolve().parent.parent.parent
a : Union[str, Any] = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a , a , a : List[str] = model_name.split('''-''')
a : List[str] = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 105 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Dict = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
a : Dict = requests.get(base_url + url ).json()[0]["urls"][0]["src"]
return requests.get(_lowercase ).content
if __name__ == "__main__":
a : str = input('''Enter Video/IGTV url: ''').strip()
a : Any = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, '''wb''') as fp:
fp.write(download_video(url))
print(F'''Done. Video saved to disk as {file_name}.''')
| 105 | 1 |
import cva
import numpy as np
class a__ :
def __init__( self , A , A ) -> Optional[Any]:
'''simple docstring'''
if k in (0.0_4, 0.0_6):
a = k
a = window_size
else:
raise ValueError("invalid k value" )
def __str__( self ) -> str:
'''simple docstring'''
return str(self.k )
def lowerCAmelCase_ ( self , A ) -> tuple[cva.Mat, list[list[int]]]:
'''simple docstring'''
a = cva.imread(A , 0 )
a , a = img.shape
a = []
a = img.copy()
a = cva.cvtColor(A , cva.COLOR_GRAY2RGB )
a , a = np.gradient(A )
a = dx**2
a = dy**2
a = dx * dy
a = 0.0_4
a = self.window_size // 2
for y in range(A , h - offset ):
for x in range(A , w - offset ):
a = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
a = (wxx * wyy) - (wxy**2)
a = wxx + wyy
a = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
lowercase__ : Optional[int] = HarrisCorner(0.04, 3)
lowercase__ , lowercase__ : Optional[Any] = edge_detect.detect("path_to_image")
cva.imwrite("detect.png", color_img)
| 180 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
a = 0
a = number
while duplicate > 0:
a , a = divmod(__UpperCamelCase , 10)
fact_sum += factorial(__UpperCamelCase)
return fact_sum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
lowercase__ : str = int(input("Enter number: ").strip())
print(
F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
)
| 180 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
SCREAMING_SNAKE_CASE_ = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
for attribute in key.split(""".""" ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
SCREAMING_SNAKE_CASE = """lm_head"""
SCREAMING_SNAKE_CASE = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
SCREAMING_SNAKE_CASE = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
SCREAMING_SNAKE_CASE = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE = value
else:
SCREAMING_SNAKE_CASE = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == """group""" , )
SCREAMING_SNAKE_CASE = True
else:
for key, mapped_key in MAPPING.items():
SCREAMING_SNAKE_CASE = """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
SCREAMING_SNAKE_CASE = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE = name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
SCREAMING_SNAKE_CASE = mapped_key.replace("""*""" , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
SCREAMING_SNAKE_CASE = """weight_g"""
elif "weight_v" in name:
SCREAMING_SNAKE_CASE = """weight_v"""
elif "bias" in name:
SCREAMING_SNAKE_CASE = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
SCREAMING_SNAKE_CASE = """weight"""
else:
SCREAMING_SNAKE_CASE = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = full_name.split("""conv_layers.""" )[-1]
SCREAMING_SNAKE_CASE = name.split(""".""" )
SCREAMING_SNAKE_CASE = int(items[0] )
SCREAMING_SNAKE_CASE = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True ) -> List[Any]:
'''simple docstring'''
if config_path is not None:
SCREAMING_SNAKE_CASE = UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = UniSpeechConfig()
if is_finetuned:
if dict_path:
SCREAMING_SNAKE_CASE = Dictionary.load_from_json(_SCREAMING_SNAKE_CASE )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
SCREAMING_SNAKE_CASE = target_dict.pad_index
SCREAMING_SNAKE_CASE = target_dict.bos_index
SCREAMING_SNAKE_CASE = target_dict.eos_index
SCREAMING_SNAKE_CASE = len(target_dict.symbols )
SCREAMING_SNAKE_CASE = os.path.join(_SCREAMING_SNAKE_CASE , """vocab.json""" )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE ) )
return
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = target_dict.indices
# fairseq has the <pad> and <s> switched
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 43
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = WavaVecaPhonemeCTCTokenizer(
_SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=_SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE = True if config.feat_extract_norm == """layer""" else False
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = UniSpeechForCTC(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE )
if is_finetuned:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] ), """w2v_path""": checkpoint_path} )
else:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
SCREAMING_SNAKE_CASE = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 296 |
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296 | 1 |
lowercase__ : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def a__ ( lowercase : dict, lowercase : Union[str, Any], lowercase : int ) -> list[str]:
"""simple docstring"""
_UpperCamelCase = set()
# keep track of all the paths to be checked
_UpperCamelCase = [[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
_UpperCamelCase = queue.pop(0 )
# get the last node from the path
_UpperCamelCase = path[-1]
if node not in explored:
_UpperCamelCase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
_UpperCamelCase = list(lowercase )
new_path.append(lowercase )
queue.append(lowercase )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(lowercase )
# in case there's no path between the 2 nodes
return []
def a__ ( lowercase : dict, lowercase : Dict, lowercase : Union[str, Any] ) -> int:
"""simple docstring"""
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
_UpperCamelCase = [start]
_UpperCamelCase = set(lowercase )
# Keep tab on distances from `start` node.
_UpperCamelCase = {start: 0, target: -1}
while queue:
_UpperCamelCase = queue.pop(0 )
if node == target:
_UpperCamelCase = (
dist[node] if dist[target] == -1 else min(dist[target], dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(lowercase )
queue.append(lowercase )
_UpperCamelCase = 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
| 362 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : Tuple = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
_snake_case : int = 'trocr'
_snake_case : List[str] = ['past_key_values']
_snake_case : Tuple = {
'num_attention_heads': 'decoder_attention_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'decoder_layers',
}
def __init__( self : Tuple , lowerCAmelCase__ : Union[str, Any]=50265 , lowerCAmelCase__ : List[Any]=1024 , lowerCAmelCase__ : List[str]=12 , lowerCAmelCase__ : int=16 , lowerCAmelCase__ : List[Any]=4096 , lowerCAmelCase__ : Optional[int]="gelu" , lowerCAmelCase__ : int=512 , lowerCAmelCase__ : Optional[Any]=0.1 , lowerCAmelCase__ : Optional[int]=0.0 , lowerCAmelCase__ : Dict=0.0 , lowerCAmelCase__ : Tuple=2 , lowerCAmelCase__ : Dict=0.02 , lowerCAmelCase__ : Tuple=0.0 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : Optional[Any]=False , lowerCAmelCase__ : Any=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : Optional[int]=1 , lowerCAmelCase__ : List[Any]=0 , lowerCAmelCase__ : Any=2 , **lowerCAmelCase__ : Optional[int] , ) -> Any:
'''simple docstring'''
_UpperCamelCase = vocab_size
_UpperCamelCase = d_model
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = activation_function
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = init_std
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = use_cache
_UpperCamelCase = scale_embedding
_UpperCamelCase = use_learned_position_embeddings
_UpperCamelCase = layernorm_embedding
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
| 287 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , A=1_3 , A=7 , A=True , A=True , A=True , A=True , A=9_9 , A=3_2 , A=5 , A=4 , A=4 , A="gelu" , A=0.0 , A=0.1 , A=True , A=5_1_2 , A=1_6 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> List[str]:
_UpperCAmelCase : Optional[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Any = seq_length
_UpperCAmelCase : List[Any] = is_training
_UpperCAmelCase : List[str] = use_input_mask
_UpperCAmelCase : List[Any] = use_token_type_ids
_UpperCAmelCase : List[Any] = use_labels
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : List[str] = intermediate_multiple_size
_UpperCAmelCase : Optional[int] = hidden_act
_UpperCAmelCase : Optional[int] = hidden_dropout
_UpperCAmelCase : Dict = attention_dropout
_UpperCAmelCase : List[str] = weight_tying
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : Optional[int] = type_vocab_size
_UpperCAmelCase : Tuple = type_sequence_label_size
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : Dict = num_choices
_UpperCAmelCase : List[str] = scope
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : List[Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Dict = None
if self.use_labels:
_UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : Any = self.get_config()
return config, input_ids, input_mask, token_labels
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = self.prepare_config_and_inputs()
_UpperCAmelCase : str = True
return config, input_ids, input_mask, token_labels
def __lowerCAmelCase ( self , A , A , A ) -> int:
_UpperCAmelCase : Optional[Any] = GPTNeoXJapaneseModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Dict = model(A , attention_mask=A )
_UpperCAmelCase : Union[str, Any] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A ) -> Optional[Any]:
_UpperCAmelCase : List[Any] = True
_UpperCAmelCase : Any = GPTNeoXJapaneseModel(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A ) -> str:
_UpperCAmelCase : str = GPTNeoXJapaneseForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Any = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , A , A , A ) -> str:
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Dict = GPTNeoXJapaneseForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
_UpperCAmelCase : Any = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : int = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : List[Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_UpperCAmelCase : List[str] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([input_mask, next_mask] , dim=-1 )
_UpperCAmelCase : Dict = model(A , attention_mask=A , output_hidden_states=A )
_UpperCAmelCase : Tuple = output_from_no_past['''hidden_states'''][0]
_UpperCAmelCase : Optional[int] = model(
A , attention_mask=A , past_key_values=A , output_hidden_states=A , )['''hidden_states'''][0]
# select random slice
_UpperCAmelCase : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Optional[Any] = 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(A , A , atol=1E-3 ) )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = config_and_inputs
_UpperCAmelCase : int = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( a ,a ,unittest.TestCase ):
'''simple docstring'''
a__ =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
a__ =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
a__ =(
{'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
a__ =False
a__ =False
a__ =False
a__ =False
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : Tuple = GPTNeoXJapaneseModelTester(self )
_UpperCAmelCase : Tuple = ConfigTester(self , config_class=A , hidden_size=3_7 )
def __lowerCAmelCase ( self ) -> str:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(A , A , A )
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(A , A , A )
def __lowerCAmelCase ( self ) -> str:
# This regression test was failing with PyTorch < 1.3
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_decoder()
_UpperCAmelCase : Union[str, Any] = None
self.model_tester.create_and_check_model_as_decoder(A , A , A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(A , A , A )
def __lowerCAmelCase ( self ) -> str:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*A )
@slow
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : List[Any] = '''abeja/gpt-neox-japanese-2.7b'''
_UpperCAmelCase : str = ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']
_UpperCAmelCase : Any = [
'''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
'''100年後に必要とされる会社は、「人」が中心の会社です。''',
'''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
'''国境の長いトンネルを抜けると、そこは雪国だった。''',
'''美味しい日本食といえば、やっぱりお寿司ですよね。''',
]
_UpperCAmelCase : str = GPTNeoXJapaneseTokenizer.from_pretrained(A )
_UpperCAmelCase : Any = GPTNeoXJapaneseForCausalLM.from_pretrained(A )
_UpperCAmelCase : Tuple = []
for prompt in prompts:
_UpperCAmelCase : str = tokenizer(A , return_tensors='''pt''' ).input_ids
_UpperCAmelCase : List[Any] = model.generate(A , max_length=5_0 )
_UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(A , skip_special_tokens=A )
predicted_outputs += generated_string
self.assertListEqual(A , A )
| 263 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase :List[str] = '▁'
_lowerCAmelCase :Tuple = {'vocab_file': 'sentencepiece.bpe.model'}
_lowerCAmelCase :List[Any] = {
'vocab_file': {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
),
}
}
_lowerCAmelCase :Tuple = {
'xlm-roberta-base': 512,
'xlm-roberta-large': 512,
'xlm-roberta-large-finetuned-conll02-dutch': 512,
'xlm-roberta-large-finetuned-conll02-spanish': 512,
'xlm-roberta-large-finetuned-conll03-english': 512,
'xlm-roberta-large-finetuned-conll03-german': 512,
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ =VOCAB_FILES_NAMES
a__ =PRETRAINED_VOCAB_FILES_MAP
a__ =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ =['''input_ids''', '''attention_mask''']
def __init__( self , A , A="<s>" , A="</s>" , A="</s>" , A="<s>" , A="<unk>" , A="<pad>" , A="<mask>" , A = None , **A , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase : Tuple = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token
