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'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ("""foo.json""",)] )
def snake_case_( self , A ) -> Dict:
_SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=A , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(A , config_name=A )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(A , config_name=A )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , A )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , A )
def snake_case_( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""gpt2""" )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_model_config(A )
_SCREAMING_SNAKE_CASE = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(A , A )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def snake_case_( self ) -> Dict:
_SCREAMING_SNAKE_CASE = GenerationConfig()
_SCREAMING_SNAKE_CASE = {
"""max_new_tokens""": 1024,
"""foo""": """bar""",
}
_SCREAMING_SNAKE_CASE = copy.deepcopy(A )
_SCREAMING_SNAKE_CASE = generation_config.update(**A )
# update_kwargs was not modified (no side effects)
self.assertEqual(A , A )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(A , {"""foo""": """bar"""} )
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = GenerationConfig()
_SCREAMING_SNAKE_CASE = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(A )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(A )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , """bar""" )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_model_config(A )
assert not hasattr(A , """foo""" ) # no new kwargs should be initialized if from config
def snake_case_( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , A )
self.assertEqual(default_config.num_beams , 1 )
_SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=A , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , A )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(A )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(A , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , A )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class a_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def snake_case_( cls ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(A )
@classmethod
def snake_case_( cls ) -> Optional[Any]:
try:
delete_repo(token=cls._token , repo_id="""test-generation-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-generation-config-org""" )
except HTTPError:
pass
def snake_case_( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=A , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""test-generation-config""" , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A , getattr(A , A ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
A , repo_id="""test-generation-config""" , push_to_hub=A , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A , getattr(A , A ) )
def snake_case_( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = GenerationConfig(
do_sample=A , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("""valid_org/test-generation-config-org""" , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A , getattr(A , A ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
A , repo_id="""valid_org/test-generation-config-org""" , push_to_hub=A , use_auth_token=self._token )
_SCREAMING_SNAKE_CASE = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(A , getattr(A , A ) )
| 58 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ :Optional[Any] = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :str = ["ReformerTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :int = ["ReformerTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ :List[str] = [
"REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"ReformerAttention",
"ReformerForMaskedLM",
"ReformerForQuestionAnswering",
"ReformerForSequenceClassification",
"ReformerLayer",
"ReformerModel",
"ReformerModelWithLMHead",
"ReformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
a_ :Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 277 | 0 |
from __future__ import annotations
from math import pow, sqrt
def __UpperCamelCase ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance == 0:
return {"resistance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(lowercase__ , 2 ) + pow(lowercase__ , 2 ) )}
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 |
from __future__ import annotations
from typing import Any
class __a :
def __init__( self : Dict , UpperCAmelCase : int = 6 ):
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
self.create_linked_list(UpperCAmelCase )
def A ( self : Union[str, Any] , UpperCAmelCase : int ):
lowerCAmelCase_ : Any = Node()
lowerCAmelCase_ : int = current_node
lowerCAmelCase_ : str = current_node
lowerCAmelCase_ : Union[str, Any] = current_node
for _ in range(1 , UpperCAmelCase ):
lowerCAmelCase_ : Any = Node()
lowerCAmelCase_ : Dict = current_node
lowerCAmelCase_ : Optional[int] = previous_node
lowerCAmelCase_ : Optional[Any] = current_node
lowerCAmelCase_ : List[str] = self.front
lowerCAmelCase_ : Optional[int] = previous_node
def A ( self : Any ):
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def A ( self : List[str] ):
self.check_can_perform_operation()
return self.front.data if self.front else None
def A ( self : Optional[int] , UpperCAmelCase : Any ):
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
lowerCAmelCase_ : int = self.rear.next
if self.rear:
lowerCAmelCase_ : Union[str, Any] = data
def A ( self : List[Any] ):
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
lowerCAmelCase_ : int = self.front.data
lowerCAmelCase_ : Optional[Any] = None
return data
lowerCAmelCase_ : Optional[int] = self.front
lowerCAmelCase_ : Any = old_front.next
lowerCAmelCase_ : Tuple = old_front.data
lowerCAmelCase_ : str = None
return data
def A ( self : Tuple ):
if self.is_empty():
raise Exception("""Empty Queue""" )
def A ( self : List[str] ):
if self.rear and self.rear.next == self.front:
raise Exception("""Full Queue""" )
class __a :
def __init__( self : Any ):
lowerCAmelCase_ : Any | None = None
lowerCAmelCase_ : Node | None = None
lowerCAmelCase_ : Node | None = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : list[list[int]] = [[0 for _ in range(__UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__lowercase : List[Any] = 1
for n in range(m + 1 ):
for k in range(1 , __UpperCamelCase ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
a_ = int(input('Enter a number: ').strip())
print(partition(n))
except ValueError:
print('Please enter a number.')
else:
try:
a_ = int(sys.argv[1])
print(partition(n))
except ValueError:
print('Please pass a number.')
| 249 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
a_ = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class UpperCAmelCase_ ( unittest.TestCase , snake_case ):
def _lowerCamelCase ( self ) -> int:
__lowercase : List[Any] = load_tool('''text-question-answering''' )
self.tool.setup()
__lowercase : Any = load_tool('''text-question-answering''' , remote=UpperCamelCase_ )
def _lowerCamelCase ( self ) -> Union[str, Any]:
__lowercase : Dict = self.tool(UpperCamelCase_ , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(UpperCamelCase_ , '''launched the BigScience Research Workshop''' )
def _lowerCamelCase ( self ) -> List[str]:
__lowercase : Optional[int] = self.remote_tool(UpperCamelCase_ , '''What did Hugging Face do in April 2021?''' )
self.assertEqual(UpperCamelCase_ , '''launched the BigScience Research Workshop''' )
def _lowerCamelCase ( self ) -> Any:
__lowercase : Union[str, Any] = self.tool(text=UpperCamelCase_ , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(UpperCamelCase_ , '''launched the BigScience Research Workshop''' )
def _lowerCamelCase ( self ) -> Optional[Any]:
__lowercase : Optional[int] = self.remote_tool(text=UpperCamelCase_ , question='''What did Hugging Face do in April 2021?''' )
self.assertEqual(UpperCamelCase_ , '''launched the BigScience Research Workshop''' )
| 249 | 1 |
import baseaa
def UpperCAmelCase ( a_ ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def UpperCAmelCase ( a_ ) -> str:
"""simple docstring"""
return baseaa.baadecode(a_ ).decode("utf-8" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = 'Hello World!'
SCREAMING_SNAKE_CASE :Optional[int] = baseaa_encode(test)
print(encoded)
SCREAMING_SNAKE_CASE :str = baseaa_decode(encoded)
print(decoded)
| 367 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = CTRLTokenizer
snake_case_ = False
snake_case_ = False
def UpperCamelCase_ ( self : Dict ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__A = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
__A = dict(zip(A ,range(len(A ) ) ) )
__A = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
__A = {"unk_token": "<unk>"}
__A = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
__A = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(A ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(A ) )
def UpperCamelCase_ ( self : List[str] ,**A : List[str] ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**A )
def UpperCamelCase_ ( self : Optional[int] ,A : Tuple ):
__A = "adapt react readapt apt"
__A = "adapt react readapt apt"
return input_text, output_text
def UpperCamelCase_ ( self : Any ):
__A = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
__A = "adapt react readapt apt"
__A = "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
__A = tokenizer.tokenize(A )
self.assertListEqual(A ,A )
__A = tokens + [tokenizer.unk_token]
__A = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) ,A )
| 124 | 0 |
'''simple docstring'''
__lowerCamelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
__lowerCamelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__lowerCamelCase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 162 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A__ ( _snake_case ):
lowercase = "roc_bert"
def __init__( self , UpperCamelCase__=30522 , 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__=True , UpperCamelCase__=0 , UpperCamelCase__="absolute" , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=768 , UpperCamelCase__=910 , UpperCamelCase__=512 , UpperCamelCase__=24858 , UpperCamelCase__=True , **UpperCamelCase__ , ) -> Tuple:
'''simple docstring'''
A_ = vocab_size
A_ = max_position_embeddings
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_ = initializer_range
A_ = type_vocab_size
A_ = layer_norm_eps
A_ = use_cache
A_ = enable_pronunciation
A_ = enable_shape
A_ = pronunciation_embed_dim
A_ = pronunciation_vocab_size
A_ = shape_embed_dim
A_ = shape_vocab_size
A_ = concat_input
A_ = position_embedding_type
A_ = classifier_dropout
super().__init__(pad_token_id=UpperCamelCase__ , **UpperCamelCase__ )
| 162 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : int ) -> List[str]:
'''simple docstring'''
def is_in_circle(_UpperCamelCase : float , _UpperCamelCase : float ) -> bool:
UpperCamelCase__ = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
UpperCamelCase__ = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(_UpperCamelCase ) )
# The ratio of the area for circle to square is pi/4.
UpperCamelCase__ = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : int , _UpperCamelCase : Callable[[float], float] , _UpperCamelCase : float = 0.0 , _UpperCamelCase : float = 1.0 , ) -> float:
'''simple docstring'''
return mean(
function_to_integrate(uniform(_UpperCamelCase , _UpperCamelCase ) ) for _ in range(_UpperCamelCase ) ) * (max_value - min_value)
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : int , _UpperCamelCase : float = 0.0 , _UpperCamelCase : float = 1.0 ) -> None:
'''simple docstring'''
def identity_function(_UpperCamelCase : float ) -> float:
return x
UpperCamelCase__ = area_under_curve_estimator(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
UpperCamelCase__ = (max_value * max_value - min_value * min_value) / 2
print("******************" )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print("******************" )
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : int ) -> None:
'''simple docstring'''
def function_to_integrate(_UpperCamelCase : float ) -> float:
return sqrt(4.0 - x * x )
UpperCamelCase__ = area_under_curve_estimator(
_UpperCamelCase , _UpperCamelCase , 0.0 , 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod() | 31 |
'''simple docstring'''
import math
import sys
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) -> str:
'''simple docstring'''
UpperCamelCase__ = ""
try:
with open(_UpperCamelCase , "rb" ) as binary_file:
UpperCamelCase__ = binary_file.read()
for dat in data:
UpperCamelCase__ = F'{dat:08b}'
result += curr_byte
return result
except OSError:
print("File not accessible" )
sys.exit()
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) -> str:
'''simple docstring'''
UpperCamelCase__ = {"0": "0", "1": "1"}
UpperCamelCase__ , UpperCamelCase__ = "", ""
UpperCamelCase__ = len(_UpperCamelCase )
for i in range(len(_UpperCamelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase__ = lexicon[curr_string]
result += last_match_id
UpperCamelCase__ = last_match_id + "0"
if math.loga(_UpperCamelCase ).is_integer():
UpperCamelCase__ = {}
for curr_key in list(_UpperCamelCase ):
UpperCamelCase__ = lexicon.pop(_UpperCamelCase )
UpperCamelCase__ = new_lex
UpperCamelCase__ = last_match_id + "1"
index += 1
UpperCamelCase__ = ""
return result
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str , _UpperCamelCase : str ) -> None:
'''simple docstring'''
UpperCamelCase__ = 8
try:
with open(_UpperCamelCase , "wb" ) as opened_file:
UpperCamelCase__ = [
to_write[i : i + byte_length]
for i in range(0 , len(_UpperCamelCase ) , _UpperCamelCase )
]
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(_UpperCamelCase , 2 ).to_bytes(1 , byteorder="big" ) )
except OSError:
print("File not accessible" )
sys.exit()
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str ) -> str:
'''simple docstring'''
UpperCamelCase__ = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
UpperCamelCase__ = data_bits[counter:]
UpperCamelCase__ = data_bits[counter + 1 :]
return data_bits
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str , _UpperCamelCase : str ) -> None:
'''simple docstring'''
UpperCamelCase__ = read_file_binary(_UpperCamelCase )
UpperCamelCase__ = remove_prefix(_UpperCamelCase )
UpperCamelCase__ = decompress_data(_UpperCamelCase )
write_file_binary(_UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2]) | 31 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'lxmert'
lowercase__ = {}
def __init__( self , __a=3_05_22 , __a=7_68 , __a=12 , __a=95_00 , __a=16_00 , __a=4_00 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=2 , __a=0.02 , __a=1e-12 , __a=9 , __a=5 , __a=5 , __a=20_48 , __a=4 , __a=6.67 , __a=True , __a=True , __a=True , __a=True , __a=True , __a=True , __a=True , **__a , ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_act
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = initializer_range
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = num_qa_labels
_UpperCamelCase = num_object_labels
_UpperCamelCase = num_attr_labels
_UpperCamelCase = l_layers
_UpperCamelCase = x_layers
_UpperCamelCase = r_layers
_UpperCamelCase = visual_feat_dim
_UpperCamelCase = visual_pos_dim
_UpperCamelCase = visual_loss_normalizer
_UpperCamelCase = task_matched
_UpperCamelCase = task_mask_lm
_UpperCamelCase = task_obj_predict
_UpperCamelCase = task_qa
_UpperCamelCase = visual_obj_loss
_UpperCamelCase = visual_attr_loss
_UpperCamelCase = visual_feat_loss
_UpperCamelCase = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__a)
| 194 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 194 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
'''processing_vision_text_dual_encoder''': ['''VisionTextDualEncoderProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''VisionTextDualEncoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''FlaxVisionTextDualEncoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''TFVisionTextDualEncoderModel''']
if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 101 |
'''simple docstring'''
import requests
__lowerCamelCase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ ( UpperCAmelCase__ = "Chicago", UpperCAmelCase__ = APPID ) -> dict:
return requests.get(URL_BASE + """weather""", params=locals() ).json()
def UpperCAmelCase__ ( UpperCAmelCase__ = "Kolkata, India", UpperCAmelCase__ = APPID ) -> dict:
return requests.get(URL_BASE + """forecast""", params=locals() ).json()
def UpperCAmelCase__ ( UpperCAmelCase__ = 55.68, UpperCAmelCase__ = 12.57, UpperCAmelCase__ = APPID ) -> dict:
return requests.get(URL_BASE + """onecall""", params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
__lowerCamelCase = input('''Enter a location:''').strip()
if location:
pprint(current_weather(location))
else:
break
| 101 | 1 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowercase_ ( *__UpperCAmelCase ) -> List[str]:
with open(__UpperCAmelCase , """r""" ) as fh:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_EX )
try:
print(*__UpperCAmelCase )
finally:
fcntl.flock(__UpperCAmelCase , fcntl.LOCK_UN )
_A = int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
_A = torch.device("""cuda""", local_rank)
_A = socket.gethostname()
_A = f"""[{hostname}-{local_rank}]"""
try:
# test distributed
dist.init_process_group("""nccl""")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
_A = dist.get_rank()
_A = dist.get_world_size()
printflock(f"""{gpu} is OK (global rank: {rank}/{world_size})""")
dist.barrier()
if rank == 0:
printflock(f"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")
except Exception:
printflock(f"""{gpu} is broken""")
raise
| 242 |
"""simple docstring"""
from itertools import product
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> list[int]:
lowerCAmelCase__ : Union[str, Any] = sides_number
lowerCAmelCase__ : Optional[int] = max_face_number * dice_number
lowerCAmelCase__ : List[str] = [0] * (max_total + 1)
lowerCAmelCase__ : Union[str, Any] = 1
lowerCAmelCase__ : Optional[int] = range(__UpperCAmelCase , max_face_number + 1 )
for dice_numbers in product(__UpperCAmelCase , repeat=__UpperCAmelCase ):
lowerCAmelCase__ : str = sum(__UpperCAmelCase )
totals_frequencies[total] += 1
return totals_frequencies
def lowercase_ ( ) -> float:
lowerCAmelCase__ : Union[str, Any] = total_frequency_distribution(
sides_number=4 , dice_number=9 )
lowerCAmelCase__ : Tuple = total_frequency_distribution(
sides_number=6 , dice_number=6 )
lowerCAmelCase__ : str = 0
lowerCAmelCase__ : int = 9
lowerCAmelCase__ : Tuple = 4 * 9
lowerCAmelCase__ : Optional[int] = 6
for peter_total in range(__UpperCAmelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
lowerCAmelCase__ : Tuple = (4**9) * (6**6)
lowerCAmelCase__ : Union[str, Any] = peter_wins_count / total_games_number
lowerCAmelCase__ : Optional[int] = round(__UpperCAmelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f"""{solution() = }""")
| 242 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase :Union[str, Any] = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase :Tuple = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase :Any = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase :Tuple = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
__UpperCAmelCase :Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 353 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class a :
"""simple docstring"""
SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
SCREAMING_SNAKE_CASE : torch.Tensor # [batch_size x 3]
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : float
SCREAMING_SNAKE_CASE : float
SCREAMING_SNAKE_CASE : Tuple[int]
def lowerCamelCase__ ( self : Any ) -> int:
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def lowerCamelCase__ ( self : Union[str, Any] ) -> str:
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def lowerCamelCase__ ( self : Any ) -> Optional[Any]:
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def lowerCamelCase__ ( self : Any ) -> torch.Tensor:
__UpperCAmelCase : Dict = torch.arange(self.height * self.width )
__UpperCAmelCase : Dict = torch.stack(
[
pixel_indices % self.width,
torch.div(snake_case , self.width , rounding_mode='''trunc''' ),
] , axis=1 , )
return coords
@property
def lowerCamelCase__ ( self : Any ) -> int:
__UpperCAmelCase , *__UpperCAmelCase : str = self.shape
__UpperCAmelCase : Dict = int(np.prod(snake_case ) )
__UpperCAmelCase : Tuple = self.get_image_coords()
__UpperCAmelCase : List[Any] = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
__UpperCAmelCase : Any = self.get_camera_rays(snake_case )
__UpperCAmelCase : List[str] = rays.view(snake_case , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def lowerCamelCase__ ( self : Union[str, Any] , snake_case : torch.Tensor ) -> torch.Tensor:
__UpperCAmelCase , *__UpperCAmelCase , __UpperCAmelCase : List[str] = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
__UpperCAmelCase : List[str] = coords.view(snake_case , -1 , 2 )
__UpperCAmelCase : Optional[Any] = self.resolution()
__UpperCAmelCase : Tuple = self.fov()
__UpperCAmelCase : Optional[int] = (flat.float() / (res - 1)) * 2 - 1
__UpperCAmelCase : Union[str, Any] = fracs * torch.tan(fov / 2 )
__UpperCAmelCase : str = fracs.view(snake_case , -1 , 2 )
__UpperCAmelCase : Any = (
self.z.view(snake_case , 1 , 3 )
+ self.x.view(snake_case , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(snake_case , 1 , 3 ) * fracs[:, :, 1:]
)
__UpperCAmelCase : Union[str, Any] = directions / directions.norm(dim=-1 , keepdim=snake_case )
__UpperCAmelCase : Union[str, Any] = torch.stack(
[
torch.broadcast_to(self.origin.view(snake_case , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(snake_case , *snake_case , 2 , 3 )
def lowerCamelCase__ ( self : Any , snake_case : int , snake_case : int ) -> "DifferentiableProjectiveCamera":
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=snake_case , height=snake_case , x_fov=self.x_fov , y_fov=self.y_fov , )
def _a ( _lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : str = []
__UpperCAmelCase : Optional[int] = []
__UpperCAmelCase : Union[str, Any] = []
__UpperCAmelCase : List[Any] = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
__UpperCAmelCase : Dict = np.array([np.sin(_lowercase ), np.cos(_lowercase ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
__UpperCAmelCase : Any = -z * 4
__UpperCAmelCase : Dict = np.array([np.cos(_lowercase ), -np.sin(_lowercase ), 0.0] )
__UpperCAmelCase : List[str] = np.cross(_lowercase , _lowercase )
origins.append(_lowercase )
xs.append(_lowercase )
ys.append(_lowercase )
zs.append(_lowercase )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(_lowercase , axis=0 ) ).float() , x=torch.from_numpy(np.stack(_lowercase , axis=0 ) ).float() , y=torch.from_numpy(np.stack(_lowercase , axis=0 ) ).float() , z=torch.from_numpy(np.stack(_lowercase , axis=0 ) ).float() , width=_lowercase , height=_lowercase , x_fov=0.7 , y_fov=0.7 , shape=(1, len(_lowercase )) , ) | 240 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self , lowercase) -> Dict:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss']):
a__ : int = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(lowercase)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Tuple = 'sshleifer/tiny-gpt2'
a__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase , multi_process=lowercase , )
a__ : List[Any] = TensorFlowBenchmark(lowercase)
a__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : Optional[Any] = 'sgugger/tiny-distilbert-classification'
a__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , only_pretrain_model=lowercase , )
a__ : str = TensorFlowBenchmark(lowercase)
a__ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
a__ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
a__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : str = 'sshleifer/tiny-gpt2'
a__ : str = AutoConfig.from_pretrained(lowercase)
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase , multi_process=lowercase , )
a__ : Optional[Any] = TensorFlowBenchmark(lowercase , [config])
a__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Dict = 'sshleifer/tiny-gpt2'
a__ : Union[str, Any] = AutoConfig.from_pretrained(lowercase)
a__ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : str = TensorFlowBenchmark(lowercase , [config])
a__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Optional[int] = 'sshleifer/tiny-gpt2'
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : Tuple = TensorFlowBenchmark(lowercase)
a__ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Optional[Any] = 'sshleifer/tiny-gpt2'
a__ : List[Any] = AutoConfig.from_pretrained(lowercase)
a__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : Optional[Any] = TensorFlowBenchmark(lowercase , [config])
a__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Optional[int] = 'patrickvonplaten/t5-tiny-random'
a__ : Optional[int] = AutoConfig.from_pretrained(lowercase)
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : List[Any] = TensorFlowBenchmark(lowercase , configs=[config])
a__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU')) == 0 , 'Cannot do xla on CPU.')