_UpperCAmelCase : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
_UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
_UpperCAmelCase : List[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_UpperCAmelCase : List[str] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_UpperCAmelCase : Any = 1
_UpperCAmelCase : Optional[Any] = len(self.sp_model ) + self.fairseq_offset
_UpperCAmelCase : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Union[str, Any]:
_UpperCAmelCase : Tuple = self.__dict__.copy()
_UpperCAmelCase : List[str] = None
_UpperCAmelCase : str = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , A ) -> Optional[int]:
_UpperCAmelCase : Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_UpperCAmelCase : Optional[Any] = {}
_UpperCAmelCase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def __lowerCAmelCase ( self , A , A = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase : Any = [self.cls_token_id]
_UpperCAmelCase : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __lowerCAmelCase ( self , A , A = None , A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def __lowerCAmelCase ( self , A , A = None ) -> List[int]:
_UpperCAmelCase : Dict = [self.sep_token_id]
_UpperCAmelCase : List[str] = [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]
@property
def __lowerCAmelCase ( self ) -> Dict:
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Dict = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCAmelCase ( self , A ) -> List[str]:
return self.sp_model.encode(A , out_type=A )
def __lowerCAmelCase ( self , A ) -> Any:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_UpperCAmelCase : Any = self.sp_model.PieceToId(A )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def __lowerCAmelCase ( self , A ) -> int:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def __lowerCAmelCase ( self , A ) -> int:
_UpperCAmelCase : str = ''''''.join(A ).replace(A , ''' ''' ).strip()
return out_string
def __lowerCAmelCase ( self , A , A = None ) -> Tuple[str]:
if not os.path.isdir(A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , '''wb''' ) as fi:
_UpperCAmelCase : str = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 263 | 1 |
"""simple docstring"""
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def _snake_case ( _snake_case : Optional[Any] ):
lowerCAmelCase : Union[str, Any] = botoa.client('''iam''' )
lowerCAmelCase : List[str] = {
"Version": "2012-10-17",
"Statement": [
{"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=_SCREAMING_SNAKE_CASE , AssumeRolePolicyDocument=json.dumps(_SCREAMING_SNAKE_CASE , indent=2 ) )
lowerCAmelCase : Dict = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"sagemaker:*",
"ecr:GetDownloadUrlForLayer",
"ecr:BatchGetImage",
"ecr:BatchCheckLayerAvailability",
"ecr:GetAuthorizationToken",
"cloudwatch:PutMetricData",
"cloudwatch:GetMetricData",
"cloudwatch:GetMetricStatistics",
"cloudwatch:ListMetrics",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:DescribeLogStreams",
"logs:PutLogEvents",
"logs:GetLogEvents",
"s3:CreateBucket",
"s3:ListBucket",
"s3:GetBucketLocation",
"s3:GetObject",
"s3:PutObject",
],
"Resource": "*",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=_SCREAMING_SNAKE_CASE , PolicyName=f'''{role_name}_policy_permission''' , PolicyDocument=json.dumps(_SCREAMING_SNAKE_CASE , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f'''role {role_name} already exists. Using existing one''' )
def _snake_case ( _snake_case : Optional[int] ):
lowerCAmelCase : List[str] = botoa.client('''iam''' )
return iam_client.get_role(RoleName=_SCREAMING_SNAKE_CASE )["Role"]["Arn"]
def _snake_case ( ):
lowerCAmelCase : Optional[Any] = _ask_options(
'''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , _SCREAMING_SNAKE_CASE , )
lowerCAmelCase : List[str] = None
if credentials_configuration == 0:
lowerCAmelCase : Dict = _ask_field('''Enter your AWS Profile name: [default] ''' , default='''default''' )
lowerCAmelCase : Union[str, Any] = aws_profile
else:
print(
'''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
'''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`''' )
lowerCAmelCase : List[str] = _ask_field('''AWS Access Key ID: ''' )
lowerCAmelCase : Optional[int] = aws_access_key_id
lowerCAmelCase : int = _ask_field('''AWS Secret Access Key: ''' )
lowerCAmelCase : int = aws_secret_access_key
lowerCAmelCase : Optional[Any] = _ask_field('''Enter your AWS Region: [us-east-1]''' , default='''us-east-1''' )
lowerCAmelCase : Optional[Any] = aws_region
lowerCAmelCase : Dict = _ask_options(
'''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?''' , ['''Provide IAM Role name''', '''Create new IAM role using credentials'''] , _SCREAMING_SNAKE_CASE , )
if role_management == 0:
lowerCAmelCase : Dict = _ask_field('''Enter your IAM role name: ''' )
else:
lowerCAmelCase : Dict = "accelerate_sagemaker_execution_role"
print(f'''Accelerate will create an iam role "{iam_role_name}" using the provided credentials''' )
_create_iam_role_for_sagemaker(_SCREAMING_SNAKE_CASE )
lowerCAmelCase : List[str] = _ask_field(
'''Do you want to use custom Docker image? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
lowerCAmelCase : Any = None
if is_custom_docker_image:
lowerCAmelCase : Optional[Any] = _ask_field('''Enter your Docker image: ''' , lambda _snake_case : str(_SCREAMING_SNAKE_CASE ).lower() )
lowerCAmelCase : List[str] = _ask_field(
'''Do you want to provide SageMaker input channels with data locations? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
lowerCAmelCase : Any = None
if is_sagemaker_inputs_enabled:
lowerCAmelCase : Tuple = _ask_field(
'''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ''' , lambda _snake_case : str(_SCREAMING_SNAKE_CASE ).lower() , )
lowerCAmelCase : Optional[int] = _ask_field(
'''Do you want to enable SageMaker metrics? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
lowerCAmelCase : List[str] = None
if is_sagemaker_metrics_enabled:
lowerCAmelCase : int = _ask_field(
'''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ''' , lambda _snake_case : str(_SCREAMING_SNAKE_CASE ).lower() , )
lowerCAmelCase : str = _ask_options(
'''What is the distributed mode?''' , ['''No distributed training''', '''Data parallelism'''] , _convert_sagemaker_distributed_mode , )
lowerCAmelCase : Tuple = {}
lowerCAmelCase : Dict = _ask_field(
'''Do you wish to optimize your script with torch dynamo?[yes/NO]:''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
if use_dynamo:
lowerCAmelCase : Any = "dynamo_"
lowerCAmelCase : str = _ask_options(
'''Which dynamo backend would you like to use?''' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
lowerCAmelCase : int = _ask_field(
'''Do you want to customize the defaults sent to torch.compile? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
if use_custom_options:
lowerCAmelCase : int = _ask_options(
'''Which mode do you want to use?''' , _SCREAMING_SNAKE_CASE , lambda _snake_case : TORCH_DYNAMO_MODES[int(_SCREAMING_SNAKE_CASE )] , default='''default''' , )
lowerCAmelCase : Any = _ask_field(
'''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
lowerCAmelCase : Union[str, Any] = _ask_field(
'''Do you want to enable dynamic shape tracing? [yes/NO]: ''' , _convert_yes_no_to_bool , default=_SCREAMING_SNAKE_CASE , error_message='''Please enter yes or no.''' , )
lowerCAmelCase : str = "Which EC2 instance type you want to use for your training?"
if distributed_type != SageMakerDistributedType.NO:
lowerCAmelCase : Tuple = _ask_options(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , lambda _snake_case : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_SCREAMING_SNAKE_CASE )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
lowerCAmelCase : Union[str, Any] = _ask_field(_SCREAMING_SNAKE_CASE , lambda _snake_case : str(_SCREAMING_SNAKE_CASE ).lower() , default='''ml.p3.2xlarge''' )
lowerCAmelCase : List[Any] = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
lowerCAmelCase : Any = _ask_field(
'''How many machines do you want use? [1]: ''' , _SCREAMING_SNAKE_CASE , default=1 , )
lowerCAmelCase : List[Any] = _ask_options(
'''Do you wish to use FP16 or BF16 (mixed precision)?''' , ['''no''', '''fp16''', '''bf16''', '''fp8'''] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.''' )
return SageMakerConfig(
image_uri=_SCREAMING_SNAKE_CASE , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=_SCREAMING_SNAKE_CASE , use_cpu=_SCREAMING_SNAKE_CASE , dynamo_config=_SCREAMING_SNAKE_CASE , eca_instance_type=_SCREAMING_SNAKE_CASE , profile=_SCREAMING_SNAKE_CASE , region=_SCREAMING_SNAKE_CASE , iam_role_name=_SCREAMING_SNAKE_CASE , mixed_precision=_SCREAMING_SNAKE_CASE , num_machines=_SCREAMING_SNAKE_CASE , sagemaker_inputs_file=_SCREAMING_SNAKE_CASE , sagemaker_metrics_file=_SCREAMING_SNAKE_CASE , )
| 351 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case__ : Optional[Any] = logging.getLogger(__name__)
def _snake_case ( _snake_case : str ):
lowerCAmelCase : Tuple = git.Repo(search_parent_directories=_snake_case )
lowerCAmelCase : Optional[int] = {
'''repo_id''': str(_snake_case ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(_snake_case , '''git_log.json''' ) , '''w''' ) as f:
json.dump(_snake_case , _snake_case , indent=4 )
def _snake_case ( _snake_case : Any ):
if params.n_gpu <= 0:
lowerCAmelCase : Dict = 0
lowerCAmelCase : Optional[int] = -1
lowerCAmelCase : Dict = True
lowerCAmelCase : int = False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
lowerCAmelCase : str = int(os.environ['''WORLD_SIZE'''] )
lowerCAmelCase : Optional[int] = int(os.environ['''N_GPU_NODE'''] )
lowerCAmelCase : int = int(os.environ['''RANK'''] )
# number of nodes / node ID
lowerCAmelCase : Dict = params.world_size // params.n_gpu_per_node
lowerCAmelCase : int = params.global_rank // params.n_gpu_per_node
lowerCAmelCase : str = True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
lowerCAmelCase : List[Any] = 1
lowerCAmelCase : List[Any] = 0
lowerCAmelCase : Optional[int] = 0
lowerCAmelCase : Any = 0
lowerCAmelCase : Any = 1
lowerCAmelCase : Any = 1
lowerCAmelCase : Dict = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
lowerCAmelCase : Tuple = params.node_id == 0 and params.local_rank == 0
lowerCAmelCase : List[Any] = params.n_nodes > 1
# summary
lowerCAmelCase : Optional[int] = f'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def _snake_case ( _snake_case : Optional[int] ):
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 314 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase__ :List[Any] = "sshleifer/mar_enro_6_3_student"
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def A__ ( self):
super().setUp()
lowercase = cached_path(
'''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz''' ,extract_compressed_file=A__ ,)
lowercase = f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
@slow
@require_torch_gpu
def A__ ( self):
MarianMTModel.from_pretrained(A__)
@slow
@require_torch_gpu
def A__ ( self):
lowercase = {
'''$MAX_LEN''': 6_4,
'''$BS''': 6_4,
'''$GAS''': 1,
'''$ENRO_DIR''': self.data_dir,
'''facebook/mbart-large-cc25''': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
'''--num_train_epochs 6''': '''--num_train_epochs 1''',
}
# Clean up bash script
lowercase = (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py''')[1].strip()
lowercase = bash_script.replace('''\\\n''' ,'''''').strip().replace('''"$@"''' ,'''''')
for k, v in env_vars_to_replace.items():
lowercase = bash_script.replace(A__ ,str(A__))
lowercase = self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
lowercase = f'\n --output_dir {output_dir}\n --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n --sortish_sampler\n --do_predict\n --gpus 1\n --freeze_encoder\n --n_train 40000\n --n_val 500\n --n_test 500\n --fp16_opt_level O1\n --num_sanity_val_steps 0\n --eval_beams 2\n '.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
lowercase = ['''finetune.py'''] + bash_script.split() + args
with patch.object(A__ ,'''argv''' ,A__):
lowercase = argparse.ArgumentParser()
lowercase = pl.Trainer.add_argparse_args(A__)
lowercase = SummarizationModule.add_model_specific_args(A__ ,os.getcwd())
lowercase = parser.parse_args()
lowercase = main(A__)
# Check metrics
lowercase = load_json(model.metrics_save_path)
lowercase = metrics['''val'''][0]
lowercase = metrics['''val'''][-1]
self.assertEqual(len(metrics['''val''']) ,(args.max_epochs / args.val_check_interval))
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,A__)
self.assertGreater(last_step_stats['''val_avg_gen_time'''] ,0.01)
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['''val_avg_gen_time'''] ,1.0)
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu'''] ,2)
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['''val_avg_bleu'''] ,1_7)
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']) ,1.1)
# check lightning ckpt can be loaded and has a reasonable statedict
lowercase = os.listdir(A__)
lowercase = [x for x in contents if x.endswith('''.ckpt''')][0]
lowercase = os.path.join(args.output_dir ,A__)
lowercase = torch.load(A__ ,map_location='''cpu''')
lowercase = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowercase = {os.path.basename(A__) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test''']) == 1
class lowercase ( SCREAMING_SNAKE_CASE__ ):
@timeout_decorator.timeout(6_0_0)
@slow
@require_torch_gpu
def A__ ( self):
lowercase = f'{self.test_file_dir_str}/test_data/wmt_en_ro'
lowercase = {
'''--fp16_opt_level=O1''': '''''',
'''$MAX_LEN''': 1_2_8,
'''$BS''': 1_6,
'''$GAS''': 1,
'''$ENRO_DIR''': data_dir,
'''$m''': '''sshleifer/student_marian_en_ro_6_1''',
'''val_check_interval=0.25''': '''val_check_interval=1.0''',
}
# Clean up bash script
lowercase = (
(self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py''')[1].strip()
)
lowercase = bash_script.replace('''\\\n''' ,'''''').strip().replace('''"$@"''' ,'''''')
lowercase = bash_script.replace('''--fp16 ''' ,''' ''')
for k, v in env_vars_to_replace.items():
lowercase = bash_script.replace(A__ ,str(A__))
lowercase = self.get_auto_remove_tmp_dir()
lowercase = bash_script.replace('''--fp16''' ,'''''')
lowercase = 6
lowercase = (
['''distillation.py''']
+ bash_script.split()
+ [
f'--output_dir={output_dir}',
'''--gpus=1''',
'''--learning_rate=1e-3''',
f'--num_train_epochs={epochs}',
'''--warmup_steps=10''',
'''--val_check_interval=1.0''',
'''--do_predict''',
]
)
with patch.object(A__ ,'''argv''' ,A__):
lowercase = argparse.ArgumentParser()
lowercase = pl.Trainer.add_argparse_args(A__)
lowercase = SummarizationDistiller.add_model_specific_args(A__ ,os.getcwd())
lowercase = parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
lowercase = distill_main(A__)
# Check metrics
lowercase = load_json(model.metrics_save_path)
lowercase = metrics['''val'''][0]
lowercase = metrics['''val'''][-1]
assert len(metrics['''val''']) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[f'val_avg_{model.val_metric}'] ,A__)
# check lightning ckpt can be loaded and has a reasonable statedict
lowercase = os.listdir(A__)
lowercase = [x for x in contents if x.endswith('''.ckpt''')][0]
lowercase = os.path.join(args.output_dir ,A__)
lowercase = torch.load(A__ ,map_location='''cpu''')
lowercase = '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
lowercase = {os.path.basename(A__) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['''test''']) == 1
| 101 | def lowerCamelCase ( SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1_000 ):
'''simple docstring'''
__UpperCamelCase :Union[str, Any] = 1
__UpperCamelCase :Any = 0
for divide_by_number in range(SCREAMING_SNAKE_CASE , digit + 1 ):
__UpperCamelCase :list[int] = []
__UpperCamelCase :Optional[int] = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Optional[Any] = len(SCREAMING_SNAKE_CASE )
__UpperCamelCase :int = divide_by_number
else:
has_been_divided.append(SCREAMING_SNAKE_CASE )
__UpperCamelCase :Any = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase ( unittest.TestCase , A__ ):
'''simple docstring'''
def snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = load_tool('''text-to-speech''' )
self.tool.setup()
def snake_case_ ( self ) -> Any:
"""simple docstring"""
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
UpperCAmelCase = self.tool('''hey''' )
UpperCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def snake_case_ ( self ) -> int:
"""simple docstring"""
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
UpperCAmelCase = self.tool('''hey''' )
UpperCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 152 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["NllbTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["NllbTokenizerFast"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 152 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}
class lowerCAmelCase_ ( __A ):
__lowerCamelCase : int = "rwkv"
__lowerCamelCase : str = {"max_position_embeddings": "context_length"}
def __init__( self , _lowerCAmelCase=50277 , _lowerCAmelCase=1024 , _lowerCAmelCase=4096 , _lowerCAmelCase=32 , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=1E-5 , _lowerCAmelCase=0 , _lowerCAmelCase=0 , _lowerCAmelCase=6 , _lowerCAmelCase=False , _lowerCAmelCase=True , **_lowerCAmelCase , ) -> Optional[Any]:
_lowerCAmelCase = vocab_size
_lowerCAmelCase = context_length
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = attention_hidden_size if attention_hidden_size is not None else hidden_size
_lowerCAmelCase = intermediate_size if intermediate_size is not None else 4 * hidden_size
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = rescale_every
_lowerCAmelCase = use_cache
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(
tie_word_embeddings=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
| 158 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ : Optional[int] = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = ['YolosFeatureExtractor']
UpperCamelCase__ : int = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344 | 0 |
'''simple docstring'''
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 AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
lowerCAmelCase : str = get_tests_dir('fixtures')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase = mock.Mock()
UpperCamelCase = 500
UpperCamelCase = {}
UpperCamelCase = HTTPError
UpperCamelCase = {}
# Download this model to make sure it's in the cache.
UpperCamelCase = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=A_ ) as mock_head:
UpperCamelCase = ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
UpperCamelCase = ViTImageProcessor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json' )
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
with self.assertRaises(A_ ):
# config is in subfolder, the following should not work without specifying the subfolder
UpperCamelCase = AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants' )
UpperCamelCase = AutoImageProcessor.from_pretrained(
'hf-internal-testing/stable-diffusion-all-variants' , subfolder='feature_extractor' )
self.assertIsNotNone(A_ )
@is_staging_test
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
@classmethod
def UpperCAmelCase_ ( cls )-> List[str]:
'''simple docstring'''
UpperCamelCase = TOKEN
HfFolder.save_token(A_ )
@classmethod
def UpperCAmelCase_ ( cls )-> List[str]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-image-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-image-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-image-processor' )
except HTTPError:
pass
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
UpperCamelCase = ViTImageProcessor.from_pretrained(A_ )
image_processor.push_to_hub('test-image-processor' , use_auth_token=self._token )
UpperCamelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(A_ , getattr(A_ , A_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
A_ , repo_id='test-image-processor' , push_to_hub=A_ , use_auth_token=self._token )
UpperCamelCase = ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor''' )
for k, v in image_processor.__dict__.items():
self.assertEqual(A_ , getattr(A_ , A_ ) )
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = ViTImageProcessor.from_pretrained(A_ )
image_processor.push_to_hub('valid_org/test-image-processor' , use_auth_token=self._token )
UpperCamelCase = ViTImageProcessor.from_pretrained('valid_org/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(A_ , getattr(A_ , A_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-image-processor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
A_ , repo_id='valid_org/test-image-processor-org' , push_to_hub=A_ , use_auth_token=self._token )
UpperCamelCase = ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org' )
for k, v in image_processor.__dict__.items():
self.assertEqual(A_ , getattr(A_ , A_ ) )
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
UpperCamelCase = CustomImageProcessor.from_pretrained(A_ )
image_processor.push_to_hub('test-dynamic-image-processor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} , )
UpperCamelCase = AutoImageProcessor.from_pretrained(
F'''{USER}/test-dynamic-image-processor''' , trust_remote_code=A_ )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , 'CustomImageProcessor' )
| 251 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : str = {'vocab_file': 'sentencepiece.bpe.model'}
lowerCAmelCase : Union[str, Any] = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
}
lowerCAmelCase : List[str] = {
'moussaKam/mbarthez': 10_24,
'moussaKam/barthez': 10_24,
'moussaKam/barthez-orangesum-title': 10_24,
}
lowerCAmelCase : Dict = '▁'
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = ["""input_ids""", """attention_mask"""]
def __init__( self , A_ , A_="<s>" , A_="</s>" , A_="</s>" , A_="<s>" , A_="<unk>" , A_="<pad>" , A_="<mask>" , A_ = None , **A_ , )-> None:
'''simple docstring'''
UpperCamelCase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A_ , eos_token=A_ , unk_token=A_ , sep_token=A_ , cls_token=A_ , pad_token=A_ , mask_token=A_ , sp_model_kwargs=self.sp_model_kwargs , **A_ , )
UpperCamelCase = vocab_file
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A_ ) )
UpperCamelCase = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
UpperCamelCase = len(self.sp_model ) - 1
UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCAmelCase_ ( self , A_ , A_ = None )-> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
UpperCamelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self , A_ , A_ = None , A_ = False )-> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
if token_ids_a is None:
return [1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
def UpperCAmelCase_ ( self , A_ , A_ = None )-> List[int]:
'''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 + sep + token_ids_a + sep ) * [0]
@property
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
return len(self.sp_model )
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase_ ( self , A_ )-> List[str]:
'''simple docstring'''
return self.sp_model.encode(A_ , out_type=A_ )
def UpperCAmelCase_ ( self , A_ )-> Union[str, Any]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase = self.sp_model.PieceToId(A_ )
return spm_id if spm_id else self.unk_token_id
def UpperCAmelCase_ ( self , A_ )-> Any:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(A_ )
def UpperCAmelCase_ ( self , A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = ''
UpperCamelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A_ ) + token
UpperCamelCase = True
UpperCamelCase = []
else:
current_sub_tokens.append(A_ )
UpperCamelCase = False
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def __getstate__( self )-> int:
'''simple docstring'''
UpperCamelCase = self.__dict__.copy()
UpperCamelCase = None
return state
def __setstate__( self , A_ )-> List[Any]:
'''simple docstring'''
UpperCamelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase = {}
UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase_ ( self , A_ , A_ = None )-> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase = os.path.join(
A_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A_ )
elif not os.path.isfile(self.vocab_file ):
with open(A_ , 'wb' ) as fi:
UpperCamelCase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (out_vocab_file,)
| 251 | 1 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ) or not all(
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
lowerCAmelCase = lowerCAmelCase = lowerCAmelCase = numbers[0]
for i in range(1 , len(SCREAMING_SNAKE_CASE ) ):
# update the maximum and minimum subarray products
lowerCAmelCase = numbers[i]
if number < 0:
lowerCAmelCase , lowerCAmelCase = min_till_now, max_till_now
lowerCAmelCase = max(SCREAMING_SNAKE_CASE , max_till_now * number )
lowerCAmelCase = min(SCREAMING_SNAKE_CASE , min_till_now * number )
# update the maximum product found till now
lowerCAmelCase = max(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return max_prod
| 46 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __snake_case :
_a : int
_a : TreeNode | None= None
_a : TreeNode | None= None
lowercase : Dict = namedtuple("""CoinsDistribResult""", """moves excess""")
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> int:
if root is None:
return 0
# Validation
def count_nodes(SCREAMING_SNAKE_CASE__ ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(SCREAMING_SNAKE_CASE__ ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(SCREAMING_SNAKE_CASE__ ) != count_coins(SCREAMING_SNAKE_CASE__ ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(SCREAMING_SNAKE_CASE__ ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase , lowercase : int = get_distrib(node.left )
lowercase , lowercase : List[Any] = get_distrib(node.right )
lowercase : Optional[Any] = 1 - left_distrib_excess
lowercase : Union[str, Any] = 1 - right_distrib_excess
lowercase : List[Any] = (
left_distrib_moves
+ right_distrib_moves
+ abs(SCREAMING_SNAKE_CASE__ )
+ abs(SCREAMING_SNAKE_CASE__ )
)
lowercase : Any = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return get_distrib(SCREAMING_SNAKE_CASE__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 20 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
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 (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase__ :
def __init__( self : int , _a : Union[str, Any] , _a : Any=1_3 , _a : Optional[int]=7 , _a : Any=True , _a : Union[str, Any]=True , _a : int=True , _a : Dict=True , _a : str=9_9 , _a : List[Any]=1_6 , _a : int=3_6 , _a : Tuple=6 , _a : List[str]=6 , _a : Dict=6 , _a : Dict=3_7 , _a : Dict="gelu" , _a : str=0.1 , _a : Optional[Any]=0.1 , _a : int=5_1_2 , _a : Optional[Any]=1_6 , _a : List[Any]=2 , _a : Optional[Any]=0.0_2 , _a : List[str]=3 , _a : Tuple=4 , _a : Optional[int]=None , ):
a__: str =parent
a__: Any =batch_size
a__: int =seq_length
a__: List[str] =is_training
a__: List[str] =use_input_mask
a__: Dict =use_token_type_ids
a__: int =use_labels
a__: List[str] =vocab_size
a__: Optional[Any] =embedding_size
a__: str =hidden_size
a__: str =num_hidden_layers
a__: int =num_hidden_groups
a__: str =num_attention_heads
a__: Dict =intermediate_size
a__: Tuple =hidden_act
a__: str =hidden_dropout_prob
a__: int =attention_probs_dropout_prob
a__: Any =max_position_embeddings
a__: Dict =type_vocab_size
a__: Union[str, Any] =type_sequence_label_size
a__: List[str] =initializer_range
a__: Optional[Any] =num_labels
a__: Dict =num_choices
a__: List[Any] =scope
def _lowerCamelCase ( self : str ):
a__: Dict =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__: Optional[Any] =None
if self.use_input_mask:
a__: List[Any] =random_attention_mask([self.batch_size, self.seq_length] )
a__: Any =None
if self.use_token_type_ids:
a__: List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__: List[str] =None
a__: Dict =None
a__: Any =None
if self.use_labels:
a__: Optional[int] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__: Dict =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__: Union[str, Any] =ids_tensor([self.batch_size] , self.num_choices )
a__: Optional[Any] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self : Any ):
return 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 , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def _lowerCamelCase ( self : Union[str, Any] , _a : Optional[Any] , _a : Union[str, Any] , _a : Optional[Any] , _a : Optional[Any] , _a : Optional[Any] , _a : List[Any] , _a : str ):
a__: List[str] =AlbertModel(config=_a )
model.to(_a )
model.eval()
a__: List[Any] =model(_a , attention_mask=_a , token_type_ids=_a )
a__: Union[str, Any] =model(_a , token_type_ids=_a )
a__: Optional[int] =model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _lowerCamelCase ( self : Union[str, Any] , _a : Optional[Any] , _a : Dict , _a : List[Any] , _a : Optional[int] , _a : Optional[Any] , _a : List[Any] , _a : Optional[int] ):
a__: List[Any] =AlbertForPreTraining(config=_a )
model.to(_a )
model.eval()
a__: Union[str, Any] =model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , sentence_order_label=_a , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def _lowerCamelCase ( self : List[str] , _a : str , _a : Dict , _a : Union[str, Any] , _a : List[str] , _a : List[str] , _a : Any , _a : Optional[int] ):
a__: List[str] =AlbertForMaskedLM(config=_a )
model.to(_a )
model.eval()
a__: Tuple =model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self : Any , _a : List[Any] , _a : Union[str, Any] , _a : Any , _a : Optional[int] , _a : Optional[int] , _a : List[str] , _a : Tuple ):
a__: Tuple =AlbertForQuestionAnswering(config=_a )
model.to(_a )
model.eval()
a__: Optional[int] =model(
_a , attention_mask=_a , token_type_ids=_a , start_positions=_a , end_positions=_a , )
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 _lowerCamelCase ( self : str , _a : List[str] , _a : Tuple , _a : Dict , _a : str , _a : Tuple , _a : Dict , _a : str ):
a__: List[str] =self.num_labels
a__: Optional[int] =AlbertForSequenceClassification(_a )
model.to(_a )
model.eval()
a__: List[Any] =model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self : Tuple , _a : Tuple , _a : Tuple , _a : Any , _a : List[str] , _a : List[str] , _a : List[str] , _a : Any ):
a__: List[Any] =self.num_labels
a__: Optional[Any] =AlbertForTokenClassification(config=_a )
model.to(_a )
model.eval()
a__: Dict =model(_a , attention_mask=_a , token_type_ids=_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self : Tuple , _a : int , _a : Tuple , _a : Tuple , _a : Dict , _a : Optional[int] , _a : Optional[Any] , _a : Tuple ):
a__: Optional[int] =self.num_choices
a__: Any =AlbertForMultipleChoice(config=_a )
model.to(_a )
model.eval()
a__: Optional[int] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__: Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__: Optional[int] =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__: Any =model(
_a , attention_mask=_a , token_type_ids=_a , labels=_a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self : Union[str, Any] ):
a__: Any =self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
): int =config_and_inputs
a__: int ={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( _a , _a , unittest.TestCase ):
_lowerCAmelCase = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowerCAmelCase = (
{
'''feature-extraction''': AlbertModel,
'''fill-mask''': AlbertForMaskedLM,
'''question-answering''': AlbertForQuestionAnswering,
'''text-classification''': AlbertForSequenceClassification,
'''token-classification''': AlbertForTokenClassification,
'''zero-shot''': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowerCAmelCase = True
def _lowerCamelCase ( self : List[Any] , _a : Dict , _a : Union[str, Any] , _a : int=False ):
a__: str =super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class in get_values(_a ):
a__: Optional[int] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_a )
a__: Optional[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_a )
return inputs_dict
def _lowerCamelCase ( self : int ):
a__: Union[str, Any] =AlbertModelTester(self )
a__: Dict =ConfigTester(self , config_class=_a , hidden_size=3_7 )
def _lowerCamelCase ( self : Tuple ):
self.config_tester.run_common_tests()
def _lowerCamelCase ( self : List[Any] ):
a__: List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _lowerCamelCase ( self : Optional[Any] ):
a__: Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_a )
def _lowerCamelCase ( self : Optional[Any] ):
a__: Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_a )
def _lowerCamelCase ( self : Tuple ):
a__: str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_a )
def _lowerCamelCase ( self : str ):
a__: Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_a )
def _lowerCamelCase ( self : Any ):
a__: Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_a )
def _lowerCamelCase ( self : List[Any] ):
a__: Tuple =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a__: Dict =type
self.model_tester.create_and_check_model(*_a )
@slow
def _lowerCamelCase ( self : Optional[int] ):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__: Dict =AlbertModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_torch
class lowerCamelCase__ ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : str ):
a__: Any =AlbertModel.from_pretrained("albert-base-v2" )
a__: Optional[int] =torch.tensor([[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]] )
a__: Union[str, Any] =torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
a__: Union[str, Any] =model(_a , attention_mask=_a )[0]
a__: Optional[Any] =torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , _a )
a__: List[str] =torch.tensor(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _a , atol=1e-4 ) )
| 42 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCAmelCase = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__UpperCAmelCase = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__UpperCAmelCase = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : str ):
a__: int =len([g for position, g in enumerate(__magic_name__ ) if g == main_target[position]] )
return (item, float(__magic_name__ ))
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : str ):
a__: Any =random.randint(0 , len(__magic_name__ ) - 1 )
a__: Tuple =parent_a[:random_slice] + parent_a[random_slice:]
a__: List[str] =parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : list[str] ):
a__: str =list(__magic_name__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
a__: Union[str, Any] =random.choice(__magic_name__ )
return "".join(__magic_name__ )
def __lowerCamelCase ( __magic_name__ : tuple[str, float] , __magic_name__ : list[tuple[str, float]] , __magic_name__ : list[str] , ):
a__: List[Any] =[]
# Generate more children proportionally to the fitness score.
a__: Dict =int(parent_a[1] * 100 ) + 1
a__: Tuple =10 if child_n >= 10 else child_n
for _ in range(__magic_name__ ):
a__: List[str] =population_score[random.randint(0 , __magic_name__ )][0]
a__ , a__: Dict =crossover(parent_a[0] , __magic_name__ )
# Append new string to the population list.
pop.append(mutate(__magic_name__ , __magic_name__ ) )
pop.append(mutate(__magic_name__ , __magic_name__ ) )
return pop
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : list[str] , __magic_name__ : bool = True ):
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
a__: Any =F"{N_POPULATION} must be bigger than {N_SELECTED}"
raise ValueError(__magic_name__ )
# Verify that the target contains no genes besides the ones inside genes variable.
a__: int =sorted({c for c in target if c not in genes} )
if not_in_genes_list:
a__: str =F"{not_in_genes_list} is not in genes list, evolution cannot converge"
raise ValueError(__magic_name__ )
# Generate random starting population.
a__: Tuple =[]
for _ in range(__magic_name__ ):
population.append("".join([random.choice(__magic_name__ ) for i in range(len(__magic_name__ ) )] ) )
# Just some logs to know what the algorithms is doing.
a__ , a__: Any =0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__magic_name__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
a__: Dict =[evaluate(__magic_name__ , __magic_name__ ) for item in population]
# Check if there is a matching evolution.
a__: Any =sorted(__magic_name__ , key=lambda __magic_name__ : x[1] , reverse=__magic_name__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F"\nGeneration: {generation}"
F"\nTotal Population:{total_population}"
F"\nBest score: {population_score[0][1]}"
F"\nBest string: {population_score[0][0]}" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
a__: Optional[int] =population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__magic_name__ )
# Normalize population score to be between 0 and 1.
a__: List[str] =[
(item, score / len(__magic_name__ )) for item, score in population_score
]
# This is selection
for i in range(__magic_name__ ):
population.extend(select(population_score[int(__magic_name__ )] , __magic_name__ , __magic_name__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__magic_name__ ) > N_POPULATION:
break
if __name__ == "__main__":
__UpperCAmelCase = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
__UpperCAmelCase = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = basic(target_str, genes_list)
print(
f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
)
| 42 | 1 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowercase ( __snake_case : Tuple , __snake_case : Dict ):
lowercase_ : int = old_name
if "patch_embed" in old_name:
lowercase_ , lowercase_ , lowercase_ : Optional[int] = old_name.split('''.''' )
if layer == "0":
lowercase_ : Optional[Any] = old_name.replace('''0''' , '''convolution1''' )
elif layer == "1":
lowercase_ : str = old_name.replace('''1''' , '''batchnorm_before''' )
elif layer == "3":
lowercase_ : List[Any] = old_name.replace('''3''' , '''convolution2''' )
else:
lowercase_ : List[Any] = old_name.replace('''4''' , '''batchnorm_after''' )
if "network" in old_name and re.search(r'''\d\.\d''' , __snake_case ):
lowercase_ : List[str] = r'''\b\d{2}\b'''
if bool(re.search(__snake_case , __snake_case ) ):
lowercase_ : Optional[Any] = re.search(r'''\d\.\d\d.''' , __snake_case ).group()
else:
lowercase_ : Dict = re.search(r'''\d\.\d.''' , __snake_case ).group()
if int(match[0] ) < 6:
lowercase_ : int = old_name.replace(__snake_case , '''''' )
lowercase_ : Union[str, Any] = trimmed_name.replace('''network''' , match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
lowercase_ : Any = '''intermediate_stages.''' + trimmed_name
else:
lowercase_ : Optional[Any] = old_name.replace(__snake_case , '''''' )
if int(match[2] ) < num_meta4D_last_stage:
lowercase_ : List[str] = trimmed_name.replace('''network''' , '''meta4D_layers.blocks.''' + match[2] )
else:
lowercase_ : Tuple = str(int(match[2] ) - num_meta4D_last_stage )
lowercase_ : Union[str, Any] = trimmed_name.replace('''network''' , '''meta3D_layers.blocks.''' + layer_index )
if "norm1" in old_name:
lowercase_ : Optional[int] = trimmed_name.replace('''norm1''' , '''layernorm1''' )
elif "norm2" in old_name:
lowercase_ : List[Any] = trimmed_name.replace('''norm2''' , '''layernorm2''' )
elif "fc1" in old_name:
lowercase_ : Union[str, Any] = trimmed_name.replace('''fc1''' , '''linear_in''' )
elif "fc2" in old_name:
lowercase_ : str = trimmed_name.replace('''fc2''' , '''linear_out''' )
lowercase_ : str = '''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(r'''.\d.''' , __snake_case ):
lowercase_ : Union[str, Any] = old_name.replace('''network''' , '''intermediate_stages''' )
if "fc" in new_name:
lowercase_ : Tuple = new_name.replace('''fc''' , '''convolution''' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
lowercase_ : str = new_name.replace('''norm1''' , '''batchnorm_before''' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
lowercase_ : List[Any] = new_name.replace('''norm2''' , '''batchnorm_after''' )
if "proj" in new_name:
lowercase_ : Union[str, Any] = new_name.replace('''proj''' , '''projection''' )
if "dist_head" in new_name:
lowercase_ : List[str] = new_name.replace('''dist_head''' , '''distillation_classifier''' )
elif "head" in new_name:
lowercase_ : List[Any] = new_name.replace('''head''' , '''classifier''' )
elif "patch_embed" in new_name:
lowercase_ : int = '''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
lowercase_ : Dict = new_name.replace('''norm''' , '''layernorm''' )
lowercase_ : Any = '''efficientformer.''' + new_name
else:
lowercase_ : List[Any] = '''efficientformer.encoder.''' + new_name
return new_name
def lowercase ( __snake_case : int , __snake_case : List[Any] ):
for key in checkpoint.copy().keys():
lowercase_ : int = checkpoint.pop(__snake_case )
lowercase_ : int = val
return checkpoint
def lowercase ( ):
lowercase_ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase_ : List[Any] = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return image
def lowercase ( __snake_case : Path , __snake_case : Path , __snake_case : Path , __snake_case : bool ):
lowercase_ : Union[str, Any] = torch.load(__snake_case , map_location='''cpu''' )['''model''']
lowercase_ : str = EfficientFormerConfig.from_json_file(__snake_case )
lowercase_ : Tuple = EfficientFormerForImageClassificationWithTeacher(__snake_case )
lowercase_ : Dict = '''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )
lowercase_ : Optional[Any] = config.depths[-1] - config.num_metaad_blocks + 1
lowercase_ : Optional[int] = convert_torch_checkpoint(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
model.eval()
lowercase_ : Dict = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
lowercase_ : str = prepare_img()
lowercase_ : int = 2_5_6