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : Optional[int] = 'sshleifer/tiny-gpt2'
a__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , use_xla=lowercase , multi_process=lowercase , )
a__ : int = TensorFlowBenchmark(lowercase)
a__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : str = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
a__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase , save_to_csv=lowercase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(lowercase , 'inf_time.csv') , inference_memory_csv_file=os.path.join(lowercase , 'inf_mem.csv') , env_info_csv_file=os.path.join(lowercase , 'env.csv') , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
benchmark.run()
self.assertTrue(Path(os.path.join(lowercase , 'inf_time.csv')).exists())
self.assertTrue(Path(os.path.join(lowercase , 'inf_mem.csv')).exists())
self.assertTrue(Path(os.path.join(lowercase , 'env.csv')).exists())
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(lowercase):
self.assertTrue(hasattr(lowercase , 'sequential'))
self.assertTrue(hasattr(lowercase , 'cumulative'))
self.assertTrue(hasattr(lowercase , 'current'))
self.assertTrue(hasattr(lowercase , 'total'))
with tempfile.TemporaryDirectory() as tmp_dir:
a__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(lowercase , 'log.txt') , log_print=lowercase , trace_memory_line_by_line=lowercase , eager_mode=lowercase , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
a__ : Any = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
self.assertTrue(Path(os.path.join(lowercase , 'log.txt')).exists())
| 99 |
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 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class lowercase ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=512 , __a=16 , __a=2 , __a=0.02 , __a=4 , ) -> int:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = batch_size
UpperCAmelCase__ = seq_length
UpperCAmelCase__ = is_training
UpperCAmelCase__ = use_attention_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_choices
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_attention_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__ = RoFormerConfig(
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=__a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = config_and_inputs
UpperCAmelCase__ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowercase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
UpperCAmelCase__ = FlaxRoFormerModelTester(self )
@slow
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
UpperCAmelCase__ = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=__a )
UpperCAmelCase__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
@require_flax
class lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ (self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
UpperCAmelCase__ = jnp.array([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = model(__a )[0]
UpperCAmelCase__ = 50000
UpperCAmelCase__ = (1, 6, vocab_size)
self.assertEqual(output.shape , __a )
UpperCAmelCase__ = jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __a , atol=1E-4 ) )
| 335 |
# flake8: noqa
# Lint as: python3
_UpperCamelCase = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 335 | 1 |
def _SCREAMING_SNAKE_CASE ( a ) -> int:
if not isinstance(a , a ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 280 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
raise Exception('''requires gluonnlp == 0.8.3''')
if version.parse(mx.__version__) != version.parse('''1.5.0'''):
raise Exception('''requires mxnet == 1.5.0''')
logging.set_verbosity_info()
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def _SCREAMING_SNAKE_CASE ( a , a ) -> Optional[Any]:
__A : Any = {
'attention_cell': 'multi_head',
'num_layers': 4,
'units': 10_24,
'hidden_size': 7_68,
'max_length': 5_12,
'num_heads': 8,
'scaled': True,
'dropout': 0.1,
'use_residual': True,
'embed_size': 10_24,
'embed_dropout': 0.1,
'word_embed': None,
'layer_norm_eps': 1e-5,
'token_type_vocab_size': 2,
}
__A : str = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
__A : Optional[int] = BERTEncoder(
attention_cell=predefined_args['attention_cell'] , num_layers=predefined_args['num_layers'] , units=predefined_args['units'] , hidden_size=predefined_args['hidden_size'] , max_length=predefined_args['max_length'] , num_heads=predefined_args['num_heads'] , scaled=predefined_args['scaled'] , dropout=predefined_args['dropout'] , output_attention=a , output_all_encodings=a , use_residual=predefined_args['use_residual'] , activation=predefined_args.get('activation' , 'gelu' ) , layer_norm_eps=predefined_args.get('layer_norm_eps' , a ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
__A : Union[str, Any] = 'openwebtext_ccnews_stories_books_cased'
# Specify download folder to Gluonnlp's vocab
__A : Any = os.path.join(get_home_dir() , 'models' )
__A : List[Any] = _load_vocab(a , a , a , cls=a )
__A : Dict = nlp.model.BERTModel(
a , len(a ) , units=predefined_args['units'] , embed_size=predefined_args['embed_size'] , embed_dropout=predefined_args['embed_dropout'] , word_embed=predefined_args['word_embed'] , use_pooler=a , use_token_type_embed=a , token_type_vocab_size=predefined_args['token_type_vocab_size'] , use_classifier=a , use_decoder=a , )
original_bort.load_parameters(a , cast_dtype=a , ignore_extra=a )
__A : Union[str, Any] = original_bort._collect_params_with_prefix()
# Build our config 🤗
__A : Any = {
'architectures': ['BertForMaskedLM'],
'attention_probs_dropout_prob': predefined_args['dropout'],
'hidden_act': 'gelu',
'hidden_dropout_prob': predefined_args['dropout'],
'hidden_size': predefined_args['embed_size'],
'initializer_range': 0.02,
'intermediate_size': predefined_args['hidden_size'],
'layer_norm_eps': predefined_args['layer_norm_eps'],
'max_position_embeddings': predefined_args['max_length'],
'model_type': 'bort',
'num_attention_heads': predefined_args['num_heads'],
'num_hidden_layers': predefined_args['num_layers'],
'pad_token_id': 1, # 2 = BERT, 1 = RoBERTa
'type_vocab_size': 1, # 2 = BERT, 1 = RoBERTa
'vocab_size': len(a ),
}
__A : int = BertConfig.from_dict(a )
__A : Union[str, Any] = BertForMaskedLM(a )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(a ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(a , a ):
__A : Tuple = hf_param.shape
__A : str = to_torch(params[gluon_param] )
__A : Union[str, Any] = gluon_param.shape
assert (
shape_hf == shape_gluon
), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
__A : str = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , 'word_embed.0.weight' )
__A : Tuple = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , 'encoder.position_weight' )
__A : List[str] = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , 'encoder.layer_norm.beta' )
__A : Tuple = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , 'encoder.layer_norm.gamma' )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
__A : Tuple = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
__A : BertLayer = hf_bort_model.bert.encoder.layer[i]
# self attention
__A : BertSelfAttention = layer.attention.self
__A : Optional[Any] = check_and_map_params(
self_attn.key.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
__A : Optional[int] = check_and_map_params(
self_attn.key.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
__A : Union[str, Any] = check_and_map_params(
self_attn.query.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
__A : Optional[Any] = check_and_map_params(
self_attn.query.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
__A : Union[str, Any] = check_and_map_params(
self_attn.value.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
__A : Optional[int] = check_and_map_params(
self_attn.value.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
__A : BertSelfOutput = layer.attention.output
__A : Tuple = check_and_map_params(
self_output.dense.bias , F"""encoder.transformer_cells.{i}.proj.bias""" )
__A : int = check_and_map_params(
self_output.dense.weight , F"""encoder.transformer_cells.{i}.proj.weight""" )
__A : List[Any] = check_and_map_params(
self_output.LayerNorm.bias , F"""encoder.transformer_cells.{i}.layer_norm.beta""" )
__A : str = check_and_map_params(
self_output.LayerNorm.weight , F"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
__A : BertIntermediate = layer.intermediate
__A : int = check_and_map_params(
intermediate.dense.bias , F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
__A : List[Any] = check_and_map_params(
intermediate.dense.weight , F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
__A : BertOutput = layer.output
__A : List[Any] = check_and_map_params(
bert_output.dense.bias , F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
__A : Dict = check_and_map_params(
bert_output.dense.weight , F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
__A : Optional[int] = check_and_map_params(
bert_output.LayerNorm.bias , F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
__A : Dict = check_and_map_params(
bert_output.LayerNorm.weight , F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
__A : Any = RobertaTokenizer.from_pretrained('roberta-base' )
__A : List[str] = tokenizer.encode_plus(a )['input_ids']
# Get gluon output
__A : List[str] = mx.nd.array([input_ids] )
__A : Union[str, Any] = original_bort(inputs=a , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(a )
__A : Optional[Any] = BertModel.from_pretrained(a )
hf_bort_model.eval()
__A : Tuple = tokenizer.encode_plus(a , return_tensors='pt' )
__A : Any = hf_bort_model(**a )[0]
__A : Union[str, Any] = output_gluon[0].asnumpy()
__A : Tuple = output_hf[0].detach().numpy()
__A : int = np.max(np.abs(hf_layer - gluon_layer ) ).item()
__A : int = np.allclose(a , a , atol=1e-3 )
if success:
print('✔️ Both model do output the same tensors' )
else:
print('❌ Both model do **NOT** output the same tensors' )
print('Absolute difference is:' , a )
if __name__ == "__main__":
UpperCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCAmelCase : Dict = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 280 | 1 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=10_24, __snake_case=10_24, __snake_case=False, **__snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained(__snake_case )
_UpperCamelCase = SeqaSeqDataset(__snake_case, __snake_case, __snake_case, __snake_case, type_path='''train''', **__snake_case )
_UpperCamelCase = tok.pad_token_id
def get_lens(__snake_case ):
_UpperCamelCase = tqdm(
DataLoader(__snake_case, batch_size=5_12, num_workers=8, shuffle=__snake_case, collate_fn=ds.collate_fn ), desc=str(ds.len_file ), )
_UpperCamelCase = []
for batch in dl:
_UpperCamelCase = batch['''input_ids'''].ne(__snake_case ).sum(1 ).tolist()
_UpperCamelCase = batch['''labels'''].ne(__snake_case ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(__snake_case, __snake_case ):
max_lens.append(max(__snake_case, __snake_case ) )
else:
max_lens.extend(__snake_case )
return max_lens
_UpperCamelCase = get_lens(__snake_case )
_UpperCamelCase = SeqaSeqDataset(__snake_case, __snake_case, __snake_case, __snake_case, type_path='''val''', **__snake_case )
_UpperCamelCase = get_lens(__snake_case )
pickle_save(__snake_case, train_ds.len_file )
pickle_save(__snake_case, val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 100 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = get_tests_dir("""fixtures/dummy-config.json""")
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = 0
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__)
self.assertIsNotNone(importlib.util.find_spec('''transformers.models.auto'''))
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = AutoConfig.from_pretrained('''bert-base-uncased''')
self.assertIsInstance(__a , __a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = AutoConfig.from_pretrained(__a)
self.assertIsInstance(__a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = AutoConfig.from_pretrained(__a)
self.assertIsInstance(__a , __a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = AutoConfig.for_model('''roberta''')
self.assertIsInstance(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
_UpperCamelCase = os.path.join(__a , '''fake-roberta''')
os.makedirs(__a , exist_ok=__a)
with open(os.path.join(__a , '''config.json''') , '''w''') as f:
f.write(json.dumps({}))
_UpperCamelCase = AutoConfig.from_pretrained(__a)
self.assertEqual(type(__a) , __a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
try:
AutoConfig.register('''custom''' , __a)
# Wrong model type will raise an error
with self.assertRaises(__a):
AutoConfig.register('''model''' , __a)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__a):
AutoConfig.register('''bert''' , __a)
# Now that the config is registered, it can be used as any other config with the auto-API
_UpperCamelCase = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a)
_UpperCamelCase = AutoConfig.from_pretrained(__a)
self.assertIsInstance(__a , __a)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
__a , '''bert-base is not a local folder and is not a valid model identifier'''):
_UpperCamelCase = AutoConfig.from_pretrained('''bert-base''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
__a , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
_UpperCamelCase = AutoConfig.from_pretrained(__a , revision='''aaaaaa''')
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
__a , '''hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.''' , ):
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/no-config-test-repo''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__a):
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
# If remote code is disabled, we can't load this config.
with self.assertRaises(__a):
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=__a)
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=__a)
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''')
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__a)
_UpperCamelCase = AutoConfig.from_pretrained(__a , trust_remote_code=__a)
self.assertEqual(reloaded_config.__class__.__name__ , '''NewModelConfig''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'new-model'
try:
AutoConfig.register('''new-model''' , __a)
# If remote code is not set, the default is to use local
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''')
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''')