lowercase_ : str = 2_2_4
lowercase_ : Tuple = EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size} , crop_size={'''height''': crop_size, '''width''': crop_size} , resample=pillow_resamplings['''bicubic'''] , )
lowercase_ : Union[str, Any] = processor(images=__snake_case , return_tensors='''pt''' ).pixel_values
# original processing pipeline
lowercase_ : int = Compose(
[
Resize(__snake_case , interpolation=pillow_resamplings['''bicubic'''] ),
CenterCrop(__snake_case ),
ToTensor(),
Normalize(__snake_case , __snake_case ),
] )
lowercase_ : Dict = image_transforms(__snake_case ).unsqueeze(0 )
assert torch.allclose(__snake_case , __snake_case )
lowercase_ : str = model(__snake_case )
lowercase_ : List[str] = outputs.logits
lowercase_ : Any = (1, 1_0_0_0)
if "l1" in model_name:
lowercase_ : Any = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :1_0] , __snake_case , atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
lowercase_ : Dict = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :1_0] , __snake_case , atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
lowercase_ : Optional[Any] = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F'''Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7''' )
# Save Checkpoints
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
processor.save_pretrained(__snake_case )
print(F'''Processor successfuly saved at {pytorch_dump_path}''' )
if push_to_hub:
print('''Pushing model to the hub...''' )
model.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message='''Add model''' , use_temp_dir=__snake_case , )
processor.push_to_hub(
repo_id=F'''Bearnardd/{pytorch_dump_path}''' , commit_message='''Add image processor''' , use_temp_dir=__snake_case , )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''',
default=None,
type=str,
required=True,
help='''Path to EfficientFormer pytorch checkpoint.''',
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for EfficientFormer model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
parser.set_defaults(push_to_hub=True)
__A : int = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 33 |
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float) -> tuple[float, float]:
'''simple docstring'''
if not all(isinstance(_lowerCamelCase , (float, int)) for val in locals().values()):
__UpperCamelCase : str = F'Input values must either be float or int: {list(locals().values())}'
raise TypeError(_lowerCamelCase)
__UpperCamelCase : List[str] = ((x * distance) / (z + distance)) * scale
__UpperCamelCase : List[Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : str , _lowerCamelCase : float) -> tuple[float, float, float]:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase):
raise TypeError("Axis must be a str")
__UpperCamelCase : str = locals()
del input_variables["axis"]
if not all(isinstance(_lowerCamelCase , (float, int)) for val in input_variables.values()):
__UpperCamelCase : Dict = (
"Input values except axis must either be float or int: "
F'{list(input_variables.values())}'
)
raise TypeError(_lowerCamelCase)
__UpperCamelCase : Optional[Any] = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
__UpperCamelCase : Tuple = x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
__UpperCamelCase : Union[str, Any] = y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z
elif axis == "x":
__UpperCamelCase : Dict = y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
__UpperCamelCase : List[str] = x
elif axis == "y":
__UpperCamelCase : Any = x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Dict = y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }") | 232 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : List[Any] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( A_ , unittest.TestCase ):
SCREAMING_SNAKE_CASE = XLNetTokenizer
SCREAMING_SNAKE_CASE = XLNetTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
def __magic_name__ ( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__a =XLNetTokenizer(snake_case__ , keep_accents=snake_case__ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a ="<s>"
__a =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def __magic_name__ ( self ) -> Tuple:
'''simple docstring'''
__a =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '<eod>' )
self.assertEqual(len(snake_case__ ) , 1006 )
def __magic_name__ ( self ) -> Any:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a =XLNetTokenizer(snake_case__ , keep_accents=snake_case__ )
__a =tokenizer.tokenize('This is a test' )
self.assertListEqual(snake_case__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [285, 46, 10, 170, 382] )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__a =tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(snake_case__ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
__a =tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =XLNetTokenizer(snake_case__ , do_lower_case=snake_case__ )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + '',
'i',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
'se',
'.',
] , )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['▁he', 'll', 'o'] )
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =XLNetTokenizer(snake_case__ , do_lower_case=snake_case__ )
__a =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
'se',
'.',
] , )
@slow
def __magic_name__ ( self ) -> List[str]:
'''simple docstring'''
__a =XLNetTokenizer.from_pretrained('xlnet-base-cased' )
__a =tokenizer.encode('sequence builders' , add_special_tokens=snake_case__ )
__a =tokenizer.encode('multi-sequence build' , add_special_tokens=snake_case__ )
__a =tokenizer.build_inputs_with_special_tokens(snake_case__ )
__a =tokenizer.build_inputs_with_special_tokens(snake_case__ , snake_case__ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def __magic_name__ ( self ) -> Optional[int]:
'''simple docstring'''
__a ={"input_ids": [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], "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, 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, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case__ , model_name='xlnet-base-cased' , revision='c841166438c31ec7ca9a106dee7bb312b73ae511' , )
| 360 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element in line.split(',' )]
for line in input_file.readlines()
]
__a =len(_snake_case )
__a =len(matrix[0] )
__a =[[-1 for _ in range(_snake_case )] for _ in range(_snake_case )]
for i in range(_snake_case ):
__a =matrix[i][0]
for j in range(1 , _snake_case ):
for i in range(_snake_case ):
__a =minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , _snake_case ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__a =min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 308 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
return default
def lowercase (_lowerCAmelCase , _lowerCAmelCase=False ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return strtobool(_lowerCAmelCase ) == 1 # As its name indicates `strtobool` actually returns an int...
def lowercase (_lowerCAmelCase , _lowerCAmelCase="no" ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return value
| 301 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace 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 warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class _SCREAMING_SNAKE_CASE ( __a ):
def __init__( self : Union[str, Any] , a__ : Any ):
__magic_name__ = data
def __iter__( self : List[str] ):
for element in self.data:
yield element
def UpperCamelCase ( a=True ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ = Accelerator(even_batches=a )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def UpperCamelCase ( a , a , a , a = False ) -> int:
'''simple docstring'''
if iterable:
__magic_name__ = DummyIterableDataset(torch.as_tensor(range(a ) ) )
else:
__magic_name__ = TensorDataset(torch.as_tensor(range(a ) ) )
__magic_name__ = DataLoader(a , batch_size=a )
__magic_name__ = accelerator.prepare(a )
return dl
def UpperCamelCase ( a , a , a , a , a , ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ = create_dataloader(accelerator=a , dataset_size=a , batch_size=a )
__magic_name__ = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
a , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
a , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
__magic_name__ = create_accelerator(even_batches=a )
verify_dataloader_batch_sizes(
a , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
a , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def UpperCamelCase ( ) -> str:
'''simple docstring'''
__magic_name__ = create_accelerator(even_batches=a )
__magic_name__ = torch.nn.Linear(1 , 1 )
__magic_name__ = accelerator.prepare(a )
__magic_name__ = create_dataloader(a , dataset_size=3 , batch_size=1 )
__magic_name__ = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(a ):
__magic_name__ = ddp_model(batch[0].float() )
__magic_name__ = output.sum()
loss.backward()
batch_idxs.append(a )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def UpperCamelCase ( a ) -> List[Any]:
'''simple docstring'''
with warnings.catch_warnings(record=a ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , a )
assert "only supported for multi-GPU" in str(w[-1].message )
def UpperCamelCase ( ) -> int:
'''simple docstring'''
__magic_name__ = True
__magic_name__ = False
__magic_name__ = create_accelerator(even_batches=a )
__magic_name__ = torch.nn.Linear(1 , 1 )
__magic_name__ = accelerator.prepare(a )
__magic_name__ = create_dataloader(a , dataset_size=3 , batch_size=1 )
__magic_name__ = create_dataloader(a , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=a ):
__magic_name__ = train_dl.batch_sampler.even_batches
__magic_name__ = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
__magic_name__ = True
__magic_name__ = False
__magic_name__ = create_accelerator(even_batches=a )
__magic_name__ = torch.nn.Linear(1 , 1 )
__magic_name__ = accelerator.prepare(a )
create_dataloader(a , dataset_size=3 , batch_size=1 , iterable=a )
__magic_name__ = create_dataloader(a , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings('''ignore''' )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=a ):
__magic_name__ = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
__magic_name__ = create_accelerator()
__magic_name__ = torch.nn.Linear(1 , 1 )
__magic_name__ = accelerator.prepare(a )
create_dataloader(a , dataset_size=3 , batch_size=1 , iterable=a )
with warnings.catch_warnings(record=a ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=a ):
pass
assert issubclass(w[-1].category , a )
assert "only supported for map-style datasets" in str(w[-1].message )
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
__magic_name__ = create_accelerator()
accelerator.print('''Test that even_batches variable ensures uniform batches across processes''' )
test_default_ensures_even_batch_sizes()
accelerator.print('''Run tests with even_batches disabled''' )
test_can_disable_even_batches()
accelerator.print('''Test joining uneven inputs''' )
test_can_join_uneven_inputs()
accelerator.print('''Test overriding even_batches when joining uneven inputs''' )
test_join_can_override_even_batches()
accelerator.print('''Test overriding even_batches for mixed dataloader types''' )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders''' )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print('''Test join with non DDP distributed raises warning''' )
__magic_name__ = accelerator.state.distributed_type
__magic_name__ = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(a )
__magic_name__ = original_state
if __name__ == "__main__":
main()
| 98 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
_lowerCAmelCase = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_lowerCAmelCase = 10
_lowerCAmelCase = 256
def UpperCamelCase ( a ) -> Optional[MinHash]:
'''simple docstring'''
if len(a ) < MIN_NUM_TOKENS:
return None
__magic_name__ = MinHash(num_perm=a )
for token in set(a ):
min_hash.update(token.encode() )
return min_hash
def UpperCamelCase ( a ) -> Set[str]:
'''simple docstring'''
return {t for t in NON_ALPHA.split(a ) if len(t.strip() ) > 0}
class _SCREAMING_SNAKE_CASE :
def __init__( self : Any , *,
a__ : float = 0.85 , ):
__magic_name__ = duplication_jaccard_threshold
__magic_name__ = NUM_PERM
__magic_name__ = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
__magic_name__ = defaultdict(a__ )
def snake_case__ ( self : int , a__ : Tuple , a__ : MinHash ):
__magic_name__ = self._index.query(a__ )
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''' )
return
self._index.insert(a__ , a__ )
if len(a__ ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(a__ )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(a__ )
def snake_case__ ( self : Optional[int] ):
__magic_name__ = []
for base, duplicates in self._duplicate_clusters.items():
__magic_name__ = [base] + list(a__ )
# reformat the cluster to be a list of dict
__magic_name__ = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(a__ )
return duplicate_clusters
def snake_case__ ( self : int , a__ : Tuple ):
__magic_name__ = self.get_duplicate_clusters()
with open(a__ , '''w''' ) as f:
json.dump(a__ , a__ )
def UpperCamelCase ( a ) -> List[Any]:
'''simple docstring'''
__magic_name__ , __magic_name__ = element
__magic_name__ = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def UpperCamelCase ( a ) -> List[Any]:
'''simple docstring'''
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(a , max_queue_size=1_0000 ) , chunksize=100 , ):
if data is not None:
yield data
def UpperCamelCase ( a , a ) -> Tuple:
'''simple docstring'''
__magic_name__ = DuplicationIndex(duplication_jaccard_threshold=a )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a ) ) , max_queue_size=100 ) ):
di.add(a , a )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def UpperCamelCase ( a , a ) -> float:
'''simple docstring'''