# If remote code is disabled, we load the local one.
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=__a)
self.assertEqual(config.__class__.__name__ , '''NewModelConfigLocal''')
# If remote is enabled, we load from the Hub
_UpperCamelCase = AutoConfig.from_pretrained('''hf-internal-testing/test_dynamic_model''' , trust_remote_code=__a)
self.assertEqual(config.__class__.__name__ , '''NewModelConfig''')
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 100 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
lowerCamelCase : list[Any] = []
lowerCamelCase : int = 0
lowerCamelCase : int = 0
def _snake_case ( self ):
"""simple docstring"""
return self.head == self.tail
def _snake_case ( self , __A ):
"""simple docstring"""
self.data.append(__A )
lowerCamelCase : str = self.tail + 1
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : str = self.data[self.head]
lowerCamelCase : List[Any] = self.head + 1
return ret
def _snake_case ( self ):
"""simple docstring"""
return self.tail - self.head
def _snake_case ( self ):
"""simple docstring"""
print(self.data )
print("**************" )
print(self.data[self.head : self.tail] )
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A ):
"""simple docstring"""
lowerCamelCase : Dict = data
lowerCamelCase : MyNode | None = None
lowerCamelCase : MyNode | None = None
lowerCamelCase : int = 1
def _snake_case ( self ):
"""simple docstring"""
return self.data
def _snake_case ( self ):
"""simple docstring"""
return self.left
def _snake_case ( self ):
"""simple docstring"""
return self.right
def _snake_case ( self ):
"""simple docstring"""
return self.height
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Dict = data
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Any = node
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Tuple = node
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = height
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if a > b:
return a
return b
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
print("left rotation node:" , node.get_data() )
lowerCamelCase : Union[str, Any] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(__lowerCamelCase )
lowerCamelCase : Any = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__lowerCamelCase )
lowerCamelCase : Any = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(__lowerCamelCase )
return ret
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
print("right rotation node:" , node.get_data() )
lowerCamelCase : Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(__lowerCamelCase )
lowerCamelCase : str = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__lowerCamelCase )
lowerCamelCase : List[str] = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(__lowerCamelCase )
return ret
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(__lowerCamelCase ) )
return right_rotation(__lowerCamelCase )
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[int] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(__lowerCamelCase ) )
return left_rotation(__lowerCamelCase )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if node is None:
return MyNode(__lowerCamelCase )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , __lowerCamelCase ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowerCamelCase : Any = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
lowerCamelCase : Any = right_rotation(__lowerCamelCase )
else:
lowerCamelCase : Tuple = lr_rotation(__lowerCamelCase )
else:
node.set_right(insert_node(node.get_right() , __lowerCamelCase ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowerCamelCase : str = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowerCamelCase : List[str] = rl_rotation(__lowerCamelCase )
else:
lowerCamelCase : str = left_rotation(__lowerCamelCase )
lowerCamelCase : List[Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(__lowerCamelCase )
return node
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
while True:
lowerCamelCase : Optional[Any] = root.get_right()
if right_child is None:
break
lowerCamelCase : Any = right_child
return root.get_data()
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
while True:
lowerCamelCase : Dict = root.get_left()
if left_child is None:
break
lowerCamelCase : Any = left_child
return root.get_data()
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = root.get_left()
lowerCamelCase : Dict = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowerCamelCase : Tuple = get_left_most(__lowerCamelCase )
root.set_data(__lowerCamelCase )
root.set_right(del_node(__lowerCamelCase , __lowerCamelCase ) )
elif left_child is not None:
lowerCamelCase : Optional[int] = left_child
elif right_child is not None:
lowerCamelCase : Tuple = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("No such data" )
return root
else:
root.set_left(del_node(__lowerCamelCase , __lowerCamelCase ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(__lowerCamelCase , __lowerCamelCase ) )
if get_height(__lowerCamelCase ) - get_height(__lowerCamelCase ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowerCamelCase : str = left_rotation(__lowerCamelCase )
else:
lowerCamelCase : List[Any] = rl_rotation(__lowerCamelCase )
elif get_height(__lowerCamelCase ) - get_height(__lowerCamelCase ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowerCamelCase : Tuple = right_rotation(__lowerCamelCase )
else:
lowerCamelCase : Optional[int] = lr_rotation(__lowerCamelCase )
lowerCamelCase : Dict = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(__lowerCamelCase )
return root
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
lowerCamelCase : MyNode | None = None
def _snake_case ( self ):
"""simple docstring"""
return get_height(self.root )
def _snake_case ( self , __A ):
"""simple docstring"""
print("insert:" + str(__A ) )
lowerCamelCase : List[str] = insert_node(self.root , __A )
def _snake_case ( self , __A ):
"""simple docstring"""
print("delete:" + str(__A ) )
if self.root is None:
print("Tree is empty!" )
return
lowerCamelCase : Optional[Any] = del_node(self.root , __A )
def __str__( self , ): # a level traversale, gives a more intuitive look on the tree
"""simple docstring"""
lowerCamelCase : str = ""
lowerCamelCase : Optional[Any] = MyQueue()
q.push(self.root )
lowerCamelCase : Any = self.get_height()
if layer == 0:
return output
lowerCamelCase : List[Any] = 0
while not q.is_empty():
lowerCamelCase : List[str] = q.pop()
lowerCamelCase : Optional[Any] = " " * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(__A )
q.push(__A )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
lowerCamelCase : Optional[Any] = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , __A ) - 1:
lowerCamelCase : Any = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowercase_( ):
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
_snake_case = AVLtree()
_snake_case = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 283 |
from importlib import import_module
from .logging import get_logger
_snake_case : Optional[int] = get_logger(__name__)
class a :
"""simple docstring"""
def __init__( self : List[str] , lowerCamelCase : Optional[Any] , lowerCamelCase : List[str]=None ) -> Any:
__snake_case : Dict = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCamelCase , getattr(lowerCamelCase , lowerCamelCase ) )
__snake_case : int = module._original_module if isinstance(lowerCamelCase , _PatchedModuleObj ) else module
class a :
"""simple docstring"""
__UpperCAmelCase : List[Any] = []
def __init__( self : List[Any] , lowerCamelCase : Any , lowerCamelCase : str , lowerCamelCase : Dict , lowerCamelCase : Optional[Any]=None ) -> List[Any]:
__snake_case : Union[str, Any] = obj
__snake_case : Dict = target
__snake_case : Any = new
__snake_case : List[str] = target.split("." )[0]
__snake_case : Union[str, Any] = {}
__snake_case : int = attrs or []
def __enter__( self : List[Any] ) -> Tuple:
*__snake_case , __snake_case : int = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCamelCase ) ):
try:
__snake_case : Any = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
__snake_case : Union[str, Any] = getattr(self.obj , lowerCamelCase )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCamelCase , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
__snake_case : List[Any] = obj_attr
# patch at top level
setattr(self.obj , lowerCamelCase , _PatchedModuleObj(lowerCamelCase , attrs=self.attrs ) )
__snake_case : Optional[int] = getattr(self.obj , lowerCamelCase )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCamelCase , lowerCamelCase , _PatchedModuleObj(getattr(lowerCamelCase , lowerCamelCase , lowerCamelCase ) , attrs=self.attrs ) )
__snake_case : List[Any] = getattr(lowerCamelCase , lowerCamelCase )
# finally set the target attribute
setattr(lowerCamelCase , lowerCamelCase , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
__snake_case : Union[str, Any] = getattr(import_module(".".join(lowerCamelCase ) ) , lowerCamelCase )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCamelCase ) is attr_value:
__snake_case : Tuple = getattr(self.obj , lowerCamelCase )
setattr(self.obj , lowerCamelCase , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
__snake_case : Dict = globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCamelCase , self.new )
else:
raise RuntimeError(F'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self : Any , *lowerCamelCase : Any ) -> Optional[int]:
for attr in list(self.original ):
setattr(self.obj , lowerCamelCase , self.original.pop(lowerCamelCase ) )
def __snake_case ( self : Optional[Any] ) -> Optional[int]:
self.__enter__()
self._active_patches.append(self )
def __snake_case ( self : Any ) -> List[str]:
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 123 | 0 |
def lowerCamelCase__ ( a__ : int ) -> list:
UpperCamelCase_ = int(snake_case__ )
if n_element < 1:
UpperCamelCase_ = ValueError("""a should be a positive number""" )
raise my_error
UpperCamelCase_ = [1]
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = (0, 0, 0)
UpperCamelCase_ = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
_A = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
_A = hamming(int(n))
print('''-----------------------------------------------------''')
print(F'''The list with nth numbers is: {hamming_numbers}''')
print('''-----------------------------------------------------''')
| 365 |
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(a__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(a__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["input_features", "attention_mask"]
def __init__(self : List[Any] , UpperCAmelCase_ : Dict=80 , UpperCAmelCase_ : Optional[Any]=16_000 , UpperCAmelCase_ : str=80 , UpperCAmelCase_ : List[Any]=0.0 , UpperCAmelCase_ : Optional[Any]=True , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Optional[int]=True , **UpperCAmelCase_ : List[str] , ) ->Optional[Any]:
'''simple docstring'''
super().__init__(feature_size=UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , padding_value=UpperCAmelCase_ , **UpperCAmelCase_)
lowerCamelCase__: Dict =num_mel_bins
lowerCamelCase__: int =do_ceptral_normalize
lowerCamelCase__: int =normalize_means
lowerCamelCase__: Optional[Any] =normalize_vars
lowerCamelCase__: Optional[int] =True
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : np.ndarray , ) ->np.ndarray:
'''simple docstring'''
lowerCamelCase__: Optional[Any] =waveform * (2**15) # Kaldi compliance: 16-bit signed integers
lowerCamelCase__: Tuple =torch.from_numpy(UpperCAmelCase_).unsqueeze(0)
lowerCamelCase__: Any =ta_kaldi.fbank(UpperCAmelCase_ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate)
return features.numpy()
@staticmethod
def SCREAMING_SNAKE_CASE_ (UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[bool] = True , UpperCAmelCase_ : Optional[bool] = True , UpperCAmelCase_ : float = 0.0 , ) ->np.ndarray:
'''simple docstring'''
if normalize_means:
lowerCamelCase__: Tuple =x[:input_length].mean(axis=0)
lowerCamelCase__: List[Any] =np.subtract(UpperCAmelCase_ , UpperCAmelCase_)
if normalize_vars:
lowerCamelCase__: int =x[:input_length].std(axis=0)
lowerCamelCase__: List[str] =np.divide(UpperCAmelCase_ , UpperCAmelCase_)
if input_length < x.shape[0]:
lowerCamelCase__: List[Any] =padding_value
# make sure array is in float32
lowerCamelCase__: List[str] =x.astype(np.floataa)
return x
def SCREAMING_SNAKE_CASE_ (self : Optional[Any] , UpperCAmelCase_ : List[np.ndarray] , UpperCAmelCase_ : Optional[np.ndarray] = None) ->List[np.ndarray]:
'''simple docstring'''
lowerCamelCase__: Tuple =attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(UpperCAmelCase_ , UpperCAmelCase_ , self.normalize_means , self.normalize_vars , self.padding_value)
for x, n in zip(UpperCAmelCase_ , UpperCAmelCase_)
]
def __call__(self : str , UpperCAmelCase_ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCAmelCase_ : Union[bool, str, PaddingStrategy] = False , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[Union[str, TensorType]] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : Optional[bool] = None , **UpperCAmelCase_ : List[str] , ) ->BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""")
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug.")
lowerCamelCase__: Optional[Any] =isinstance(UpperCAmelCase_ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
lowerCamelCase__: int =is_batched_numpy or (
isinstance(UpperCAmelCase_ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
lowerCamelCase__: str =[np.asarray(UpperCAmelCase_ , dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(UpperCAmelCase_ , np.ndarray):
lowerCamelCase__: str =np.asarray(UpperCAmelCase_ , dtype=np.floataa)
elif isinstance(UpperCAmelCase_ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
lowerCamelCase__: Union[str, Any] =raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
lowerCamelCase__: List[str] =[raw_speech]
# extract fbank features
lowerCamelCase__: str =[self._extract_fbank_features(UpperCAmelCase_) for waveform in raw_speech]
# convert into correct format for padding
lowerCamelCase__: Any =BatchFeature({"input_features": features})
lowerCamelCase__: Union[str, Any] =self.pad(
UpperCAmelCase_ , padding=UpperCAmelCase_ , max_length=UpperCAmelCase_ , truncation=UpperCAmelCase_ , pad_to_multiple_of=UpperCAmelCase_ , return_attention_mask=UpperCAmelCase_ , **UpperCAmelCase_ , )
# make sure list is in array format
lowerCamelCase__: Optional[Any] =padded_inputs.get("input_features")
if isinstance(input_features[0] , UpperCAmelCase_):
lowerCamelCase__: Tuple =[np.asarray(UpperCAmelCase_ , dtype=np.floataa) for feature in input_features]
lowerCamelCase__: Union[str, Any] =padded_inputs.get("attention_mask")
if attention_mask is not None:
lowerCamelCase__: Optional[int] =[np.asarray(UpperCAmelCase_ , dtype=np.intaa) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
lowerCamelCase__: Any =(
np.array(UpperCAmelCase_ , dtype=np.intaa)
if self._get_padding_strategies(UpperCAmelCase_ , max_length=UpperCAmelCase_) is not PaddingStrategy.DO_NOT_PAD
else None
)
lowerCamelCase__: Any =self.normalize(
padded_inputs["input_features"] , attention_mask=UpperCAmelCase_)
if return_tensors is not None:
lowerCamelCase__: str =padded_inputs.convert_to_tensors(UpperCAmelCase_)
return padded_inputs
| 10 |
'''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
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
def UpperCAmelCase ( a_ , a_ ) -> Optional[int]:
"""simple docstring"""
try:
with open(a_ , """rb""" ) as flax_state_f:
A_ : Tuple = from_bytes(a_ , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(a_ ) 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(a_ , a_ )
def UpperCAmelCase ( a_ , a_ ) -> Any:
"""simple docstring"""
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
A_ : List[Any] = flatten_dict(jax.tree_util.tree_map(lambda a_ : x.dtype == jnp.bfloataa , a_ ) ).values()
if any(a_ ):
# 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.""" )
A_ : str = jax.tree_util.tree_map(
lambda a_ : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , a_ )
A_ : Any = """"""
A_ : Optional[int] = flatten_dict(a_ , sep=""".""" )
A_ : List[str] = pt_model.state_dict()
# keep track of unexpected & missing keys
A_ : Union[str, Any] = []
A_ : Dict = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
A_ : List[Any] = flax_key_tuple.split(""".""" )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
A_ : Optional[Any] = flax_key_tuple_array[:-1] + ["""weight"""]
A_ : Optional[Any] = jnp.transpose(a_ , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
A_ : int = flax_key_tuple_array[:-1] + ["""weight"""]
A_ : Optional[int] = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
A_ : Any = flax_key_tuple_array[:-1] + ["""weight"""]
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(a_ ):
A_ : Tuple = (
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""" )
)
A_ : Dict = """.""".join(a_ )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
A_ : Optional[Any] = np.asarray(a_ ) if not isinstance(a_ , np.ndarray ) else flax_tensor
A_ : Tuple = torch.from_numpy(a_ )
# remove from missing keys
missing_keys.remove(a_ )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(a_ )
pt_model.load_state_dict(a_ )
# re-transform missing_keys to list
A_ : Dict = list(a_ )
if len(a_ ) > 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(a_ ) > 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
| 344 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
a__ : int = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
a__ : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a__ : List[Any] = [2, 4, 1, 5]
a__ : Union[str, Any] = len(train_data)
a__ : Tuple = 0.0_09
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_="train" ):
'''simple docstring'''
return calculate_hypothesis_value(lowerCAmelCase_ , lowerCAmelCase_ ) - output(
lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for i in range(len(lowerCAmelCase_ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_=m ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for i in range(lowerCAmelCase_ ):
if index == -1:
summation_value += _error(lowerCAmelCase_ )
else:
summation_value += _error(lowerCAmelCase_ ) * train_data[i][0][index]
return summation_value
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = summation_of_cost_derivative(lowerCAmelCase_ , lowerCAmelCase_ ) / m
return cost_derivative_value
def UpperCAmelCase__ ():
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
__SCREAMING_SNAKE_CASE = 0.000002
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while True:
j += 1
__SCREAMING_SNAKE_CASE = [0, 0, 0, 0]
for i in range(0 , len(lowerCAmelCase_ ) ):
__SCREAMING_SNAKE_CASE = get_cost_derivative(i - 1 )
__SCREAMING_SNAKE_CASE = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
lowerCAmelCase_ , lowerCAmelCase_ , atol=lowerCAmelCase_ , rtol=lowerCAmelCase_ , ):
break
__SCREAMING_SNAKE_CASE = temp_parameter_vector
print(("Number of iterations:", j) )
def UpperCAmelCase__ ():
'''simple docstring'''
for i in range(len(lowerCAmelCase_ ) ):
print(("Actual output value:", output(lowerCAmelCase_ , "test" )) )
print(("Hypothesis output:", calculate_hypothesis_value(lowerCAmelCase_ , "test" )) )
if __name__ == "__main__":
run_gradient_descent()
print('''\nTesting gradient descent for a linear hypothesis function.\n''')
test_gradient_descent()
| 351 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCAmelCase_ , lowerCAmelCase_ ) ) )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if dataset.ndim != value_array.ndim:
__SCREAMING_SNAKE_CASE = (
"Wrong input data's dimensions... "
f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
)
raise ValueError(lowerCAmelCase_ )
try:
if dataset.shape[1] != value_array.shape[1]:
__SCREAMING_SNAKE_CASE = (
"Wrong input data's shape... "
f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
)
raise ValueError(lowerCAmelCase_ )
except IndexError:
if dataset.ndim != value_array.ndim:
raise TypeError("Wrong shape" )
if dataset.dtype != value_array.dtype:
__SCREAMING_SNAKE_CASE = (
"Input data have different datatype... "
f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
)
raise TypeError(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = []
for value in value_array:
__SCREAMING_SNAKE_CASE = euclidean(lowerCAmelCase_ , dataset[0] )
__SCREAMING_SNAKE_CASE = dataset[0].tolist()
for dataset_value in dataset[1:]:
__SCREAMING_SNAKE_CASE = euclidean(lowerCAmelCase_ , lowerCAmelCase_ )
if dist > temp_dist:
__SCREAMING_SNAKE_CASE = temp_dist
__SCREAMING_SNAKE_CASE = dataset_value.tolist()
answer.append([vector, dist] )
return answer
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return np.dot(lowerCAmelCase_ , lowerCAmelCase_ ) / (norm(lowerCAmelCase_ ) * norm(lowerCAmelCase_ ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 195 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : float | Decimal , snake_case__ : float = 10**-10 ):
"""simple docstring"""
_snake_case : Optional[Any] = a
while True:
_snake_case : Optional[Any] = Decimal(snake_case__ ) - (
Decimal(eval(snake_case__ ) ) / Decimal(eval(str(diff(snake_case__ ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(snake_case__ ) ) < precision: # noqa: S307
return float(snake_case__ )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')
# Find root of polynomial
print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}''')
# Find Square Root of 5
print(F'''The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}''')
# Exponential Roots
print(F'''The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}''')
| 64 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ):
'''simple docstring'''
A : List[Any] = inspect.getfile(accelerate.test_utils )
A : int = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
A : List[str] = ['''accelerate''', '''launch''']
A : List[Any] = Path.home() / '''.cache/huggingface/accelerate'''
A : Any = '''default_config.yaml'''
A : Dict = config_folder / config_file
A : Union[str, Any] = config_folder / '''_default_config.yaml'''
A : int = Path('''tests/test_configs''' )
@classmethod
def UpperCamelCase_ ( cls ):
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def UpperCamelCase_ ( cls ):
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path], env=os.environ.copy() )
def UpperCamelCase_ ( self ):
'''simple docstring'''
for config in sorted(self.test_config_path.glob('**/*.yaml' ) ):
with self.subTest(config_file=A ):
execute_subprocess_async(
self.base_cmd + ['--config_file', str(A ), self.test_file_path], env=os.environ.copy() )
def UpperCamelCase_ ( self ):
'''simple docstring'''
execute_subprocess_async(['accelerate', 'test'], env=os.environ.copy() )
class _a ( unittest.TestCase ):
'''simple docstring'''
A : Union[str, Any] = '''test-tpu'''
A : List[str] = '''us-central1-a'''
A : List[str] = '''ls'''
A : List[str] = ['''accelerate''', '''tpu-config''']
A : List[str] = '''cd /usr/share'''
A : Optional[int] = '''tests/test_samples/test_command_file.sh'''
A : Optional[int] = '''Running gcloud compute tpus tpu-vm ssh'''
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = run_command(
self.cmd
+ ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug'], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command',
self.command,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug'], return_stdout=A )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug'], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--command',
self.command,
'--command',
'echo "Hello World"',
'--debug',
], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = run_command(
self.cmd
+ ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug'], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/0_12_0.yaml',
'--command_file',
self.command_file,
'--tpu_zone',
self.tpu_zone,
'--tpu_name',
self.tpu_name,
'--debug',
], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = run_command(
self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug'], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all", A, )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = run_command(
self.cmd
+ [
'--config_file',
'tests/test_configs/latest.yaml',
'--install_accelerate',
'--accelerate_version',
'12.0.0',
'--debug',
], return_stdout=A, )
self.assertIn(
F"{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all", A, )
| 251 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase (__UpperCamelCase : str ):
"""simple docstring"""
__UpperCamelCase =SwinvaConfig()
__UpperCamelCase =swinva_name.split('''_''' )
__UpperCamelCase =name_split[1]
if "to" in name_split[3]:
__UpperCamelCase =int(name_split[3][-3:] )
else:
__UpperCamelCase =int(name_split[3] )
if "to" in name_split[2]:
__UpperCamelCase =int(name_split[2][-2:] )
else:
__UpperCamelCase =int(name_split[2][6:] )
if model_size == "tiny":
__UpperCamelCase =9_6
__UpperCamelCase =(2, 2, 6, 2)
__UpperCamelCase =(3, 6, 1_2, 2_4)
elif model_size == "small":
__UpperCamelCase =9_6
__UpperCamelCase =(2, 2, 1_8, 2)
__UpperCamelCase =(3, 6, 1_2, 2_4)
elif model_size == "base":
__UpperCamelCase =1_2_8
__UpperCamelCase =(2, 2, 1_8, 2)
__UpperCamelCase =(4, 8, 1_6, 3_2)
else:
__UpperCamelCase =1_9_2
__UpperCamelCase =(2, 2, 1_8, 2)
__UpperCamelCase =(6, 1_2, 2_4, 4_8)
if "to" in swinva_name:
__UpperCamelCase =(1_2, 1_2, 1_2, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
__UpperCamelCase =2_1_8_4_1
__UpperCamelCase ='''huggingface/label-files'''
__UpperCamelCase ='''imagenet-22k-id2label.json'''
__UpperCamelCase =json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
__UpperCamelCase ={int(__UpperCamelCase ): v for k, v in idalabel.items()}
__UpperCamelCase =idalabel
__UpperCamelCase ={v: k for k, v in idalabel.items()}
else:
__UpperCamelCase =1_0_0_0
__UpperCamelCase ='''huggingface/label-files'''
__UpperCamelCase ='''imagenet-1k-id2label.json'''
__UpperCamelCase =json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='''dataset''' ) , '''r''' ) )
__UpperCamelCase ={int(__UpperCamelCase ): v for k, v in idalabel.items()}
__UpperCamelCase =idalabel
__UpperCamelCase ={v: k for k, v in idalabel.items()}
__UpperCamelCase =img_size
__UpperCamelCase =num_classes
__UpperCamelCase =embed_dim
__UpperCamelCase =depths
__UpperCamelCase =num_heads
__UpperCamelCase =window_size
return config
def lowerCAmelCase (__UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if "patch_embed.proj" 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.norm''' )
if "layers" in name:
__UpperCamelCase ='''encoder.''' + name
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 name == "norm.weight":
__UpperCamelCase ='''layernorm.weight'''
if name == "norm.bias":
__UpperCamelCase ='''layernorm.bias'''
if "head" in name:
__UpperCamelCase =name.replace('''head''' , '''classifier''' )
else:
__UpperCamelCase ='''swinv2.''' + name
return name
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__UpperCamelCase =orig_state_dict.pop(__UpperCamelCase )
if "mask" in key:
continue
elif "qkv" in key:
__UpperCamelCase =key.split('''.''' )
__UpperCamelCase =int(key_split[1] )
__UpperCamelCase =int(key_split[3] )
__UpperCamelCase =model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
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:]
else:
__UpperCamelCase =val
return orig_state_dict
def lowerCAmelCase (__UpperCamelCase : Union[str, Any] , __UpperCamelCase : Any ):
"""simple docstring"""
__UpperCamelCase =timm.create_model(__UpperCamelCase , pretrained=__UpperCamelCase )
timm_model.eval()
__UpperCamelCase =get_swinva_config(__UpperCamelCase )
__UpperCamelCase =SwinvaForImageClassification(__UpperCamelCase )
model.eval()
__UpperCamelCase =convert_state_dict(timm_model.state_dict() , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
__UpperCamelCase ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
__UpperCamelCase =AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' , '''-''' ) ) )
__UpperCamelCase =Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
__UpperCamelCase =image_processor(images=__UpperCamelCase , return_tensors='''pt''' )
__UpperCamelCase =timm_model(inputs['''pixel_values'''] )
__UpperCamelCase =model(**__UpperCamelCase ).logits
assert torch.allclose(__UpperCamelCase , __UpperCamelCase , atol=1E-3 )
print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__UpperCamelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__UpperCamelCase )
model.push_to_hub(
repo_path_or_name=Path(__UpperCamelCase , __UpperCamelCase ) , organization='''nandwalritik''' , commit_message='''Add model''' , )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 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 = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 85 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = '''albert'''
def __init__( self : List[Any] , UpperCamelCase__ : List[Any]=30000 , UpperCamelCase__ : int=128 , UpperCamelCase__ : str=4096 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : Union[str, Any]=64 , UpperCamelCase__ : Any=16384 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Optional[int]="gelu_new" , UpperCamelCase__ : int=0 , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : Dict=512 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : str=0.02 , UpperCamelCase__ : Tuple=1E-12 , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Dict="absolute" , UpperCamelCase__ : List[Any]=0 , UpperCamelCase__ : int=2 , UpperCamelCase__ : Optional[Any]=3 , **UpperCamelCase__ : List[str] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
__UpperCamelCase =vocab_size
__UpperCamelCase =embedding_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_hidden_groups
__UpperCamelCase =num_attention_heads
__UpperCamelCase =inner_group_num
__UpperCamelCase =hidden_act
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =type_vocab_size
__UpperCamelCase =initializer_range
__UpperCamelCase =layer_norm_eps
__UpperCamelCase =classifier_dropout_prob
__UpperCamelCase =position_embedding_type
class _lowercase ( __a ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__UpperCamelCase ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__UpperCamelCase ={0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 85 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
a__ : Union[str, Any] = None
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : Optional[int] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
a__ : Optional[int] = {
'''vocab_file''': {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
},
}
a__ : List[str] = {
'''google/fnet-base''': 5_1_2,
'''google/fnet-large''': 5_1_2,
}
a__ : Union[str, Any] = '''▁'''
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : List[Any] = VOCAB_FILES_NAMES
snake_case__ : int = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : List[str] = ["input_ids", "token_type_ids"]
snake_case__ : Tuple = FNetTokenizer
def __init__( self : str , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : Optional[Any]=False , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : int=True , UpperCAmelCase__ : List[Any]="<unk>" , UpperCAmelCase__ : Dict="[SEP]" , UpperCAmelCase__ : Tuple="<pad>" , UpperCAmelCase__ : Any="[CLS]" , UpperCAmelCase__ : List[Any]="[MASK]" , **UpperCAmelCase__ : Union[str, Any] , ) -> str:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__SCREAMING_SNAKE_CASE = (
AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ , normalized=UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
else mask_token
)
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , remove_space=UpperCAmelCase__ , keep_accents=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , **UpperCAmelCase__ , )
__SCREAMING_SNAKE_CASE = do_lower_case
__SCREAMING_SNAKE_CASE = remove_space
__SCREAMING_SNAKE_CASE = keep_accents
__SCREAMING_SNAKE_CASE = vocab_file
__SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
def UpperCAmelCase_ ( self : List[Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : int , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [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 : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE = os.path.join(
UpperCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 54 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : List[Any] , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : Any ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Any , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : List[str] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : int , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Any ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Dict , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Optional[Any] ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : int , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : List[str] ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : List[Any] , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : List[Any] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : List[str] ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : str ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : List[Any] , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : List[str] , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[Any] , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : List[str] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : List[str] , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Any , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : str , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : Any ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : Optional[Any] ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : int , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : Tuple ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Dict , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : Tuple ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Any , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : str ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Dict , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : Tuple ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : List[Any] ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : List[Any] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Dict , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Optional[int] , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : Dict ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[Any] , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : Optional[Any] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : Any ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : List[str] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : str ):
requires_backends(cls , ['flax'] )
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""flax"""]
def __init__( self : Optional[Any] , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : int ):
requires_backends(self , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
requires_backends(cls , ['flax'] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : Tuple ):
requires_backends(cls , ['flax'] )
| 224 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a: List[str] = logging.get_logger(__name__)
__a: str = {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
"""google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
"""google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "big_bird"
def __init__( self , __lowerCAmelCase=50358 , __lowerCAmelCase=768 , __lowerCAmelCase=12 , __lowerCAmelCase=12 , __lowerCAmelCase=3072 , __lowerCAmelCase="gelu_new" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=4096 , __lowerCAmelCase=2 , __lowerCAmelCase=0.0_2 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=True , __lowerCAmelCase=0 , __lowerCAmelCase=1 , __lowerCAmelCase=2 , __lowerCAmelCase=66 , __lowerCAmelCase="block_sparse" , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=64 , __lowerCAmelCase=3 , __lowerCAmelCase=None , **__lowerCAmelCase , ) -> Optional[int]:
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , sep_token_id=__lowerCAmelCase , **__lowerCAmelCase , )
lowercase__ : Tuple = vocab_size
lowercase__ : List[str] = max_position_embeddings
lowercase__ : Any = hidden_size
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : List[str] = num_attention_heads
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : int = hidden_act
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : Optional[int] = initializer_range
lowercase__ : Optional[int] = type_vocab_size
lowercase__ : int = layer_norm_eps
lowercase__ : Dict = use_cache
lowercase__ : Optional[Any] = rescale_embeddings
lowercase__ : List[Any] = attention_type
lowercase__ : Tuple = use_bias
lowercase__ : Union[str, Any] = block_size
lowercase__ : Optional[Any] = num_random_blocks
lowercase__ : Union[str, Any] = classifier_dropout
class UpperCAmelCase ( a__ ):
'''simple docstring'''
@property
def _lowerCAmelCase( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowercase__ : List[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase__ : Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 214 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = DDIMPipeline
SCREAMING_SNAKE_CASE = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
SCREAMING_SNAKE_CASE = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE = False
def _lowerCAmelCase( self ) -> Union[str, Any]:
torch.manual_seed(0 )
lowercase__ : Optional[int] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
lowercase__ : Any = DDIMScheduler()
lowercase__ : Union[str, Any] = {'''unet''': unet, '''scheduler''': scheduler}
return components
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> int:
if str(__lowerCAmelCase ).startswith('''mps''' ):
lowercase__ : Optional[Any] = torch.manual_seed(__lowerCAmelCase )
else:
lowercase__ : int = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
lowercase__ : List[Any] = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ : int = '''cpu'''
lowercase__ : Optional[Any] = self.get_dummy_components()
lowercase__ : str = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : str = self.get_dummy_inputs(__lowerCAmelCase )
lowercase__ : str = pipe(**__lowerCAmelCase ).images
lowercase__ : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
lowercase__ : Optional[int] = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowercase__ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase , 1E-3 )
def _lowerCAmelCase( self ) -> str:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> List[str]:
super().test_save_load_local(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> Tuple:
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> int:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : List[str] = '''google/ddpm-cifar10-32'''
lowercase__ : List[Any] = UNetaDModel.from_pretrained(__lowerCAmelCase )
lowercase__ : str = DDIMScheduler()
lowercase__ : List[Any] = DDIMPipeline(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
ddim.to(__lowerCAmelCase )