__magic_name__ = get_tokens(a )
__magic_name__ = get_tokens(a )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
_lowerCAmelCase = None
def UpperCamelCase ( a , a ) -> Dict:
'''simple docstring'''
__magic_name__ = []
for elementa in cluster:
__magic_name__ = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
__magic_name__ = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(a , a ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
__magic_name__ = 1
extremes.append(a )
return extremes
def UpperCamelCase ( a , a , a ) -> Optional[Any]:
'''simple docstring'''
global _shared_dataset
__magic_name__ = dataset
__magic_name__ = []
__magic_name__ = partial(_find_cluster_extremes_shared , jaccard_threshold=a )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
a , a , ) , total=len(a ) , ):
extremes_list.append(a )
return extremes_list
def UpperCamelCase ( a , a = 0.85 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
'''simple docstring'''
__magic_name__ = make_duplicate_clusters(a , a )
__magic_name__ = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
__magic_name__ = {}
__magic_name__ = find_extremes(a , a , a )
for extremes in extremes_clusters:
for element in extremes:
__magic_name__ = element
__magic_name__ = duplicate_indices - set(extreme_dict.keys() )
__magic_name__ = dataset.filter(lambda a , a : idx not in remove_indices , with_indices=a )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
__magic_name__ = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
__magic_name__ = extreme_dict[element['''base_index''']]['''copies''']
print(F'''Original dataset size: {len(a )}''' )
print(F'''Number of duplicate clusters: {len(a )}''' )
print(F'''Files in duplicate cluster: {len(a )}''' )
print(F'''Unique files in duplicate cluster: {len(a )}''' )
print(F'''Filtered dataset size: {len(a )}''' )
return ds_filter, duplicate_clusters
| 98 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
_a = logging.getLogger()
_a = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
os.makedirs(UpperCAmelCase , exist_ok=UpperCAmelCase )
_UpperCAmelCase = {'source': 'What is love ?', 'target': 'life'}
_UpperCAmelCase = {'train': 12, 'val': 2, 'test': 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
_UpperCAmelCase = '\n'.join([contents[field]] * n_lines[split] )
with open(os.path.join(UpperCAmelCase , F"""{split}.{field}""" ) , 'w' ) as f:
f.write(UpperCAmelCase )
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase = "pytorch" ):
"""simple docstring"""
_UpperCAmelCase = self.get_auto_remove_tmp_dir()
_UpperCAmelCase = os.path.join(UpperCAmelCase , 'output' )
_UpperCAmelCase = os.path.join(UpperCAmelCase , 'data' )
self._create_dummy_data(data_dir=UpperCAmelCase )
_UpperCAmelCase = F"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(F"""--gpus={gpus}""" )
if is_apex_available():
testargs.append('--fp16' )
else:
testargs.append('--gpus=0' )
testargs.append('--distributed_backend=ddp_cpu' )
testargs.append('--num_processes=2' )
_UpperCAmelCase = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(UpperCAmelCase , env=self.get_env() )
_UpperCAmelCase = os.path.join(UpperCAmelCase , 'metrics.json' )
with open(UpperCAmelCase ) as f:
_UpperCAmelCase = json.load(UpperCAmelCase )
return result
@require_torch_gpu
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_gpu
@require_ray
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
@require_ray
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
| 39 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 201 | 0 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowerCAmelCase : Dict = {
'''vocab_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
},
'''merges_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''Salesforce/codegen-350M-mono''': (
'''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
),
},
}
_lowerCAmelCase : Tuple = {
'''Salesforce/codegen-350M-mono''': 2_048,
}
class __magic_name__ ( _a ):
"""simple docstring"""
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["""input_ids""", """attention_mask"""]
__UpperCamelCase = CodeGenTokenizer
def __init__( self :Optional[int] , snake_case :Dict=None , snake_case :Dict=None , snake_case :Dict=None , snake_case :str="<|endoftext|>" , snake_case :Dict="<|endoftext|>" , snake_case :Optional[int]="<|endoftext|>" , snake_case :List[Any]=False , **snake_case :Optional[int] , ):
'''simple docstring'''
super().__init__(
__lowerCAmelCase , __lowerCAmelCase , tokenizer_file=__lowerCAmelCase , unk_token=__lowerCAmelCase , bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , add_prefix_space=__lowerCAmelCase , **__lowerCAmelCase , )
if kwargs.pop("add_bos_token" , __lowerCAmelCase ):
A_ : Tuple = kwargs.pop("name_or_path" , "" )
raise ValueError(
"Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
"Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
"This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
" so that the fast tokenizer works correctly." )
A_ : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , __lowerCAmelCase ) != add_prefix_space:
A_ : List[str] = getattr(__lowerCAmelCase , pre_tok_state.pop("type" ) )
A_ : Optional[int] = add_prefix_space
A_ : str = pre_tok_class(**__lowerCAmelCase )
A_ : Tuple = add_prefix_space
def SCREAMING_SNAKE_CASE ( self :Optional[int] , *snake_case :Dict , **snake_case :Any ):
'''simple docstring'''
A_ : Dict = kwargs.get("is_split_into_words" , __lowerCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__lowerCAmelCase , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] , *snake_case :Tuple , **snake_case :Tuple ):
'''simple docstring'''
A_ : Union[str, Any] = kwargs.get("is_split_into_words" , __lowerCAmelCase )
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__lowerCAmelCase , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self :str , snake_case :str , snake_case :Optional[int] = None ):
'''simple docstring'''
A_ : Optional[Any] = self._tokenizer.model.save(__lowerCAmelCase , name=__lowerCAmelCase )
return tuple(__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] , snake_case :List[str] , snake_case :List[Any] = False , snake_case :Any = None , snake_case :str = None , **snake_case :Optional[Any] , ):
'''simple docstring'''
A_ : str = super().decode(
token_ids=__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase , **__lowerCAmelCase , )
if truncate_before_pattern is not None and len(__lowerCAmelCase ) > 0:
A_ : int = self.truncate(__lowerCAmelCase , __lowerCAmelCase )
return decoded_text
def SCREAMING_SNAKE_CASE ( self :str , snake_case :Optional[Any] , snake_case :List[Any] ):
'''simple docstring'''
def find_re(snake_case :int , snake_case :Dict , snake_case :Optional[Any] ):
A_ : str = pattern.search(__lowerCAmelCase , __lowerCAmelCase )
return m.start() if m else -1
A_ : List[str] = [re.compile(__lowerCAmelCase , re.MULTILINE ) for pattern in truncate_before_pattern]
A_ : Optional[int] = list(re.finditer("^print" , __lowerCAmelCase , re.MULTILINE ) )
if len(__lowerCAmelCase ) > 1:
A_ : Union[str, Any] = completion[: prints[1].start()]
A_ : int = list(re.finditer("^def" , __lowerCAmelCase , re.MULTILINE ) )
if len(__lowerCAmelCase ) > 1:
A_ : List[Any] = completion[: defs[1].start()]
A_ : Dict = 0
A_ : Any = [
pos for pos in [find_re(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) for terminal in terminals] if pos != -1
]
if len(__lowerCAmelCase ) > 0:
return completion[: min(__lowerCAmelCase )]
else:
return completion
| 370 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowerCAmelCase : Any = (3, 9, -11, 0, 7, 5, 1, -1)
_lowerCAmelCase : Any = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_name__ :
"""simple docstring"""
__UpperCamelCase = 42
__UpperCamelCase = 42
class __magic_name__ :
"""simple docstring"""
def __init__( self :str , snake_case :Iterable[int] ):
'''simple docstring'''
A_ : Node | None = None
for i in sorted(snake_case , reverse=snake_case ):
A_ : str = Node(snake_case , self.head )
def __iter__( self :Any ):
'''simple docstring'''
A_ : List[Any] = self.head
while node:
yield node.data
A_ : Optional[int] = node.next_node
def __len__( self :Tuple ):
'''simple docstring'''
return sum(1 for _ in self )
def __str__( self :Tuple ):
'''simple docstring'''
return " -> ".join([str(snake_case ) for node in self] )
def __snake_case ( _lowerCAmelCase : SortedLinkedList , _lowerCAmelCase : SortedLinkedList ) -> SortedLinkedList:
return SortedLinkedList(list(_lowerCAmelCase ) + list(_lowerCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : int = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 70 | 0 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class __UpperCAmelCase :
def __init__( self ):
"""simple docstring"""
_snake_case = {}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 ):
"""simple docstring"""
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
_snake_case = [[w, v]]
if not self.graph.get(SCREAMING_SNAKE_CASE__ ):
_snake_case = []
def lowerCamelCase ( self ):
"""simple docstring"""
return list(self.graph )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(SCREAMING_SNAKE_CASE__ )
def lowerCamelCase ( self , lowerCAmelCase_=-2 , lowerCAmelCase_=-1 ):
"""simple docstring"""
if s == d:
return []
_snake_case = []
_snake_case = []
if s == -2:
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(SCREAMING_SNAKE_CASE__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return visited
def lowerCamelCase ( self , lowerCAmelCase_=-1 ):
"""simple docstring"""
if c == -1:
_snake_case = floor(random() * 1_00_00 ) + 10
for i in range(SCREAMING_SNAKE_CASE__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
_snake_case = floor(random() * c ) + 1
if n != i:
self.add_pair(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
def lowerCamelCase ( self , lowerCAmelCase_=-2 ):
"""simple docstring"""
_snake_case = deque()
_snake_case = []
if s == -2:
_snake_case = list(self.graph )[0]
d.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
while d:
_snake_case = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return len(self.graph[u] )
def lowerCamelCase ( self , lowerCAmelCase_=-2 ):
"""simple docstring"""
_snake_case = []
_snake_case = []
if s == -2:
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
_snake_case = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return sorted_nodes
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = []
_snake_case = []
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = -2
_snake_case = []
_snake_case = s
_snake_case = False
_snake_case = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_snake_case = len(SCREAMING_SNAKE_CASE__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_snake_case = True
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = False
indirect_parents.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return list(SCREAMING_SNAKE_CASE__ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = []
_snake_case = []
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = -2
_snake_case = []
_snake_case = s
_snake_case = False
_snake_case = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_snake_case = len(SCREAMING_SNAKE_CASE__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_snake_case = True
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = False
indirect_parents.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return False
def lowerCamelCase ( self , lowerCAmelCase_=-2 , lowerCAmelCase_=-1 ):
"""simple docstring"""
_snake_case = time()
self.dfs(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_snake_case = time()
return end - begin
def lowerCamelCase ( self , lowerCAmelCase_=-2 ):
"""simple docstring"""
_snake_case = time()
self.bfs(SCREAMING_SNAKE_CASE__ )
_snake_case = time()
return end - begin
class __UpperCAmelCase :
def __init__( self ):
"""simple docstring"""
_snake_case = {}
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=1 ):
"""simple docstring"""
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
_snake_case = [[w, v]]
# add the other way
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
_snake_case = [[w, u]]
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(SCREAMING_SNAKE_CASE__ )
# the other way round
if self.graph.get(SCREAMING_SNAKE_CASE__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(SCREAMING_SNAKE_CASE__ )
def lowerCamelCase ( self , lowerCAmelCase_=-2 , lowerCAmelCase_=-1 ):
"""simple docstring"""
if s == d:
return []
_snake_case = []
_snake_case = []
if s == -2:
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(SCREAMING_SNAKE_CASE__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return visited
def lowerCamelCase ( self , lowerCAmelCase_=-1 ):
"""simple docstring"""
if c == -1:
_snake_case = floor(random() * 1_00_00 ) + 10
for i in range(SCREAMING_SNAKE_CASE__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 1_02 ) + 1 ):
_snake_case = floor(random() * c ) + 1
if n != i:
self.add_pair(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 1 )
def lowerCamelCase ( self , lowerCAmelCase_=-2 ):
"""simple docstring"""
_snake_case = deque()
_snake_case = []
if s == -2:
_snake_case = list(self.graph )[0]
d.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
while d:
_snake_case = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
return len(self.graph[u] )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = []
_snake_case = []
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = -2
_snake_case = []
_snake_case = s
_snake_case = False
_snake_case = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_snake_case = len(SCREAMING_SNAKE_CASE__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_snake_case = True
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = False
indirect_parents.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return list(SCREAMING_SNAKE_CASE__ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = []
_snake_case = []
_snake_case = list(self.graph )[0]
stack.append(SCREAMING_SNAKE_CASE__ )
visited.append(SCREAMING_SNAKE_CASE__ )
_snake_case = -2
_snake_case = []
_snake_case = s
_snake_case = False
_snake_case = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
_snake_case = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
_snake_case = len(SCREAMING_SNAKE_CASE__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
_snake_case = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
_snake_case = True
if len(SCREAMING_SNAKE_CASE__ ) != 0:
_snake_case = stack[len(SCREAMING_SNAKE_CASE__ ) - 1]
else:
_snake_case = False
indirect_parents.append(SCREAMING_SNAKE_CASE__ )
_snake_case = s
_snake_case = ss
# check if se have reached the starting point
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return False
def lowerCamelCase ( self ):
"""simple docstring"""
return list(self.graph )
def lowerCamelCase ( self , lowerCAmelCase_=-2 , lowerCAmelCase_=-1 ):
"""simple docstring"""
_snake_case = time()
self.dfs(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_snake_case = time()
return end - begin
def lowerCamelCase ( self , lowerCAmelCase_=-2 ):
"""simple docstring"""
_snake_case = time()
self.bfs(SCREAMING_SNAKE_CASE__ )
_snake_case = time()
return end - begin
| 42 |
"""simple docstring"""
from typing import Any
def __lowerCamelCase ( a_ : list ) -> list[Any]:
if not input_list:
return []
__SCREAMING_SNAKE_CASE :int = [input_list.count(a_ ) for value in input_list]
__SCREAMING_SNAKE_CASE :str = max(a_ ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(a_ ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod() | 191 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__UpperCamelCase = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
"processing_trocr": ["TrOCRProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase = [
"TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrOCRForCausalLM",
"TrOCRPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 13 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> List[Any]:
"""simple docstring"""
for attribute in key.split(""".""" ):
__snake_case : Optional[int] = getattr(_lowerCamelCase , _lowerCamelCase )
if weight_type is not None:
__snake_case : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase ).shape
else:
__snake_case : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
__snake_case : Union[str, Any] = value
elif weight_type == "weight_g":
__snake_case : str = value
elif weight_type == "weight_v":
__snake_case : Tuple = value
elif weight_type == "bias":
__snake_case : str = value
else:
__snake_case : List[Any] = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Optional[int]:
"""simple docstring"""
__snake_case : Tuple = []
__snake_case : List[Any] = fairseq_model.state_dict()
__snake_case : int = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__snake_case : Any = False
if "conv_layers" in name:
load_conv_layer(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
__snake_case : Optional[int] = True
else:
for key, mapped_key in MAPPING.items():
__snake_case : Optional[Any] = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__snake_case : Dict = True
if "*" in mapped_key:
__snake_case : List[Any] = name.split(_lowerCamelCase )[0].split(""".""" )[-2]
__snake_case : Optional[int] = mapped_key.replace("""*""" , _lowerCamelCase )
if "weight_g" in name:
__snake_case : Dict = """weight_g"""
elif "weight_v" in name:
__snake_case : List[str] = """weight_v"""
elif "weight" in name:
__snake_case : str = """weight"""
elif "bias" in name:
__snake_case : int = """bias"""
else:
__snake_case : int = None
set_recursively(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
continue
if not is_used:
unused_weights.append(_lowerCamelCase )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Any:
"""simple docstring"""
__snake_case : Dict = full_name.split("""conv_layers.""" )[-1]
__snake_case : Optional[int] = name.split(""".""" )
__snake_case : Dict = int(items[0] )
__snake_case : Optional[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
__snake_case : Union[str, Any] = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
__snake_case : int = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
__snake_case : str = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
__snake_case : List[Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_lowerCamelCase )
def _a ( _lowerCamelCase , _lowerCamelCase ) -> Tuple:
"""simple docstring"""
__snake_case : List[str] = SEWConfig()
if is_finetuned:
__snake_case : List[Any] = model.wav_encoder.wav_model.cfg
else:
__snake_case : Optional[Any] = model.cfg
__snake_case : Tuple = fs_config.conv_bias
__snake_case : List[Any] = eval(fs_config.conv_feature_layers )
__snake_case : List[Any] = [x[0] for x in conv_layers]
__snake_case : Dict = [x[1] for x in conv_layers]
__snake_case : Tuple = [x[2] for x in conv_layers]
__snake_case : List[str] = """gelu"""
__snake_case : Dict = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
__snake_case : Optional[int] = 0.0
__snake_case : Optional[Any] = fs_config.activation_fn.name
__snake_case : Dict = fs_config.encoder_embed_dim
__snake_case : Dict = 0.02
__snake_case : Any = fs_config.encoder_ffn_embed_dim
__snake_case : Tuple = 1E-5
__snake_case : Dict = fs_config.encoder_layerdrop
__snake_case : Any = fs_config.encoder_attention_heads
__snake_case : int = fs_config.conv_pos_groups
__snake_case : Tuple = fs_config.conv_pos
__snake_case : Optional[int] = len(_lowerCamelCase )
__snake_case : int = fs_config.encoder_layers
__snake_case : Optional[int] = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
__snake_case : Union[str, Any] = model.cfg
__snake_case : Tuple = fs_config.final_dropout
__snake_case : Tuple = fs_config.layerdrop
__snake_case : Any = fs_config.activation_dropout
__snake_case : int = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
__snake_case : Tuple = fs_config.attention_dropout
__snake_case : List[Any] = fs_config.dropout_input
__snake_case : Optional[Any] = fs_config.dropout
__snake_case : str = fs_config.mask_channel_length
__snake_case : Any = fs_config.mask_channel_prob
__snake_case : int = fs_config.mask_length
__snake_case : str = fs_config.mask_prob
__snake_case : str = """Wav2Vec2FeatureExtractor"""
__snake_case : Dict = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def _a ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=True ) -> int:
"""simple docstring"""
if is_finetuned:
__snake_case , __snake_case , __snake_case : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
__snake_case , __snake_case , __snake_case : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
__snake_case : Optional[Any] = SEWConfig.from_pretrained(_lowerCamelCase )
else:
__snake_case : int = convert_config(model[0] , _lowerCamelCase )
__snake_case : Dict = model[0].eval()
__snake_case : Optional[Any] = True if config.feat_extract_norm == """layer""" else False
__snake_case : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=_lowerCamelCase , return_attention_mask=_lowerCamelCase , )
if is_finetuned:
if dict_path:
__snake_case : str = Dictionary.load(_lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__snake_case : Union[str, Any] = target_dict.pad_index
__snake_case : Optional[Any] = target_dict.bos_index
__snake_case : Tuple = target_dict.pad_index
__snake_case : List[str] = target_dict.bos_index
__snake_case : Optional[Any] = target_dict.eos_index
__snake_case : List[str] = len(target_dict.symbols )
__snake_case : Optional[Any] = os.path.join(_lowerCamelCase , """vocab.json""" )
if not os.path.isdir(_lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCamelCase ) )
return
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
with open(_lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , _lowerCamelCase )
__snake_case : List[Any] = WavaVecaCTCTokenizer(
_lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=_lowerCamelCase , )
__snake_case : Optional[int] = WavaVecaProcessor(feature_extractor=_lowerCamelCase , tokenizer=_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
__snake_case : List[str] = SEWForCTC(_lowerCamelCase )
else:
__snake_case : List[str] = SEWModel(_lowerCamelCase )
feature_extractor.save_pretrained(_lowerCamelCase )
recursively_load_weights(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
hf_model.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__UpperCamelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 13 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=[1, 2, 1] , _SCREAMING_SNAKE_CASE=[2, 2, 4] , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=2.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"] , _SCREAMING_SNAKE_CASE=[1, 2, 3] , ):
__lowerCAmelCase : List[str] = parent
__lowerCAmelCase : Tuple = batch_size
__lowerCAmelCase : str = image_size
__lowerCAmelCase : Any = patch_size
__lowerCAmelCase : str = num_channels
__lowerCAmelCase : str = embed_dim
__lowerCAmelCase : Optional[Any] = depths
__lowerCAmelCase : Tuple = num_heads
__lowerCAmelCase : int = window_size
__lowerCAmelCase : List[str] = mlp_ratio
__lowerCAmelCase : Tuple = qkv_bias
__lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
__lowerCAmelCase : List[str] = attention_probs_dropout_prob
__lowerCAmelCase : str = drop_path_rate
__lowerCAmelCase : Any = hidden_act
__lowerCAmelCase : Any = use_absolute_embeddings
__lowerCAmelCase : Dict = patch_norm
__lowerCAmelCase : int = layer_norm_eps
__lowerCAmelCase : Any = initializer_range
__lowerCAmelCase : Tuple = is_training
__lowerCAmelCase : Tuple = scope
__lowerCAmelCase : Dict = use_labels
__lowerCAmelCase : Tuple = type_sequence_label_size
__lowerCAmelCase : List[str] = encoder_stride
__lowerCAmelCase : int = out_features
__lowerCAmelCase : List[str] = out_indices
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase : List[Any] = None
if self.use_labels:
__lowerCAmelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase : str = self.get_config()
return config, pixel_values, labels
def __lowerCamelCase ( self ):
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = MaskFormerSwinModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Tuple = model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowerCAmelCase : Union[str, Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[Any] = ['stem']
__lowerCAmelCase : Any = MaskFormerSwinBackbone(config=_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[str] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = config_and_inputs
__lowerCAmelCase : Dict = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A__ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase):
A_ : List[Any] = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
A_ : Union[str, Any] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
A_ : str = False
A_ : Union[str, Any] = False
A_ : Optional[Any] = False
A_ : Optional[int] = False
A_ : Optional[int] = False
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = MaskFormerSwinModelTester(self )
__lowerCAmelCase : Optional[int] = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
'`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
' `nn.DataParallel`'
) )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCamelCase ( self ):
return
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE )
@unittest.skip('Swin does not use inputs_embeds' )
def __lowerCamelCase ( self ):
pass
@unittest.skip('Swin does not support feedforward chunking' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase : List[str] = model_class(_SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowerCAmelCase : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear ) )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
__lowerCAmelCase : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions' )
def __lowerCamelCase ( self ):
pass
@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__lowerCAmelCase : str = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Dict = outputs.hidden_states
__lowerCAmelCase : str = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
# Swin has a different seq_length
__lowerCAmelCase : Dict = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowerCAmelCase : List[str] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Union[str, Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowerCAmelCase : List[str] = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase : Optional[Any] = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Optional[Any] = 3
__lowerCAmelCase : Union[str, Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowerCAmelCase : str = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowerCAmelCase : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowerCAmelCase : Optional[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowerCAmelCase : Optional[Any] = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase : Any = True
self.check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints' )
def __lowerCamelCase ( self ):
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def __lowerCamelCase ( self ):
pass
@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin' )
def __lowerCamelCase ( self ):
pass
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = 0
return t
def check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE={} ):
with torch.no_grad():
__lowerCAmelCase : str = model(**_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = model(**_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ).to_tuple()
def recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if isinstance(_SCREAMING_SNAKE_CASE , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ) , set_nan_tensor_to_zero(_SCREAMING_SNAKE_CASE ) , atol=1E-5 ) , msg=(
'Tuple and dict output are not equal. Difference:'
f" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"
f" {torch.isnan(_SCREAMING_SNAKE_CASE ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE )}. Dict has"
f" `nan`: {torch.isnan(_SCREAMING_SNAKE_CASE ).any()} and `inf`: {torch.isinf(_SCREAMING_SNAKE_CASE )}."