ddim.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Optional[int] = ddim(generator=__lowerCAmelCase , eta=0.0 , output_type='''numpy''' ).images
lowercase__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase__ : Optional[int] = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Optional[Any] = '''google/ddpm-ema-bedroom-256'''
lowercase__ : Tuple = UNetaDModel.from_pretrained(__lowerCAmelCase )
lowercase__ : Union[str, Any] = DDIMScheduler.from_pretrained(__lowerCAmelCase )
lowercase__ : List[Any] = DDIMPipeline(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
ddpm.to(__lowerCAmelCase )
ddpm.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Union[str, Any] = torch.manual_seed(0 )
lowercase__ : Optional[Any] = ddpm(generator=__lowerCAmelCase , output_type='''numpy''' ).images
lowercase__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase__ : Union[str, Any] = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 214 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
A__ : Tuple =logging.get_logger(__name__)
class UpperCAmelCase :
_lowercase: str
_lowercase: str = None
@staticmethod
def lowercase__ ( ) -> Dict:
raise NotImplementedError
def lowercase__ ( self : List[str] , __snake_case : List[Any] , __snake_case : int , __snake_case : str , **__snake_case : str ) -> str:
raise NotImplementedError
def lowercase__ ( self : Any , __snake_case : Any ) -> str:
raise NotImplementedError
def lowercase__ ( self : Dict ) -> Tuple:
if not self.is_available():
raise RuntimeError(
f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}." )
@classmethod
def lowercase__ ( cls : Optional[Any] ) -> str:
return f"`pip install {cls.pip_package or cls.name}`"
class UpperCAmelCase ( snake_case_ ):
_lowercase: Union[str, Any] = '''optuna'''
@staticmethod
def lowercase__ ( ) -> List[str]:
return is_optuna_available()
def lowercase__ ( self : Tuple , __snake_case : Optional[Any] , __snake_case : int , __snake_case : str , **__snake_case : Dict ) -> Tuple:
return run_hp_search_optuna(__snake_case , __snake_case , __snake_case , **__snake_case )
def lowercase__ ( self : List[str] , __snake_case : List[Any] ) -> Optional[Any]:
return default_hp_space_optuna(__snake_case )
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[Any] = '''ray'''
_lowercase: Union[str, Any] = '''\'ray[tune]\''''
@staticmethod
def lowercase__ ( ) -> Any:
return is_ray_available()
def lowercase__ ( self : Dict , __snake_case : Dict , __snake_case : int , __snake_case : str , **__snake_case : List[str] ) -> List[str]:
return run_hp_search_ray(__snake_case , __snake_case , __snake_case , **__snake_case )
def lowercase__ ( self : int , __snake_case : Dict ) -> Union[str, Any]:
return default_hp_space_ray(__snake_case )
class UpperCAmelCase ( snake_case_ ):
_lowercase: str = '''sigopt'''
@staticmethod
def lowercase__ ( ) -> List[Any]:
return is_sigopt_available()
def lowercase__ ( self : Optional[Any] , __snake_case : List[str] , __snake_case : int , __snake_case : str , **__snake_case : Union[str, Any] ) -> str:
return run_hp_search_sigopt(__snake_case , __snake_case , __snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : str ) -> Union[str, Any]:
return default_hp_space_sigopt(__snake_case )
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[Any] = '''wandb'''
@staticmethod
def lowercase__ ( ) -> int:
return is_wandb_available()
def lowercase__ ( self : List[str] , __snake_case : Optional[Any] , __snake_case : int , __snake_case : str , **__snake_case : List[str] ) -> Optional[int]:
return run_hp_search_wandb(__snake_case , __snake_case , __snake_case , **__snake_case )
def lowercase__ ( self : int , __snake_case : Optional[Any] ) -> Any:
return default_hp_space_wandb(__snake_case )
A__ : int ={
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCAmelCase ) > 0:
_lowerCAmelCase = available_backends[0].name
if len(lowerCAmelCase ) > 1:
logger.info(
f"{len(lowerCAmelCase )} hyperparameter search backends available. Using {name} as the default." )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f" - To install {backend.name} run {backend.pip_install()}"
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 70 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=6 , _UpperCAmelCase=17 , _UpperCAmelCase=23 , _UpperCAmelCase=11 , _UpperCAmelCase=True , ):
__a : str = parent
__a : Any = batch_size
__a : Any = seq_length
__a : Optional[int] = act_dim
__a : Any = state_dim
__a : str = hidden_size
__a : List[str] = max_length
__a : Dict = is_training
def _lowerCamelCase ( self ):
__a : Dict = floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
__a : Union[str, Any] = floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
__a : int = floats_tensor((self.batch_size, self.seq_length, 1) )
__a : List[str] = floats_tensor((self.batch_size, self.seq_length, 1) )
__a : Optional[Any] = ids_tensor((self.batch_size, self.seq_length) , vocab_size=1000 )
__a : str = random_attention_mask((self.batch_size, self.seq_length) )
__a : Dict = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def _lowerCamelCase ( self ):
return DecisionTransformerConfig(
batch_size=self.batch_size , seq_length=self.seq_length , act_dim=self.act_dim , state_dim=self.state_dim , hidden_size=self.hidden_size , max_length=self.max_length , )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
__a : Union[str, Any] = DecisionTransformerModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : int = model(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
self.parent.assertEqual(result.state_preds.shape , states.shape )
self.parent.assertEqual(result.action_preds.shape , actions.shape )
self.parent.assertEqual(result.return_preds.shape , returns_to_go.shape )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.seq_length * 3, self.hidden_size) ) # seq length *3 as there are 3 modelities: states, returns and actions
def _lowerCamelCase ( self ):
__a : Optional[Any] = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : List[str] = config_and_inputs
__a : Dict = {
'''states''': states,
'''actions''': actions,
'''rewards''': rewards,
'''returns_to_go''': returns_to_go,
'''timesteps''': timesteps,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_torch
class __lowercase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = (DecisionTransformerModel,) if is_torch_available() else ()
__lowerCAmelCase = ()
__lowerCAmelCase = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
__lowerCAmelCase = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
def _lowerCamelCase ( self ):
__a : str = DecisionTransformerModelTester(self )
__a : Any = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def _lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def _lowerCamelCase ( self ):
__a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
@slow
def _lowerCamelCase ( self ):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Dict = DecisionTransformerModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a , __a : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a : Optional[int] = model_class(_UpperCAmelCase )
__a : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a : Optional[int] = [*signature.parameters.keys()]
__a : Union[str, Any] = [
'''states''',
'''actions''',
'''rewards''',
'''returns_to_go''',
'''timesteps''',
'''attention_mask''',
]
self.assertListEqual(arg_names[: len(_UpperCAmelCase )] , _UpperCAmelCase )
@require_torch
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
__a : Dict = 2 # number of steps of autoregressive prediction we will perform
__a : List[str] = 10 # defined by the RL environment, may be normalized
__a : Union[str, Any] = DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert''' )
__a : str = model.to(_UpperCAmelCase )
__a : str = model.config
torch.manual_seed(0 )
__a : List[str] = torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ) # env.reset()
__a : List[str] = torch.tensor(
[[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]] , device=_UpperCAmelCase )
__a : str = torch.tensor(_UpperCAmelCase , device=_UpperCAmelCase , dtype=torch.floataa ).reshape(1 , 1 , 1 )
__a : str = state
__a : List[Any] = torch.zeros(1 , 0 , config.act_dim , device=_UpperCAmelCase , dtype=torch.floataa )
__a : List[Any] = torch.zeros(1 , 0 , device=_UpperCAmelCase , dtype=torch.floataa )
__a : int = torch.tensor(0 , device=_UpperCAmelCase , dtype=torch.long ).reshape(1 , 1 )
for step in range(_UpperCAmelCase ):
__a : Optional[int] = torch.cat([actions, torch.zeros(1 , 1 , config.act_dim , device=_UpperCAmelCase )] , dim=1 )
__a : Any = torch.cat([rewards, torch.zeros(1 , 1 , device=_UpperCAmelCase )] , dim=1 )
__a : Any = torch.ones(1 , states.shape[1] ).to(dtype=torch.long , device=states.device )
with torch.no_grad():
__a , __a , __a : int = model(
states=_UpperCAmelCase , actions=_UpperCAmelCase , rewards=_UpperCAmelCase , returns_to_go=_UpperCAmelCase , timesteps=_UpperCAmelCase , attention_mask=_UpperCAmelCase , return_dict=_UpperCAmelCase , )
self.assertEqual(action_pred.shape , actions.shape )
self.assertTrue(torch.allclose(action_pred[0, -1] , expected_outputs[step] , atol=1e-4 ) )
__a , __a , __a , __a : Dict = ( # env.step(action)
torch.randn(1 , 1 , config.state_dim ).to(device=_UpperCAmelCase , dtype=torch.floataa ),
1.0,
False,
{},
)
__a : int = action_pred[0, -1]
__a : int = torch.cat([states, state] , dim=1 )
__a : Any = returns_to_go[0, -1] - reward
__a : Dict = torch.cat([returns_to_go, pred_return.reshape(1 , 1 , 1 )] , dim=1 )
__a : Optional[Any] = torch.cat(
[timesteps, torch.ones((1, 1) , device=_UpperCAmelCase , dtype=torch.long ) * (step + 1)] , dim=1 ) | 160 | 0 |
def lowerCAmelCase__ ( a__: list ) -> list:
'''simple docstring'''
if any(not isinstance(a__ , a__ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(a__ ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(a__ , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9] | 364 |
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase__ :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
lowerCAmelCase__ :Any = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowerCAmelCase__ ( a__: list[list[int]] ) -> list[list[int]]:
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(a__ ) ):
_UpperCAmelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_UpperCAmelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(a__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(a__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(a__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_UpperCAmelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(a__ )
return next_generation
def lowerCAmelCase__ ( a__: list[list[int]] , a__: int ) -> list[Image.Image]:
'''simple docstring'''
_UpperCAmelCase = []
for _ in range(a__ ):
# Create output image
_UpperCAmelCase = Image.new('RGB' , (len(cells[0] ), len(a__ )) )
_UpperCAmelCase = img.load()
# Save cells to image
for x in range(len(a__ ) ):
for y in range(len(cells[0] ) ):
_UpperCAmelCase = 2_5_5 - cells[y][x] * 2_5_5
_UpperCAmelCase = (colour, colour, colour)
# Save image
images.append(a__ )
_UpperCAmelCase = new_generation(a__ )
return images
if __name__ == "__main__":
lowerCAmelCase__ :Tuple = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 185 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def UpperCAmelCase_ ( __snake_case , __snake_case=False ) -> Optional[Any]:
"""simple docstring"""
_lowercase =OmegaConf.load(_UpperCamelCase )
if display:
print(yaml.dump(OmegaConf.to_container(_UpperCamelCase ) ) )
return config
def UpperCAmelCase_ ( __snake_case , __snake_case=None , __snake_case=None ) -> Optional[Any]:
"""simple docstring"""
if conf_path is None:
_lowercase ='''./model_checkpoints/vqgan_only.yaml'''
_lowercase =load_config(_UpperCamelCase , display=_UpperCamelCase )
_lowercase =VQModel(**config.model.params )
if ckpt_path is None:
_lowercase ='''./model_checkpoints/vqgan_only.pt'''
_lowercase =torch.load(_UpperCamelCase , map_location=_UpperCamelCase )
if ".ckpt" in ckpt_path:
_lowercase =sd['''state_dict''']
model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase )
model.to(_UpperCamelCase )
del sd
return model
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Dict:
"""simple docstring"""
_lowercase , _lowercase , _lowercase =model.encode(_UpperCamelCase )
print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
_lowercase =model.decode(_UpperCamelCase )
return xrec
def UpperCAmelCase_ ( __snake_case , __snake_case=False ) -> int:
"""simple docstring"""
_lowercase , _lowercase =string.rsplit('''.''' , 1 )
if reload:
_lowercase =importlib.import_module(_UpperCamelCase )
importlib.reload(_UpperCamelCase )
return getattr(importlib.import_module(_UpperCamelCase , package=_UpperCamelCase ) , cls )
def UpperCAmelCase_ ( __snake_case ) -> List[str]:
"""simple docstring"""
if "target" not in config:
raise KeyError('''Expected key `target` to instantiate.''' )
return get_obj_from_str(config['''target'''] )(**config.get('''params''' , {} ) )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case=True , __snake_case=True ) -> Union[str, Any]:
"""simple docstring"""
_lowercase =instantiate_from_config(_UpperCamelCase )
if sd is not None:
model.load_state_dict(_UpperCamelCase )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case ) -> List[Any]:
"""simple docstring"""
if ckpt:
_lowercase =torch.load(_UpperCamelCase , map_location='''cpu''' )
_lowercase =pl_sd['''global_step''']
print(F"loaded model from global step {global_step}." )
else:
_lowercase ={'''state_dict''': None}
_lowercase =None
_lowercase =load_model_from_config(config.model , pl_sd['''state_dict'''] , gpu=_UpperCamelCase , eval_mode=_UpperCamelCase )['''model''']
return model, global_step
| 5 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""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(_UpperCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowerCAmelCase ( _UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =str(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =[n]
for i in range(1 , len(_UpperCamelCase ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
if len(str(_UpperCamelCase ) ) > 3:
if not is_prime(int(str(_UpperCamelCase )[-3:] ) ) or not is_prime(int(str(_UpperCamelCase )[:3] ) ):
return False
return True
def _lowerCAmelCase ( _UpperCamelCase : int = 11 ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =13
while len(_UpperCamelCase ) != count:
if validate(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =list_truncated_nums(_UpperCamelCase )
if all(is_prime(_UpperCamelCase ) for i in list_nums ):
list_truncated_primes.append(_UpperCamelCase )
num += 2
return list_truncated_primes
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(f'''{sum(compute_truncated_primes(1_1)) = }''')
| 47 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Dict = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
'NezhaForNextSentencePrediction',
'NezhaForMaskedLM',
'NezhaForPreTraining',
'NezhaForMultipleChoice',
'NezhaForQuestionAnswering',
'NezhaForSequenceClassification',
'NezhaForTokenClassification',
'NezhaModel',
'NezhaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 362 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ (self : Dict):
A = tempfile.mkdtemp()
A = BlipImageProcessor()
A = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
A = BlipaProcessor(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
processor.save_pretrained(self.tmpdirname)
def SCREAMING_SNAKE_CASE__ (self : Dict , **__SCREAMING_SNAKE_CASE : Any):
return AutoProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE).tokenizer
def SCREAMING_SNAKE_CASE__ (self : Tuple , **__SCREAMING_SNAKE_CASE : int):
return AutoProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE).image_processor
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
shutil.rmtree(self.tmpdirname)
def SCREAMING_SNAKE_CASE__ (self : Any):
A = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta)]
A = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1)) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE__ (self : Any):
A = BlipaProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
A = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)")
A = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0)
A = BlipaProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer , __SCREAMING_SNAKE_CASE)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE)
def SCREAMING_SNAKE_CASE__ (self : Optional[Any]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = self.prepare_image_inputs()
A = image_processor(__SCREAMING_SNAKE_CASE , return_tensors="np")
A = processor(images=__SCREAMING_SNAKE_CASE , return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2)
def SCREAMING_SNAKE_CASE__ (self : Tuple):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = processor(text=__SCREAMING_SNAKE_CASE)
A = tokenizer(__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = self.prepare_image_inputs()
A = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE)
self.assertListEqual(list(inputs.keys()) , ["pixel_values", "input_ids", "attention_mask"])
# test if it raises when no input is passed
with pytest.raises(__SCREAMING_SNAKE_CASE):
processor()
def SCREAMING_SNAKE_CASE__ (self : List[Any]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A = processor.batch_decode(__SCREAMING_SNAKE_CASE)
A = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
def SCREAMING_SNAKE_CASE__ (self : Optional[int]):
A = self.get_image_processor()
A = self.get_tokenizer()
A = BlipaProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE)
A = "lower newer"
A = self.prepare_image_inputs()
A = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE)
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys()) , ["pixel_values", "input_ids", "attention_mask"])
| 57 | 0 |
"""simple docstring"""
from __future__ import annotations
_a = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class A_ :
'''simple docstring'''
def __init__( self , lowercase_ , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = graph
# mapping node to its parent in resulting breadth first tree
UpperCAmelCase_ : dict[str, str | None] = {}
UpperCAmelCase_ : Optional[Any] = source_vertex
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = {self.source_vertex}
UpperCAmelCase_ : Dict = None
UpperCAmelCase_ : Optional[Any] = [self.source_vertex] # first in first out queue
while queue:
UpperCAmelCase_ : int = queue.pop(0 )
for adjacent_vertex in self.graph[vertex]:
if adjacent_vertex not in visited:
visited.add(lowercase_ )
UpperCAmelCase_ : Dict = vertex
queue.append(lowercase_ )
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
if target_vertex == self.source_vertex:
return self.source_vertex
UpperCAmelCase_ : List[str] = self.parent.get(lowercase_ )
if target_vertex_parent is None:
UpperCAmelCase_ : str = (
F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
)
raise ValueError(lowercase_ )
return self.shortest_path(lowercase_ ) + F"""->{target_vertex}"""
if __name__ == "__main__":
_a = Graph(graph, 'G')
g.breath_first_search()
print(g.shortest_path('D'))
print(g.shortest_path('G'))
print(g.shortest_path('Foo'))
| 61 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
_a = None
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
_a = {
'vocab_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
},
'tokenizer_file': {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
},
}
_a = {
'google/fnet-base': 512,
'google/fnet-large': 512,
}
_a = '▁'
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["""input_ids""", """token_type_ids"""]
SCREAMING_SNAKE_CASE__ : Tuple = FNetTokenizer
def __init__( self , lowercase_=None , lowercase_=None , lowercase_=False , lowercase_=True , lowercase_=True , lowercase_="<unk>" , lowercase_="[SEP]" , lowercase_="<pad>" , lowercase_="[CLS]" , lowercase_="[MASK]" , **lowercase_ , ):
"""simple docstring"""
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
UpperCAmelCase_ : int = (
AddedToken(lowercase_ , lstrip=lowercase_ , rstrip=lowercase_ , normalized=lowercase_ )
if isinstance(lowercase_ , lowercase_ )
else mask_token
)
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , do_lower_case=lowercase_ , remove_space=lowercase_ , keep_accents=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , pad_token=lowercase_ , cls_token=lowercase_ , mask_token=lowercase_ , **lowercase_ , )
UpperCAmelCase_ : Any = do_lower_case
UpperCAmelCase_ : Tuple = remove_space
UpperCAmelCase_ : str = keep_accents
UpperCAmelCase_ : Any = vocab_file
UpperCAmelCase_ : List[Any] = False if not self.vocab_file else True
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = [self.sep_token_id]
UpperCAmelCase_ : Any = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ):
"""simple docstring"""
UpperCAmelCase_ : Any = [self.sep_token_id]
UpperCAmelCase_ : Any = [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 , lowercase_ , lowercase_ = None ):
"""simple docstring"""
if not os.path.isdir(lowercase_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ : List[str] = os.path.join(
lowercase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
return (out_vocab_file,)
| 61 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Dict:
_SCREAMING_SNAKE_CASE : Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , "tf_padding" ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , "depth_multiplier" ) )
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase=1_3 , __lowerCamelCase=3 , __lowerCamelCase=3_2 , __lowerCamelCase=0.25 , __lowerCamelCase=8 , __lowerCamelCase=8 , __lowerCamelCase=6 , __lowerCamelCase=3_2 , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase="relu6" , __lowerCamelCase=1_2_8_0 , __lowerCamelCase=0.1 , __lowerCamelCase=0.02 , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=1_0 , __lowerCamelCase=None , ) -> int:
_SCREAMING_SNAKE_CASE : Dict = parent
_SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
_SCREAMING_SNAKE_CASE : str = num_channels
_SCREAMING_SNAKE_CASE : Any = image_size
_SCREAMING_SNAKE_CASE : List[str] = depth_multiplier
_SCREAMING_SNAKE_CASE : Tuple = depth_divisible_by
_SCREAMING_SNAKE_CASE : Tuple = min_depth
_SCREAMING_SNAKE_CASE : int = expand_ratio
_SCREAMING_SNAKE_CASE : int = tf_padding
_SCREAMING_SNAKE_CASE : List[str] = output_stride
_SCREAMING_SNAKE_CASE : Union[str, Any] = first_layer_is_expansion
_SCREAMING_SNAKE_CASE : Optional[int] = finegrained_output
_SCREAMING_SNAKE_CASE : int = hidden_act
_SCREAMING_SNAKE_CASE : List[str] = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
_SCREAMING_SNAKE_CASE : Optional[Any] = classifier_dropout_prob
_SCREAMING_SNAKE_CASE : int = use_labels
_SCREAMING_SNAKE_CASE : str = is_training
_SCREAMING_SNAKE_CASE : Dict = num_labels
_SCREAMING_SNAKE_CASE : Any = initializer_range
_SCREAMING_SNAKE_CASE : Dict = scope
def UpperCamelCase_ ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE : List[str] = None
_SCREAMING_SNAKE_CASE : int = None
if self.use_labels:
_SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_SCREAMING_SNAKE_CASE : Optional[int] = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCamelCase_ ( self ) -> Optional[int]:
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
_SCREAMING_SNAKE_CASE : int = MobileNetVaModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
_SCREAMING_SNAKE_CASE : Union[str, Any] = model(__lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
_SCREAMING_SNAKE_CASE : str = self.num_labels
_SCREAMING_SNAKE_CASE : List[Any] = MobileNetVaForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
_SCREAMING_SNAKE_CASE : Tuple = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
_SCREAMING_SNAKE_CASE : Any = self.num_labels
_SCREAMING_SNAKE_CASE : Optional[int] = MobileNetVaForSemanticSegmentation(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
_SCREAMING_SNAKE_CASE : str = model(__lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
_SCREAMING_SNAKE_CASE : int = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCamelCase_ ( self ) -> str:
_SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE : List[Any] = config_and_inputs
_SCREAMING_SNAKE_CASE : List[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase__( __lowercase , __lowercase , unittest.TestCase ):
'''simple docstring'''
__snake_case = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
__snake_case = (
{
'feature-extraction': MobileNetVaModel,
'image-classification': MobileNetVaForImageClassification,
'image-segmentation': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__snake_case = False
__snake_case = False
__snake_case = False
__snake_case = False
def UpperCamelCase_ ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Tuple = MobileNetVaModelTester(self )
_SCREAMING_SNAKE_CASE : List[Any] = MobileNetVaConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase )
def UpperCamelCase_ ( self ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV2 does not use inputs_embeds" )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="MobileNetV2 does not support input and output embeddings" )
def UpperCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason="MobileNetV2 does not output attentions" )
def UpperCamelCase_ ( self ) -> Any:
pass
def UpperCamelCase_ ( self ) -> Any:
_SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Dict = model_class(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE : int = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def UpperCamelCase_ ( self ) -> Any:
_SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[str]:
def check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE : List[Any] = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : str = outputs.hidden_states
_SCREAMING_SNAKE_CASE : str = 1_6
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : List[str] = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE : int = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def UpperCamelCase_ ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def UpperCamelCase_ ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase )
@slow
def UpperCamelCase_ ( self ) -> List[Any]:
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE : Optional[Any] = MobileNetVaModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCAmelCase__( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self ) -> Tuple:
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224" ) if is_vision_available() else None
)
@slow
def UpperCamelCase_ ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : List[str] = MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224" ).to(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.default_image_processor
_SCREAMING_SNAKE_CASE : List[str] = prepare_img()
_SCREAMING_SNAKE_CASE : int = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[Any] = model(**__lowerCamelCase )
# verify the logits
_SCREAMING_SNAKE_CASE : Optional[int] = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([0.2445, -1.1993, 0.1905] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 ) )
@slow
def UpperCamelCase_ ( self ) -> Dict:
_SCREAMING_SNAKE_CASE : Tuple = MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
_SCREAMING_SNAKE_CASE : Dict = model.to(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = prepare_img()
_SCREAMING_SNAKE_CASE : Any = image_processor(images=__lowerCamelCase , return_tensors="pt" ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE : Optional[int] = model(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = outputs.logits
# verify the logits
_SCREAMING_SNAKE_CASE : int = torch.Size((1, 2_1, 6_5, 6_5) )
self.assertEqual(logits.shape , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = torch.tensor(
[
[[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
[[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
[[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
] , device=__lowerCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __lowerCamelCase , atol=1E-4 ) ) | 371 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__( __lowercase , __lowercase ):
'''simple docstring'''
@register_to_config
def __init__( self , __lowerCamelCase = 1_2_8 , __lowerCamelCase = 2_5_6 , __lowerCamelCase = 2000.0 , __lowerCamelCase = 7_6_8 , __lowerCamelCase = 1_2 , __lowerCamelCase = 1_2 , __lowerCamelCase = 6_4 , __lowerCamelCase = 2_0_4_8 , __lowerCamelCase = 0.1 , ) -> int:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[int] = nn.Sequential(
nn.Linear(__lowerCamelCase , d_model * 4 , bias=__lowerCamelCase ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__lowerCamelCase ) , nn.SiLU() , )
_SCREAMING_SNAKE_CASE : str = nn.Embedding(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = False
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(p=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList()
for lyr_num in range(__lowerCamelCase ):
# FiLM conditional T5 decoder
_SCREAMING_SNAKE_CASE : Optional[int] = DecoderLayer(d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase )
self.decoders.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = TaLayerNorm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = nn.Dropout(p=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
_SCREAMING_SNAKE_CASE : int = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_SCREAMING_SNAKE_CASE : Tuple = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_SCREAMING_SNAKE_CASE : str = self.conditioning_emb(__lowerCamelCase ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_SCREAMING_SNAKE_CASE : Tuple = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_SCREAMING_SNAKE_CASE : Optional[int] = torch.broadcast_to(
torch.arange(__lowerCamelCase , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.position_encoding(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.continuous_inputs_projection(__lowerCamelCase )
inputs += position_encodings
_SCREAMING_SNAKE_CASE : Any = self.dropout(__lowerCamelCase )
# decoder: No padding present.
_SCREAMING_SNAKE_CASE : Any = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_SCREAMING_SNAKE_CASE : List[str] = [(x, self.encoder_decoder_mask(__lowerCamelCase , __lowerCamelCase )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_SCREAMING_SNAKE_CASE : Tuple = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_SCREAMING_SNAKE_CASE : Optional[Any] = lyr(
__lowerCamelCase , conditioning_emb=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , encoder_attention_mask=__lowerCamelCase , )[0]
_SCREAMING_SNAKE_CASE : int = self.decoder_norm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = self.post_dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = self.spec_out(__lowerCamelCase )
return spec_out
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=1E-6 ) -> Dict:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[int] = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , dropout_rate=__lowerCamelCase ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__lowerCamelCase , d_kv=__lowerCamelCase , num_heads=__lowerCamelCase , dropout_rate=__lowerCamelCase , layer_norm_epsilon=__lowerCamelCase , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase , layer_norm_epsilon=__lowerCamelCase ) )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : int = self.layer[0](
__lowerCamelCase , conditioning_emb=__lowerCamelCase , attention_mask=__lowerCamelCase , )
if encoder_hidden_states is not None:
_SCREAMING_SNAKE_CASE : str = torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_SCREAMING_SNAKE_CASE : Tuple = self.layer[1](
__lowerCamelCase , key_value_states=__lowerCamelCase , attention_mask=__lowerCamelCase , )
# Apply Film Conditional Feed Forward layer
_SCREAMING_SNAKE_CASE : Optional[Any] = self.layer[-1](__lowerCamelCase , __lowerCamelCase )
return (hidden_states,)
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : List[str] = TaLayerNorm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = TaFiLMLayer(in_features=d_model * 4 , out_features=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = Attention(query_dim=__lowerCamelCase , heads=__lowerCamelCase , dim_head=__lowerCamelCase , out_bias=__lowerCamelCase , scale_qk=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , ) -> Union[str, Any]:
# pre_self_attention_layer_norm
_SCREAMING_SNAKE_CASE : int = self.layer_norm(__lowerCamelCase )
if conditioning_emb is not None:
_SCREAMING_SNAKE_CASE : Any = self.FiLMLayer(__lowerCamelCase , __lowerCamelCase )
# Self-attention block
_SCREAMING_SNAKE_CASE : Optional[int] = self.attention(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = hidden_states + self.dropout(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[str]:
super().__init__()
_SCREAMING_SNAKE_CASE : Optional[Any] = Attention(query_dim=__lowerCamelCase , heads=__lowerCamelCase , dim_head=__lowerCamelCase , out_bias=__lowerCamelCase , scale_qk=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = TaLayerNorm(__lowerCamelCase , eps=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , ) -> List[Any]:
_SCREAMING_SNAKE_CASE : Tuple = self.layer_norm(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.attention(
__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , attention_mask=attention_mask.squeeze(1 ) , )
_SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_states + self.dropout(__lowerCamelCase )
return layer_output
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> List[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Tuple = TaDenseGatedActDense(d_model=__lowerCamelCase , d_ff=__lowerCamelCase , dropout_rate=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = TaFiLMLayer(in_features=d_model * 4 , out_features=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = TaLayerNorm(__lowerCamelCase , eps=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.Dropout(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase=None ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = self.layer_norm(__lowerCamelCase )
if conditioning_emb is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.film(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = self.DenseReluDense(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = hidden_states + self.dropout(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = NewGELUActivation()
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Any:
_SCREAMING_SNAKE_CASE : Dict = self.act(self.wi_a(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : Dict = self.wi_a(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = hidden_gelu * hidden_linear
_SCREAMING_SNAKE_CASE : Optional[int] = self.dropout(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = self.wo(__lowerCamelCase )
return hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase=1E-6 ) -> int:
super().__init__()
_SCREAMING_SNAKE_CASE : Dict = nn.Parameter(torch.ones(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE : str = eps
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Optional[Any]:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_SCREAMING_SNAKE_CASE : Tuple = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_SCREAMING_SNAKE_CASE : str = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def UpperCamelCase_ ( self , __lowerCamelCase ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase , 3.0 )) ))
class lowerCAmelCase__( nn.Module ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE : Any = nn.Linear(__lowerCamelCase , out_features * 2 , bias=__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE : List[Any] = self.scale_bias(__lowerCamelCase )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = torch.chunk(__lowerCamelCase , 2 , -1 )
_SCREAMING_SNAKE_CASE : Optional[int] = x * (1 + scale) + shift
return x | 325 | 0 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
a ="""bert-base-cased"""
a ="""google/pegasus-xsum"""
a =[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a =["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a ="""patrickvonplaten/t5-tiny-random"""
a ="""sshleifer/bart-tiny-random"""
a ="""sshleifer/tiny-mbart"""
a ="""sshleifer/tiny-marian-en-de"""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
__lowerCamelCase : str = '\n'.join(lowerCamelCase__ )
Path(lowerCamelCase__ ).open('w' ).writelines(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Union[str, Any]:
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(lowerCamelCase__ , F"{split}.source" ) , lowerCamelCase__ )
_dump_articles(os.path.join(lowerCamelCase__ , F"{split}.target" ) , lowerCamelCase__ )
return tmp_dir
class A_ ( SCREAMING_SNAKE_CASE ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] ,)
@slow
def lowerCAmelCase ( self : Dict ,SCREAMING_SNAKE_CASE__ : Tuple):
__lowerCamelCase : Dict = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__lowerCamelCase : Union[str, Any] = max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__)) for a in ARTICLES)
__lowerCamelCase : List[str] = max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__)) for a in SUMMARIES)
__lowerCamelCase : str = 4
__lowerCamelCase : List[str] = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
__lowerCamelCase , __lowerCamelCase : Dict = 'ro_RO', 'de_DE' # ignored for all but mbart, but never causes error.
__lowerCamelCase : Dict = SeqaSeqDataset(
SCREAMING_SNAKE_CASE__ ,data_dir=SCREAMING_SNAKE_CASE__ ,type_path='train' ,max_source_length=SCREAMING_SNAKE_CASE__ ,max_target_length=SCREAMING_SNAKE_CASE__ ,src_lang=SCREAMING_SNAKE_CASE__ ,tgt_lang=SCREAMING_SNAKE_CASE__ ,)
__lowerCamelCase : Any = DataLoader(SCREAMING_SNAKE_CASE__ ,batch_size=2 ,collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
__lowerCamelCase : Union[str, Any] = shift_tokens_right(batch['labels'] ,tokenizer.pad_token_id)
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED])
def lowerCAmelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Any):
__lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
__lowerCamelCase : List[Any] = max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__)) for a in ARTICLES)
__lowerCamelCase : List[Any] = max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__)) for a in SUMMARIES)
__lowerCamelCase : Dict = 4
__lowerCamelCase : List[Any] = LegacySeqaSeqDataset(
SCREAMING_SNAKE_CASE__ ,data_dir=SCREAMING_SNAKE_CASE__ ,type_path='train' ,max_source_length=2_0 ,max_target_length=SCREAMING_SNAKE_CASE__ ,)
__lowerCamelCase : Optional[Any] = DataLoader(SCREAMING_SNAKE_CASE__ ,batch_size=2 ,collate_fn=train_dataset.collate_fn)
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 2_0 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def lowerCAmelCase ( self : List[str]):
__lowerCamelCase : int = AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')
__lowerCamelCase : int = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
__lowerCamelCase : Union[str, Any] = tmp_dir.joinpath('train.source').open().readlines()
__lowerCamelCase : str = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
pack_data_dir(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,1_2_8 ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : int = {x.name for x in tmp_dir.iterdir()}