) , )
recursive_check(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__lowerCAmelCase : int = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
__lowerCAmelCase : Dict = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , {'output_hidden_states': True} )
__lowerCAmelCase : Optional[int] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , return_labels=_SCREAMING_SNAKE_CASE )
check_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , {'output_hidden_states': True} )
@require_torch
class A__ ( unittest.TestCase , _lowerCamelCase):
A_ : Tuple = (MaskFormerSwinBackbone,) if is_torch_available() else ()
A_ : Union[str, Any] = MaskFormerSwinConfig
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = MaskFormerSwinModelTester(self )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Any = inputs_dict['pixel_values'].shape[0]
for backbone_class in self.all_model_classes:
__lowerCAmelCase : int = backbone_class(_SCREAMING_SNAKE_CASE )
backbone.to(_SCREAMING_SNAKE_CASE )
backbone.eval()
__lowerCAmelCase : Union[str, Any] = backbone(**_SCREAMING_SNAKE_CASE )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _SCREAMING_SNAKE_CASE )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowerCAmelCase : Tuple = backbone(**_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Optional[int] = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowerCAmelCase : str = backbone(**_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(outputs.attentions ) | 86 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=14 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=5_12 , _SCREAMING_SNAKE_CASE=0.02 , ):
__lowerCAmelCase : Union[str, Any] = parent
__lowerCAmelCase : Any = batch_size
__lowerCAmelCase : Any = seq_length
__lowerCAmelCase : Optional[Any] = is_training
__lowerCAmelCase : Any = use_input_mask
__lowerCAmelCase : Any = use_token_type_ids
__lowerCAmelCase : Tuple = use_labels
__lowerCAmelCase : Optional[Any] = vocab_size
__lowerCAmelCase : Tuple = hidden_size
__lowerCAmelCase : str = rotary_dim
__lowerCAmelCase : Union[str, Any] = num_hidden_layers
__lowerCAmelCase : Union[str, Any] = num_attention_heads
__lowerCAmelCase : int = intermediate_size
__lowerCAmelCase : List[str] = hidden_act
__lowerCAmelCase : int = hidden_dropout_prob
__lowerCAmelCase : Any = attention_probs_dropout_prob
__lowerCAmelCase : List[Any] = max_position_embeddings
__lowerCAmelCase : Optional[Any] = initializer_range
__lowerCAmelCase : Tuple = None
__lowerCAmelCase : int = vocab_size - 1
__lowerCAmelCase : Dict = vocab_size - 1
__lowerCAmelCase : int = vocab_size - 1
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : List[str] = None
if self.use_input_mask:
__lowerCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Optional[int] = GPTJConfig(
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 , use_cache=_SCREAMING_SNAKE_CASE , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def __lowerCamelCase ( self ):
__lowerCAmelCase : Optional[int] = self.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Any = config_and_inputs
__lowerCAmelCase : Dict = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : List[str] = 20
__lowerCAmelCase : List[str] = model_class_name(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = model.init_cache(input_ids.shape[0] , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
__lowerCAmelCase : Optional[Any] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCAmelCase : Any = model(
input_ids[:, :-1] , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , position_ids=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Any = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCAmelCase : int = model(
input_ids[:, -1:] , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , position_ids=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"Max diff is {diff}" )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = 20
__lowerCAmelCase : List[str] = model_class_name(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
__lowerCAmelCase : List[str] = model.init_cache(input_ids.shape[0] , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
__lowerCAmelCase : Optional[Any] = model(
input_ids[:, :-1] , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , position_ids=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : str = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
__lowerCAmelCase : Tuple = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=_SCREAMING_SNAKE_CASE , position_ids=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"Max diff is {diff}" )
@require_flax
class A__ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase):
A_ : Tuple = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
A_ : str = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = FlaxGPTJModelTester(self )
def __lowerCamelCase ( self ):
for model_class_name in self.all_model_classes:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
for model_class_name in self.all_model_classes:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@tooslow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
__lowerCAmelCase : Optional[int] = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCAmelCase : Any = False
__lowerCAmelCase : Any = model.config.eos_token_id
__lowerCAmelCase : Union[str, Any] = jax.jit(model.generate )
__lowerCAmelCase : Optional[Any] = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
__lowerCAmelCase : str = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@is_pt_flax_cross_test
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCAmelCase : str = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCAmelCase : Dict = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCAmelCase : Optional[int] = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase : List[Any] = pt_inputs['input_ids'].shape
__lowerCAmelCase : int = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase : Tuple = 1
__lowerCAmelCase : List[str] = 0
__lowerCAmelCase : Any = 1
__lowerCAmelCase : Optional[Any] = pt_model_class(_SCREAMING_SNAKE_CASE ).eval()
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE , dtype=jnp.floataa )
__lowerCAmelCase : int = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = fx_state
with torch.no_grad():
__lowerCAmelCase : Union[str, Any] = pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
__lowerCAmelCase : str = fx_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = fx_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(
len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
__lowerCAmelCase : List[str] = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
__lowerCAmelCase : Dict = model_class.__name__[4:] # Skip the "Flax" at the beginning
__lowerCAmelCase : str = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = pt_model_class(_SCREAMING_SNAKE_CASE ).eval()
__lowerCAmelCase : Tuple = model_class(_SCREAMING_SNAKE_CASE , dtype=jnp.floataa )
__lowerCAmelCase : List[str] = load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE , fx_model.params )
__lowerCAmelCase , __lowerCAmelCase : int = pt_inputs['input_ids'].shape
__lowerCAmelCase : List[str] = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = 0
__lowerCAmelCase : Optional[Any] = 1
__lowerCAmelCase : Optional[int] = 0
__lowerCAmelCase : Optional[Any] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
__lowerCAmelCase : List[str] = pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
__lowerCAmelCase : Optional[int] = fx_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = pt_model_class.from_pretrained(_SCREAMING_SNAKE_CASE , from_flax=_SCREAMING_SNAKE_CASE )
with torch.no_grad():
__lowerCAmelCase : Any = pt_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(
len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def __lowerCamelCase ( self ):
for model_class_name in self.all_model_classes:
__lowerCAmelCase : Optional[int] = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
__lowerCAmelCase : List[Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE ) | 86 | 1 |
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 _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :int = ["""image_processor""", """tokenizer"""]
__SCREAMING_SNAKE_CASE :List[str] = """BridgeTowerImageProcessor"""
__SCREAMING_SNAKE_CASE :Optional[Any] = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self : Dict , a__ : Optional[int] , a__ : Tuple ):
super().__init__(a__ , a__ )
def __call__( self : Tuple , a__ : Dict , a__ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , a__ : bool = True , a__ : Union[bool, str, PaddingStrategy] = False , a__ : Union[bool, str, TruncationStrategy] = None , a__ : Optional[int] = None , a__ : int = 0 , a__ : Optional[int] = None , a__ : Optional[bool] = None , a__ : Optional[bool] = None , a__ : bool = False , a__ : bool = False , a__ : bool = False , a__ : bool = False , a__ : bool = True , a__ : Optional[Union[str, TensorType]] = None , **a__ : Tuple , ):
__magic_name__ = self.tokenizer(
text=a__ , add_special_tokens=a__ , padding=a__ , truncation=a__ , max_length=a__ , stride=a__ , pad_to_multiple_of=a__ , return_token_type_ids=a__ , return_attention_mask=a__ , return_overflowing_tokens=a__ , return_special_tokens_mask=a__ , return_offsets_mapping=a__ , return_length=a__ , verbose=a__ , return_tensors=a__ , **a__ , )
# add pixel_values + pixel_mask
__magic_name__ = self.image_processor(
a__ , return_tensors=a__ , do_normalize=a__ , do_center_crop=a__ , **a__ )
encoding.update(a__ )
return encoding
def snake_case__ ( self : Dict , *a__ : Optional[int] , **a__ : Union[str, Any] ):
return self.tokenizer.batch_decode(*a__ , **a__ )
def snake_case__ ( self : Dict , *a__ : Tuple , **a__ : int ):
return self.tokenizer.decode(*a__ , **a__ )
@property
def snake_case__ ( self : int ):
__magic_name__ = self.tokenizer.model_input_names
__magic_name__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object()
def UpperCamelCase ( a , a ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ = tuple((re.compile(x + '''$''' ) for x in qs) )
for i in range(len(a ) - len(a ) + 1 ):
__magic_name__ = [x.match(a ) for x, y in zip(a , ks[i:] )]
if matches and all(a ):
return True
return False
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
def replace(a , a ):
for rule, replacement in rules:
if _match(a , a ):
return replacement
return val
return replace
def UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
return [
# embeddings
(("transformer", "wpe", "embedding"), P('''mp''' , a )),
(("transformer", "wte", "embedding"), P('''mp''' , a )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a , '''mp''' )),
(("attention", "out_proj", "kernel"), P('''mp''' , a )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(a , '''mp''' )),
(("mlp", "c_fc", "bias"), P('''mp''' )),
(("mlp", "c_proj", "kernel"), P('''mp''' , a )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def UpperCamelCase ( a ) -> str:
'''simple docstring'''
__magic_name__ = _get_partition_rules()
__magic_name__ = _replacement_rules(a )
__magic_name__ = {k: _unmatched for k in flatten_dict(a )}
__magic_name__ = {k: replace(a , a ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(a ) )
| 98 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : int = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a ='gpt_bigcode'
__a =['past_key_values']
__a ={
'hidden_size': 'n_embd',
'max_position_embeddings': 'n_positions',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[Any] , __a : Tuple=5_02_57 , __a : str=10_24 , __a : Dict=7_68 , __a : Tuple=12 , __a : str=12 , __a : Optional[int]=None , __a : Dict="gelu_pytorch_tanh" , __a : Tuple=0.1 , __a : Tuple=0.1 , __a : Union[str, Any]=0.1 , __a : Tuple=1e-5 , __a : str=0.02 , __a : Dict=True , __a : Union[str, Any]=True , __a : Optional[int]=5_02_56 , __a : Optional[int]=5_02_56 , __a : Union[str, Any]=True , __a : Dict=True , __a : Union[str, Any]=True , **__a : List[Any] , ):
_a = vocab_size
_a = n_positions
_a = n_embd
_a = n_layer
_a = n_head
_a = n_inner
_a = activation_function
_a = resid_pdrop
_a = embd_pdrop
_a = attn_pdrop
_a = layer_norm_epsilon
_a = initializer_range
_a = scale_attn_weights
_a = use_cache
_a = attention_softmax_in_fpaa
_a = scale_attention_softmax_in_fpaa
_a = multi_query
_a = bos_token_id
_a = eos_token_id
super().__init__(bos_token_id=__a , eos_token_id=__a , **__a )
| 63 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
UpperCamelCase_ = 'src/diffusers'
UpperCamelCase_ = '.'
# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase_ = importlib.util.spec_from_file_location(
'diffusers',
os.path.join(DIFFUSERS_PATH, '__init__.py'),
submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCamelCase_ = spec.loader.load_module()
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
return line.startswith(UpperCAmelCase ) or len(UpperCAmelCase ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$" , UpperCAmelCase ) is not None
def UpperCamelCase ( UpperCAmelCase ) ->Any:
"""simple docstring"""
a_ = object_name.split("." )
a_ = 0
# First let's find the module where our object lives.
a_ = parts[i]
while i < len(UpperCAmelCase ) and not os.path.isfile(os.path.join(UpperCAmelCase , F'''{module}.py''' ) ):
i += 1
if i < len(UpperCAmelCase ):
a_ = os.path.join(UpperCAmelCase , parts[i] )
if i >= len(UpperCAmelCase ):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''' )
with open(os.path.join(UpperCAmelCase , F'''{module}.py''' ) , "r" , encoding="utf-8" , newline="\n" ) as f:
a_ = f.readlines()
# Now let's find the class / func in the code!
a_ = ""
a_ = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCAmelCase ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(UpperCAmelCase ):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
a_ = line_index
while line_index < len(UpperCAmelCase ) and _should_continue(lines[line_index] , UpperCAmelCase ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a_ = lines[start_index:line_index]
return "".join(UpperCAmelCase )
UpperCamelCase_ = re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase_ = re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase_ = re.compile(R'<FILL\s+[^>]*>')
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = code.split("\n" )
a_ = 0
while idx < len(UpperCAmelCase ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCAmelCase ):
return re.search(r"^(\s*)\S" , lines[idx] ).groups()[0]
return ""
def UpperCamelCase ( UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = len(get_indent(UpperCAmelCase ) ) > 0
if has_indent:
a_ = F'''class Bla:\n{code}'''
a_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=UpperCAmelCase )
a_ = black.format_str(UpperCAmelCase , mode=UpperCAmelCase )
a_ , a_ = style_docstrings_in_code(UpperCAmelCase )
return result[len("class Bla:\n" ) :] if has_indent else result
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=False ) ->str:
"""simple docstring"""
with open(UpperCAmelCase , "r" , encoding="utf-8" , newline="\n" ) as f:
a_ = f.readlines()
a_ = []
a_ = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCAmelCase ):
a_ = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
a_ , a_ , a_ = search.groups()
a_ = find_code_in_diffusers(UpperCAmelCase )
a_ = get_indent(UpperCAmelCase )
a_ = line_index + 1 if indent == theoretical_indent else line_index + 2
a_ = theoretical_indent
a_ = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
a_ = True
while line_index < len(UpperCAmelCase ) and should_continue:
line_index += 1
if line_index >= len(UpperCAmelCase ):
break
a_ = lines[line_index]
a_ = _should_continue(UpperCAmelCase , UpperCAmelCase ) and re.search(F'''^{indent}# End copy''' , UpperCAmelCase ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
a_ = lines[start_index:line_index]
a_ = "".join(UpperCAmelCase )
# Remove any nested `Copied from` comments to avoid circular copies
a_ = [line for line in theoretical_code.split("\n" ) if _re_copy_warning.search(UpperCAmelCase ) is None]
a_ = "\n".join(UpperCAmelCase )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCAmelCase ) > 0:
a_ = replace_pattern.replace("with" , "" ).split("," )
a_ = [_re_replace_pattern.search(UpperCAmelCase ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
a_ , a_ , a_ = pattern.groups()
a_ = re.sub(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
if option.strip() == "all-casing":
a_ = re.sub(obja.lower() , obja.lower() , UpperCAmelCase )
a_ = re.sub(obja.upper() , obja.upper() , UpperCAmelCase )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
a_ = blackify(lines[start_index - 1] + theoretical_code )
a_ = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
a_ = lines[:start_index] + [theoretical_code] + lines[line_index:]
a_ = start_index + 1
if overwrite and len(UpperCAmelCase ) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''' )
with open(UpperCAmelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(UpperCAmelCase )
return diffs
def UpperCamelCase ( UpperCAmelCase = False ) ->int:
"""simple docstring"""
a_ = glob.glob(os.path.join(UpperCAmelCase , "**/*.py" ) , recursive=UpperCAmelCase )
a_ = []
for filename in all_files:
a_ = is_copy_consistent(UpperCAmelCase , UpperCAmelCase )
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(UpperCAmelCase ) > 0:
a_ = "\n".join(UpperCAmelCase )
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
UpperCamelCase_ = parser.parse_args()
check_copies(args.fix_and_overwrite) | 243 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__magic_name__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 152 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__magic_name__ = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
"ErnieForCausalLM",
"ErnieForMaskedLM",
"ErnieForMultipleChoice",
"ErnieForNextSentencePrediction",
"ErnieForPreTraining",
"ErnieForQuestionAnswering",
"ErnieForSequenceClassification",
"ErnieForTokenClassification",
"ErnieModel",
"ErniePreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 152 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.