__lowerCamelCase : Optional[int] = {x.name for x in save_dir.iterdir()}
__lowerCamelCase : int = save_dir.joinpath('train.source').open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(SCREAMING_SNAKE_CASE__) < len(SCREAMING_SNAKE_CASE__)
assert len(SCREAMING_SNAKE_CASE__) == 1
assert len(packed_examples[0]) == sum(len(SCREAMING_SNAKE_CASE__) for x in orig_examples)
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,reason='This test requires fairseq')
def lowerCAmelCase ( self : str):
if not FAIRSEQ_AVAILABLE:
return
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : str = self._get_dataset(max_len=6_4)
__lowerCamelCase : Dict = 6_4
__lowerCamelCase : str = ds.make_dynamic_sampler(SCREAMING_SNAKE_CASE__ ,required_batch_size_multiple=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : int = [len(SCREAMING_SNAKE_CASE__) for x in batch_sampler]
assert len(set(SCREAMING_SNAKE_CASE__)) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(SCREAMING_SNAKE_CASE__) == len(SCREAMING_SNAKE_CASE__) # no dropped or added examples
__lowerCamelCase : Union[str, Any] = DataLoader(SCREAMING_SNAKE_CASE__ ,batch_sampler=SCREAMING_SNAKE_CASE__ ,collate_fn=ds.collate_fn ,num_workers=2)
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : Tuple = []
for batch in data_loader:
__lowerCamelCase : Tuple = batch['input_ids'].shape
__lowerCamelCase : Dict = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
__lowerCamelCase : int = np.product(batch['input_ids'].shape)
num_src_per_batch.append(SCREAMING_SNAKE_CASE__)
if num_src_tokens > (max_tokens * 1.1):
failures.append(SCREAMING_SNAKE_CASE__)
assert num_src_per_batch[0] == max(SCREAMING_SNAKE_CASE__)
if failures:
raise AssertionError(F"too many tokens in {len(SCREAMING_SNAKE_CASE__)} batches")
def lowerCAmelCase ( self : Any):
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Dict = self._get_dataset(max_len=5_1_2)
__lowerCamelCase : Optional[int] = 2
__lowerCamelCase : Dict = ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__ ,shuffle=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = DataLoader(SCREAMING_SNAKE_CASE__ ,batch_size=SCREAMING_SNAKE_CASE__ ,collate_fn=ds.collate_fn ,num_workers=2)
__lowerCamelCase : Any = DataLoader(SCREAMING_SNAKE_CASE__ ,batch_size=SCREAMING_SNAKE_CASE__ ,collate_fn=ds.collate_fn ,num_workers=2 ,sampler=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = tokenizer.pad_token_id
def count_pad_tokens(SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Dict="input_ids"):
return [batch[k].eq(SCREAMING_SNAKE_CASE__).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__ ,k='labels')) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__ ,k='labels'))
assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__)) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__))
assert len(SCREAMING_SNAKE_CASE__) == len(SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : str=1_0_0_0 ,SCREAMING_SNAKE_CASE__ : Dict=1_2_8):
if os.getenv('USE_REAL_DATA' ,SCREAMING_SNAKE_CASE__):
__lowerCamelCase : int = 'examples/seq2seq/wmt_en_ro'
__lowerCamelCase : str = max_len * 2 * 6_4
if not Path(SCREAMING_SNAKE_CASE__).joinpath('train.len').exists():
save_len_file(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
else:
__lowerCamelCase : Dict = 'examples/seq2seq/test_data/wmt_en_ro'
__lowerCamelCase : Any = max_len * 4
save_len_file(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : int = SeqaSeqDataset(
SCREAMING_SNAKE_CASE__ ,data_dir=SCREAMING_SNAKE_CASE__ ,type_path='train' ,max_source_length=SCREAMING_SNAKE_CASE__ ,max_target_length=SCREAMING_SNAKE_CASE__ ,n_obs=SCREAMING_SNAKE_CASE__ ,)
return ds, max_tokens, tokenizer
def lowerCAmelCase ( self : str):
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : List[str] = self._get_dataset()
__lowerCamelCase : List[str] = set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__ ,2_5_6 ,num_replicas=2 ,rank=0 ,add_extra_examples=SCREAMING_SNAKE_CASE__))
__lowerCamelCase : Any = set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__ ,2_5_6 ,num_replicas=2 ,rank=1 ,add_extra_examples=SCREAMING_SNAKE_CASE__))
assert idsa.intersection(SCREAMING_SNAKE_CASE__) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] ,)
def lowerCAmelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Optional[Any]):
__lowerCamelCase : str = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ,use_fast=SCREAMING_SNAKE_CASE__)
if tok_name == MBART_TINY:
__lowerCamelCase : List[str] = SeqaSeqDataset(
SCREAMING_SNAKE_CASE__ ,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) ,type_path='train' ,max_source_length=4 ,max_target_length=8 ,src_lang='EN' ,tgt_lang='FR' ,)
__lowerCamelCase : List[Any] = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
__lowerCamelCase : Union[str, Any] = SeqaSeqDataset(
SCREAMING_SNAKE_CASE__ ,data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()) ,type_path='train' ,max_source_length=4 ,max_target_length=8 ,)
__lowerCamelCase : Dict = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(SCREAMING_SNAKE_CASE__) == 1 if tok_name == BART_TINY else len(SCREAMING_SNAKE_CASE__) == 0
| 73 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A_ :
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : list[tuple[float, float]]):
__lowerCamelCase : Union[str, Any] = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__lowerCamelCase : int = len(SCREAMING_SNAKE_CASE__) - 1
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : float):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowerCamelCase : list[float] = []
for i in range(len(self.list_of_points)):
# basis function for each i
output_values.append(
comb(self.degree ,SCREAMING_SNAKE_CASE__) * ((1 - t) ** (self.degree - i)) * (t**i))
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(SCREAMING_SNAKE_CASE__) ,5) == 1
return output_values
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : float):
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__lowerCamelCase : Tuple = self.basis_function(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = 0.0
__lowerCamelCase : Optional[Any] = 0.0
for i in range(len(self.list_of_points)):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def lowerCAmelCase ( self : int ,SCREAMING_SNAKE_CASE__ : float = 0.01):
from matplotlib import pyplot as plt # type: ignore
__lowerCamelCase : list[float] = [] # x coordinates of points to plot
__lowerCamelCase : list[float] = [] # y coordinates of points to plot
__lowerCamelCase : Any = 0.0
while t <= 1:
__lowerCamelCase : List[Any] = self.bezier_curve_function(SCREAMING_SNAKE_CASE__)
to_plot_x.append(value[0])
to_plot_y.append(value[1])
t += step_size
__lowerCamelCase : Optional[Any] = [i[0] for i in self.list_of_points]
__lowerCamelCase : List[str] = [i[1] for i in self.list_of_points]
plt.plot(
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,color='blue' ,label='Curve of Degree ' + str(self.degree) ,)
plt.scatter(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,color='red' ,label='Control Points')
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 73 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowercase : Any = logging.get_logger(__name__)
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase) -> None:
'''simple docstring'''
warnings.warn(
'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use ImageGPTImageProcessor instead.' , lowercase , )
super().__init__(*lowercase , **lowercase)
| 225 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
lowercase : str = logging.get_logger(__name__)
lowercase : Optional[int] = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowercase : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A__ :
"""simple docstring"""
__A : str = field(
default=__UpperCAmelCase , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(__UpperCAmelCase )} )
__A : str = field(
default=__UpperCAmelCase , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
__A : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__A : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
__A : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
__A : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
__A : bool = field(
default=__UpperCAmelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
__A : bool = field(
default=__UpperCAmelCase , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
__A : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
__A : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
__A : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
__A : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : Dict = '''train'''
__A : Tuple = '''dev'''
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
__A : SquadDataTrainingArguments
__A : List[SquadFeatures]
__A : Split
__A : bool
def __init__( self , lowercase , lowercase , lowercase = None , lowercase = Split.train , lowercase = False , lowercase = None , lowercase = "pt" , ) -> str:
'''simple docstring'''
a__ : List[str] = args
a__ : Any = is_language_sensitive
a__ : Optional[Any] = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(lowercase , lowercase):
try:
a__ : Optional[int] = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name')
a__ : List[Any] = mode
# Load data features from cache or dataset file
a__ : List[str] = 'v2' if args.version_2_with_negative else 'v1'
a__ : Optional[Any] = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
a__ : List[Any] = cached_features_file + '.lock'
with FileLock(lowercase):
if os.path.exists(lowercase) and not args.overwrite_cache:
a__ : List[Any] = time.time()
a__ : Optional[Any] = torch.load(lowercase)
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
a__ : List[str] = self.old_features['features']
a__ : int = self.old_features.get('dataset' , lowercase)
a__ : Dict = self.old_features.get('examples' , lowercase)
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start)
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
' future run')
else:
if mode == Split.dev:
a__ : Dict = self.processor.get_dev_examples(args.data_dir)
else:
a__ : int = self.processor.get_train_examples(args.data_dir)
a__ , a__ : Any = squad_convert_examples_to_features(
examples=self.examples , tokenizer=lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=lowercase , )
a__ : Tuple = time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples} , lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]')
def __len__( self) -> Tuple:
'''simple docstring'''
return len(self.features)
def __getitem__( self , lowercase) -> Dict[str, torch.Tensor]:
'''simple docstring'''
a__ : Union[str, Any] = self.features[i]
a__ : Optional[int] = torch.tensor(feature.input_ids , dtype=torch.long)
a__ : Tuple = torch.tensor(feature.attention_mask , dtype=torch.long)
a__ : Optional[Any] = torch.tensor(feature.token_type_ids , dtype=torch.long)
a__ : str = torch.tensor(feature.cls_index , dtype=torch.long)
a__ : Optional[Any] = torch.tensor(feature.p_mask , dtype=torch.float)
a__ : Tuple = torch.tensor(feature.is_impossible , dtype=torch.float)
a__ : str = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask})
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible})
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape , dtype=torch.intaa) * self.args.lang_id)})
if self.mode == Split.train:
a__ : Dict = torch.tensor(feature.start_position , dtype=torch.long)
a__ : str = torch.tensor(feature.end_position , dtype=torch.long)
inputs.update({'start_positions': start_positions, 'end_positions': end_positions})
return inputs
| 225 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase):
def __A (self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __A (self ) -> Optional[Any]:
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
_lowercase ='''xvjiarui/stable-diffusion-2-inpainting'''
_lowercase , _lowercase =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase , safety_checker=UpperCAmelCase )
_lowercase ='''Face of a yellow cat, high resolution, sitting on a park bench'''
_lowercase =jax.random.PRNGKey(0 )
_lowercase =5_0
_lowercase =jax.device_count()
_lowercase =num_samples * [prompt]
_lowercase =num_samples * [init_image]
_lowercase =num_samples * [mask_image]
_lowercase , _lowercase , _lowercase =pipeline.prepare_inputs(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# shard inputs and rng
_lowercase =replicate(UpperCAmelCase )
_lowercase =jax.random.split(UpperCAmelCase , jax.device_count() )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =pipeline(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , jit=UpperCAmelCase )
_lowercase =output.images.reshape(UpperCAmelCase , 5_1_2 , 5_1_2 , 3 )
_lowercase =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
_lowercase =jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase =jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 5 | """simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case :
"""simple docstring"""
snake_case__ = 42
snake_case__ = None
snake_case__ = None
lowerCAmelCase__ : Union[str, Any] = namedtuple('CoinsDistribResult', 'moves excess')
def a_ ( lowerCamelCase ):
if root is None:
return 0
# Validation
def count_nodes(lowerCamelCase ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(lowerCamelCase ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(lowerCamelCase ) != count_coins(lowerCamelCase ):
raise ValueError('The nodes number should be same as the number of coins' )
# Main calculation
def get_distrib(lowerCamelCase ) -> 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(lowerCamelCase )
+ abs(lowerCamelCase )
)
UpperCAmelCase__ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(lowerCamelCase , lowerCamelCase )
return get_distrib(lowerCamelCase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 98 | 0 |
'''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_ ( A_ ):
'''simple docstring'''
def __init__( self : str , lowercase_ : Tuple , lowercase_ : int=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[int]=True , lowercase_ : List[Any]=True , lowercase_ : Optional[Any]=True , lowercase_ : str=True , lowercase_ : List[str]=99 , lowercase_ : str=32 , lowercase_ : List[Any]=5 , lowercase_ : List[Any]=4 , lowercase_ : Dict=37 , lowercase_ : Union[str, Any]="gelu" , lowercase_ : Tuple=0.1 , lowercase_ : Any=0.1 , lowercase_ : Optional[int]=512 , lowercase_ : Union[str, Any]=16 , lowercase_ : Any=2 , lowercase_ : Dict=0.02 , lowercase_ : Any=False , lowercase_ : Union[str, Any]=True , lowercase_ : List[Any]="None" , lowercase_ : Any=3 , lowercase_ : Optional[int]=4 , lowercase_ : List[Any]=None , ) -> Any:
UpperCAmelCase : List[Any] = parent
UpperCAmelCase : Tuple = batch_size
UpperCAmelCase : List[Any] = seq_length
UpperCAmelCase : str = 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 : List[str] = hidden_size
UpperCAmelCase : Tuple = num_hidden_layers
UpperCAmelCase : Union[str, Any] = num_attention_heads
UpperCAmelCase : Tuple = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : Dict = hidden_dropout_prob
UpperCAmelCase : Any = attention_probs_dropout_prob
UpperCAmelCase : List[str] = max_position_embeddings
UpperCAmelCase : Dict = type_vocab_size
UpperCAmelCase : Any = type_sequence_label_size
UpperCAmelCase : int = initializer_range
UpperCAmelCase : Any = num_labels
UpperCAmelCase : List[Any] = num_choices
UpperCAmelCase : Dict = relative_attention
UpperCAmelCase : Optional[int] = position_biased_input
UpperCAmelCase : str = pos_att_type
UpperCAmelCase : str = scope
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Optional[int] = None
if self.use_input_mask:
UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
UpperCAmelCase : List[str] = None
if self.use_token_type_ids:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase : List[Any] = None
UpperCAmelCase : Any = None
UpperCAmelCase : Tuple = None
if self.use_labels:
UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self : Dict ) -> Any:
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 : Union[str, Any] ) -> Any:
UpperCAmelCase : int = self.get_config()
UpperCAmelCase : Tuple = 300
return config
def UpperCAmelCase_ ( self : int , lowercase_ : int ) -> List[Any]:
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def UpperCAmelCase_ ( self : List[str] , lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> str:
UpperCAmelCase : Optional[Any] = DebertaModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : str = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )[0]
UpperCAmelCase : List[str] = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )[0]
UpperCAmelCase : Any = model(_lowerCamelCase )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def UpperCAmelCase_ ( self : str , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : int ) -> Optional[Any]:
UpperCAmelCase : Union[str, Any] = DebertaForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : List[str] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self : Tuple , lowercase_ : Dict , lowercase_ : int , lowercase_ : Any , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : int ) -> List[Any]:
UpperCAmelCase : Optional[Any] = self.num_labels
UpperCAmelCase : str = DebertaForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : Any = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(_lowerCamelCase )
def UpperCAmelCase_ ( self : Dict , lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Optional[int] ) -> List[Any]:
UpperCAmelCase : List[Any] = self.num_labels
UpperCAmelCase : Optional[int] = DebertaForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : List[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self : Tuple , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : int , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : str ) -> Optional[int]:
UpperCAmelCase : str = DebertaForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
UpperCAmelCase : Any = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) : List[Any] = config_and_inputs
UpperCAmelCase : Dict = {'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 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ : Dict = (
{
"""feature-extraction""": DebertaModel,
"""fill-mask""": DebertaForMaskedLM,
"""question-answering""": DebertaForQuestionAnswering,
"""text-classification""": DebertaForSequenceClassification,
"""token-classification""": DebertaForTokenClassification,
"""zero-shot""": DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ : Dict = True
UpperCAmelCase_ : Optional[Any] = False
UpperCAmelCase_ : List[str] = False
UpperCAmelCase_ : int = False
UpperCAmelCase_ : Optional[int] = False
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
UpperCAmelCase : List[str] = DebertaModelTester(self )
UpperCAmelCase : List[Any] = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowerCamelCase )
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*_lowerCamelCase )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*_lowerCamelCase )
def UpperCAmelCase_ ( self : str ) -> Any:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*_lowerCamelCase )
@slow
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase : Union[str, Any] = DebertaModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class A_ ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
pass
@slow
def UpperCAmelCase_ ( self : int ) -> str:
UpperCAmelCase : Dict = DebertaModel.from_pretrained('microsoft/deberta-base' )
UpperCAmelCase : Optional[int] = torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
UpperCAmelCase : Tuple = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase : str = model(_lowerCamelCase , attention_mask=_lowerCamelCase )[0]
# compare the actual values for a slice.
UpperCAmelCase : Tuple = torch.tensor(
[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCamelCase , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 368 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import PoolFormerImageProcessor
class A_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any , lowercase_ : str , lowercase_ : Union[str, Any]=7 , lowercase_ : Union[str, Any]=3 , lowercase_ : int=30 , lowercase_ : Tuple=400 , lowercase_ : Tuple=True , lowercase_ : Optional[int]=None , lowercase_ : List[str]=0.9 , lowercase_ : Tuple=None , lowercase_ : Union[str, Any]=True , lowercase_ : int=[0.5, 0.5, 0.5] , lowercase_ : List[str]=[0.5, 0.5, 0.5] , ) -> Tuple:
UpperCAmelCase : Optional[int] = size if size is not None else {'shortest_edge': 30}
UpperCAmelCase : int = crop_size if crop_size is not None else {'height': 30, 'width': 30}
UpperCAmelCase : Tuple = parent
UpperCAmelCase : Optional[Any] = batch_size
UpperCAmelCase : int = num_channels
UpperCAmelCase : int = min_resolution
UpperCAmelCase : Optional[int] = max_resolution
UpperCAmelCase : str = do_resize_and_center_crop
UpperCAmelCase : int = size
UpperCAmelCase : Dict = crop_pct
UpperCAmelCase : Union[str, Any] = crop_size
UpperCAmelCase : Optional[int] = do_normalize
UpperCAmelCase : Optional[Any] = image_mean
UpperCAmelCase : Optional[Any] = image_std
def UpperCAmelCase_ ( self : str ) -> int:
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class A_ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = PoolFormerImageProcessor if is_vision_available() else None
def UpperCAmelCase_ ( self : Dict ) -> str:
UpperCAmelCase : Any = PoolFormerImageProcessingTester(self )
@property
def UpperCAmelCase_ ( self : List[Any] ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ ( self : Tuple ) -> str:
UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ , 'do_resize_and_center_crop' ) )
self.assertTrue(hasattr(lowercase_ , 'size' ) )
self.assertTrue(hasattr(lowercase_ , 'crop_pct' ) )
self.assertTrue(hasattr(lowercase_ , 'do_normalize' ) )
self.assertTrue(hasattr(lowercase_ , 'image_mean' ) )
self.assertTrue(hasattr(lowercase_ , 'image_std' ) )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 30} )
self.assertEqual(image_processor.crop_size , {'height': 30, 'width': 30} )
UpperCAmelCase : Any = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> str:
pass
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
# Initialize image_processing
UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , Image.Image )
# Test not batched input
UpperCAmelCase : Dict = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
UpperCAmelCase : str = image_processing(lowercase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
# Initialize image_processing
UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , np.ndarray )
# Test not batched input
UpperCAmelCase : Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
UpperCAmelCase : Optional[Any] = image_processing(lowercase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCAmelCase_ ( self : str ) -> Dict:
# Initialize image_processing
UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ , torch.Tensor )
# Test not batched input
UpperCAmelCase : int = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
UpperCAmelCase : Optional[int] = image_processing(lowercase_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 280 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __A ( lowerCAmelCase__ ):
a__ : torch.FloatTensor
a__ : Optional[torch.FloatTensor] = None
def lowerCAmelCase_ ( snake_case_ : List[str] , snake_case_ : str=0.999 , snake_case_ : Any="cosine" , ) -> List[str]:
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case_ : Tuple ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case_ : Tuple ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCAmelCase_ = []
for i in range(_SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ = i / num_diffusion_timesteps
UpperCAmelCase_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.floataa )
class __A ( lowerCAmelCase__ , lowerCAmelCase__ ):
@register_to_config
def __init__(self : str , __a : Dict = 1000 , __a : Optional[Any] = "fixed_small_log" , __a : int = True , __a : List[Any] = 1.0 , __a : Any = "epsilon" , __a : Dict = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
UpperCAmelCase_ = betas_for_alpha_bar(UpperCamelCase__ )
UpperCAmelCase_ = 1.0 - self.betas
UpperCAmelCase_ = torch.cumprod(self.alphas , dim=0 )
UpperCAmelCase_ = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCAmelCase_ = 1.0
# setable values
UpperCAmelCase_ = None
UpperCAmelCase_ = torch.from_numpy(np.arange(0 , UpperCamelCase__ )[::-1].copy() )
UpperCAmelCase_ = variance_type
def _lowercase (self : List[str] , __a : int , __a : Tuple = None ):
return sample
def _lowercase (self : Optional[int] , __a : List[str] , __a : Optional[Any] = None ):
UpperCAmelCase_ = num_inference_steps
UpperCAmelCase_ = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCAmelCase_ = (np.arange(0 , UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCAmelCase_ = torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
def _lowercase (self : str , __a : Dict , __a : List[Any]=None , __a : Any=None , __a : Any=None ):
if prev_timestep is None:
UpperCAmelCase_ = t - 1
UpperCAmelCase_ = self.alphas_cumprod[t]
UpperCAmelCase_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase_ = 1 - alpha_prod_t
UpperCAmelCase_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase_ = self.betas[t]
else:
UpperCAmelCase_ = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
UpperCAmelCase_ = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCAmelCase_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCAmelCase_ = torch.log(torch.clamp(UpperCamelCase__ , min=1E-20 ) )
UpperCAmelCase_ = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCAmelCase_ = variance.log()
UpperCAmelCase_ = beta.log()
UpperCAmelCase_ = (predicted_variance + 1) / 2
UpperCAmelCase_ = frac * max_log + (1 - frac) * min_log
return variance
def _lowercase (self : int , __a : List[Any] , __a : Dict , __a : Any , __a : str = None , __a : Dict=None , __a : Dict = True , ):
UpperCAmelCase_ = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCAmelCase_ = torch.split(UpperCamelCase__ , sample.shape[1] , dim=1 )
else:
UpperCAmelCase_ = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCAmelCase_ = t - 1
UpperCAmelCase_ = self.alphas_cumprod[t]
UpperCAmelCase_ = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCAmelCase_ = 1 - alpha_prod_t
UpperCAmelCase_ = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCAmelCase_ = self.betas[t]
UpperCAmelCase_ = self.alphas[t]
else:
UpperCAmelCase_ = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCAmelCase_ = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase_ = model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase_ = torch.clamp(
UpperCamelCase__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_ = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCAmelCase_ = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
UpperCAmelCase_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCAmelCase_ = 0
if t > 0:
UpperCAmelCase_ = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=UpperCamelCase__ , device=model_output.device )
UpperCAmelCase_ = self._get_variance(
UpperCamelCase__ , predicted_variance=UpperCamelCase__ , prev_timestep=UpperCamelCase__ , )
if self.variance_type == "fixed_small_log":
UpperCAmelCase_ = variance
elif self.variance_type == "learned_range":
UpperCAmelCase_ = (0.5 * variance).exp()
else:
raise ValueError(
f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
" for the UnCLIPScheduler." )
UpperCAmelCase_ = variance * variance_noise
UpperCAmelCase_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__ , pred_original_sample=UpperCamelCase__ )
def _lowercase (self : int , __a : Dict , __a : Optional[int] , __a : List[Any] , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
UpperCAmelCase_ = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCAmelCase_ = timesteps.to(original_samples.device )
UpperCAmelCase_ = alphas_cumprod[timesteps] ** 0.5
UpperCAmelCase_ = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase_ = sqrt_alpha_prod.unsqueeze(-1 )
UpperCAmelCase_ = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCAmelCase_ = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCAmelCase_ = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCAmelCase_ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
SCREAMING_SNAKE_CASE__ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 48 | 0 |
"""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
lowercase_ = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self , **_a ):
super().__init__(**_a )
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(_a )
def __UpperCAmelCase ( self , **_a ):
__a = {}
__a = {}
__a = {}
# preprocess args
if "points_per_batch" in kwargs:
__a = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
__a = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
__a = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
__a = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
__a = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
__a = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
__a = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
__a = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
__a = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
__a = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
__a = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
__a = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self , _a , *_a , _a=None , _a=None , **_a ):
return super().__call__(_a , *_a , num_workers=_a , batch_size=_a , **_a )
def __UpperCAmelCase ( self , _a , _a=64 , _a = 0 , _a = 512 / 1_500 , _a = 32 , _a = 1 , ):
__a = load_image(_a )
__a = self.image_processor.size['longest_edge']
__a = self.image_processor.generate_crop_boxes(
_a , _a , _a , _a , _a , _a )
__a = self.image_processor(images=_a , return_tensors='''pt''' )
with self.device_placement():
if self.framework == "pt":
__a = self.get_inference_context()
with inference_context():
__a = self._ensure_tensor_on_device(_a , device=self.device )
__a = self.model.get_image_embeddings(model_inputs.pop('''pixel_values''' ) )
__a = image_embeddings
__a = grid_points.shape[1]
__a = 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 , _a , _a ):
__a = grid_points[:, i : i + points_per_batch, :, :]
__a = input_labels[:, i : i + points_per_batch]
__a = 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 __UpperCAmelCase ( self , _a , _a=0.88 , _a=0.95 , _a=0 , _a=1 , ):
__a = model_inputs.pop('''input_boxes''' )
__a = model_inputs.pop('''is_last''' )
__a = model_inputs.pop('''original_sizes''' ).tolist()
__a = model_inputs.pop('''reshaped_input_sizes''' ).tolist()
__a = self.model(**_a )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
__a = model_outputs['pred_masks']
__a = self.image_processor.post_process_masks(
_a , _a , _a , _a , binarize=_a )
__a = model_outputs['iou_scores']
__a = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , _a , _a , _a , _a , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def __UpperCAmelCase ( self , _a , _a=False , _a=False , _a=0.7 , ):
__a = []
__a = []
__a = []
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''' ) )
__a = torch.cat(_a )
__a = torch.cat(_a )
__a = self.image_processor.post_process_for_mask_generation(
_a , _a , _a , _a )
__a = defaultdict(_a )
for output in model_outputs:
for k, v in output.items():
extra[k].append(_a )
__a = {}
if output_rle_mask:
__a = rle_mask
if output_bboxes_mask:
__a = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 358 |
"""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_mbart import MBartTokenizer
else:
lowercase_ = None
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
lowercase_ = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
lowercase_ = {
"facebook/mbart-large-en-ro": 1_0_2_4,
"facebook/mbart-large-cc25": 1_0_2_4,
}
# fmt: off
lowercase_ = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
__UpperCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Tuple = ['input_ids', 'attention_mask']
__UpperCAmelCase : Optional[Any] = MBartTokenizer
__UpperCAmelCase : List[int] = []
__UpperCAmelCase : 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 , ):
# Mask token behave like a normal word, i.e. include the space before it
__a = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
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 , **_a , )
__a = vocab_file
__a = False if not self.vocab_file else True
__a = 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} )
__a = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
__a = src_lang if src_lang is not None else '''en_XX'''
__a = self.convert_tokens_to_ids(self._src_lang )
__a = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __UpperCAmelCase ( self ):
return self._src_lang
@src_lang.setter
def __UpperCAmelCase ( self , _a ):
__a = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __UpperCAmelCase ( self , _a , _a = None ):
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 ):
__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 + sep + token_ids_a + sep ) * [0]
def __UpperCAmelCase ( self , _a , _a , _a , _a , **_a ):
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
__a = src_lang
__a = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
__a = self.convert_tokens_to_ids(_a )
__a = tgt_lang_id
return inputs
def __UpperCAmelCase ( self , _a , _a = "en_XX" , _a = None , _a = "ro_RO" , **_a , ):
__a = src_lang
__a = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def __UpperCAmelCase ( self ):
return self.set_src_lang_special_tokens(self.src_lang )
def __UpperCAmelCase ( self ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __UpperCAmelCase ( self , _a ):
__a = self.convert_tokens_to_ids(_a )
__a = []
__a = [self.eos_token_id, self.cur_lang_code]
__a = self.convert_ids_to_tokens(self.prefix_tokens )
__a = self.convert_ids_to_tokens(self.suffix_tokens )
__a = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __UpperCAmelCase ( self , _a ):
__a = self.convert_tokens_to_ids(_a )
__a = []
__a = [self.eos_token_id, self.cur_lang_code]
__a = self.convert_ids_to_tokens(self.prefix_tokens )
__a = self.convert_ids_to_tokens(self.suffix_tokens )
__a = processors.TemplateProcessing(
single=prefix_tokens_str + ['''$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 ):
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
__a = 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,)
| 11 | 0 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ):
_A : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""",type=snake_case_,default="""biencoder-nq-dev.json""",help="""Path to raw DPR training data""",)
parser.add_argument(
"""--evaluation_set""",type=snake_case_,help="""where to store parsed evaluation_set file""",)
parser.add_argument(
"""--gold_data_path""",type=snake_case_,help="""where to store parsed gold_data_path file""",)
_A : str = parser.parse_args()
with open(args.src_path,"""r""" ) as src_file, open(args.evaluation_set,"""w""" ) as eval_file, open(
args.gold_data_path,"""w""" ) as gold_file:
_A : List[Any] = json.load(snake_case_ )
for dpr_record in tqdm(snake_case_ ):
_A : Union[str, Any] = dpr_record["""question"""]
_A : List[str] = [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
eval_file.write(question + """\n""" )
gold_file.write("""\t""".join(snake_case_ ) + """\n""" )
if __name__ == "__main__":
main()
| 26 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
A : Tuple = logging.get_logger(__name__)
A : Tuple = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
A : Optional[Any] = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location="cpu" )
return sd
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=rename_keys_prefix ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict()
__lowerCAmelCase = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
__lowerCAmelCase = key
for name_pair in rename_keys_prefix:
__lowerCAmelCase = new_key.replace(name_pair[0] , name_pair[1] )
__lowerCAmelCase = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
__lowerCAmelCase = new_d["cls.predictions.bias"]
return new_d
@torch.no_grad()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
assert (
checkpoint_path.split("/" )[-1] in ACCEPTABLE_CHECKPOINTS
), f"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."
# Get Config
if "pre" in checkpoint_path:
__lowerCAmelCase = "pretraining"
if "vcr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 512}
elif "vqa_advanced" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
elif "vqa" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
elif "nlvr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 1024}
else:
raise NotImplementedError(f"No implementation found for `{checkpoint_path}`." )
else:
if "vcr" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 512}
__lowerCAmelCase = "multichoice"
elif "vqa_advanced" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048}
__lowerCAmelCase = "vqa_advanced"
elif "vqa" in checkpoint_path:
__lowerCAmelCase = {"visual_embedding_dim": 2048, "num_labels": 3129}
__lowerCAmelCase = "vqa"
elif "nlvr" in checkpoint_path:
__lowerCAmelCase = {
"visual_embedding_dim": 1024,
"num_labels": 2,
}
__lowerCAmelCase = "nlvr"
__lowerCAmelCase = VisualBertConfig(**_UpperCamelCase )
# Load State Dict
__lowerCAmelCase = load_state_dict(_UpperCamelCase )
__lowerCAmelCase = get_new_dict(_UpperCamelCase , _UpperCamelCase )
if model_type == "pretraining":
__lowerCAmelCase = VisualBertForPreTraining(_UpperCamelCase )
elif model_type == "vqa":
__lowerCAmelCase = VisualBertForQuestionAnswering(_UpperCamelCase )
elif model_type == "nlvr":
__lowerCAmelCase = VisualBertForVisualReasoning(_UpperCamelCase )
elif model_type == "multichoice":
__lowerCAmelCase = VisualBertForMultipleChoice(_UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
# Save Checkpoints
Path(_UpperCamelCase ).mkdir(exist_ok=_UpperCamelCase )
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
A : Optional[int] = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 57 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a_ : Union[str, Any] =CLIPTokenizer
a_ : int =CLIPTokenizerFast
a_ : Optional[Any] =True
a_ : Any ={}
a_ : Dict =False
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
super().setUp()
# fmt: off
_snake_case : List[Any] = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_snake_case : Tuple = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
_snake_case : str = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
_snake_case : Optional[int] = {"""unk_token""": """<unk>"""}
_snake_case : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_snake_case : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__UpperCAmelCase ) )
def UpperCamelCase_ ( self : Optional[Any] , **UpperCamelCase : List[Any] ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCamelCase_ ( self : str , **UpperCamelCase : Dict ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCamelCase_ ( self : List[Any] , UpperCamelCase : Any ):
'''simple docstring'''
_snake_case : Optional[int] = """lower newer"""
_snake_case : Tuple = """lower newer"""
return input_text, output_text
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Optional[int] = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_snake_case : Union[str, Any] = """lower newer"""
_snake_case : int = ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
_snake_case : int = tokenizer.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
_snake_case : Dict = tokens + [tokenizer.unk_token]
_snake_case : Tuple = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase ) , __UpperCAmelCase )
@require_ftfy
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_snake_case : Optional[Any] = self.tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
_snake_case : Tuple = self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
_snake_case : Optional[int] = """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
_snake_case : Optional[Any] = tokenizer_s.tokenize(__UpperCAmelCase )
_snake_case : Optional[Any] = tokenizer_r.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
_snake_case : Dict = """xa\u0303y""" + """ """ + """x\xe3y"""
_snake_case : int = tokenizer_s.tokenize(__UpperCAmelCase )
_snake_case : Dict = tokenizer_r.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
# Test that the tokenization is identical on unicode of space type
_snake_case : int = [
"""\u0009""", # (horizontal tab, '\t')
"""\u000B""", # (vertical tab)
"""\u000C""", # (form feed)
"""\u0020""", # (space, ' ')
"""\u200E""", # (left-to-right mark):w
"""\u200F""", # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
_snake_case : Optional[int] = tokenizer_s.tokenize(__UpperCAmelCase )
_snake_case : Any = tokenizer_r.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
# Test that the tokenization is identical on unicode of line break type
_snake_case : Union[str, Any] = [
"""\u000A""", # (line feed, '\n')
"""\r\n""", # (carriage return and line feed, '\r\n')
"""\u000D""", # (carriage return, '\r')
"""\r""", # (carriage return, '\r')
"""\u000D""", # (carriage return, '\r')
"""\u2028""", # (line separator)
"""\u2029""", # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
_snake_case : str = tokenizer_s.tokenize(__UpperCAmelCase )
_snake_case : int = tokenizer_r.tokenize(__UpperCAmelCase )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_snake_case : Optional[Any] = """hello""" # `hello` is a token in the vocabulary of `pretrained_name`
_snake_case : str = f"""{text_of_1_token} {text_of_1_token}"""
_snake_case : Dict = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , )
_snake_case : Optional[Any] = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__UpperCAmelCase ) + 1, len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
_snake_case : Any = f""" {text}"""
_snake_case : Optional[int] = self.rust_tokenizer_class.from_pretrained(
__UpperCAmelCase , use_fast=__UpperCAmelCase , )
_snake_case : Dict = tokenizer_r(__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__UpperCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__UpperCAmelCase ) + 1, 1 + len(__UpperCAmelCase ) + 1 + len(__UpperCAmelCase )) , )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
with self.assertRaises(__UpperCAmelCase ) as context:
self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer' )
self.assertTrue(
context.exception.args[0].startswith(
'The `backend_tokenizer` provided does not match the expected format.' ) )
@require_ftfy
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
super().test_tokenization_python_rust_equals()
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
pass
| 366 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase_ = 111_4112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0xE_000
lowerCAmelCase_ = 0xE_001
lowerCAmelCase_ = 0xE_002
lowerCAmelCase_ = 0xE_003
lowerCAmelCase_ = 0xE_004
# Maps special codepoints to human-readable names.
lowerCAmelCase_ = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase_ = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : Dict =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Dict , UpperCamelCase : int=chr(UpperCamelCase ) , UpperCamelCase : Union[str, Any]=chr(UpperCamelCase ) , UpperCamelCase : Any=chr(UpperCamelCase ) , UpperCamelCase : Union[str, Any]=chr(UpperCamelCase ) , UpperCamelCase : List[Any]=chr(UpperCamelCase ) , UpperCamelCase : List[str]=chr(UpperCamelCase ) , UpperCamelCase : int=False , UpperCamelCase : str=20_48 , **UpperCamelCase : List[str] , ):
'''simple docstring'''
_snake_case : Tuple = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else bos_token
_snake_case : Optional[Any] = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else eos_token
_snake_case : Any = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else sep_token
_snake_case : str = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else cls_token
_snake_case : Dict = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_snake_case : str = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else mask_token
super().__init__(
bos_token=UpperCamelCase , eos_token=UpperCamelCase , sep_token=UpperCamelCase , cls_token=UpperCamelCase , pad_token=UpperCamelCase , mask_token=UpperCamelCase , add_prefix_space=UpperCamelCase , model_max_length=UpperCamelCase , **UpperCamelCase , )
# Creates a mapping for looking up the IDs of special symbols.
_snake_case : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
_snake_case : Tuple = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
_snake_case : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
_snake_case : str = UNICODE_VOCAB_SIZE
_snake_case : Optional[Any] = len(self._special_codepoints )
@property
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
return self._unicode_vocab_size
def UpperCamelCase_ ( self : Optional[Any] , UpperCamelCase : str ):
'''simple docstring'''
return list(UpperCamelCase )
def UpperCamelCase_ ( self : List[Any] , UpperCamelCase : str ):
'''simple docstring'''
try:
return ord(UpperCamelCase )
except TypeError:
raise ValueError(f"""invalid token: '{token}'""" )
def UpperCamelCase_ ( self : Dict , UpperCamelCase : int ):
'''simple docstring'''
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(UpperCamelCase )
except TypeError:
raise ValueError(f"""invalid id: {index}""" )
def UpperCamelCase_ ( self : Optional[Any] , UpperCamelCase : List[Any] ):
'''simple docstring'''
return "".join(UpperCamelCase )
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
_snake_case : Optional[Any] = [self.sep_token_id]
_snake_case : int = [self.cls_token_id]
_snake_case : Any = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def UpperCamelCase_ ( self : Tuple , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase , token_ids_a=UpperCamelCase , already_has_special_tokens=UpperCamelCase )
_snake_case : int = [1] + ([0] * len(UpperCamelCase )) + [1]
if token_ids_a is not None:
result += ([0] * len(UpperCamelCase )) + [1]
return result
def UpperCamelCase_ ( self : Union[str, Any] , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
_snake_case : List[Any] = [self.sep_token_id]
_snake_case : Dict = [self.cls_token_id]
_snake_case : Tuple = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def UpperCamelCase_ ( self : int , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ):
'''simple docstring'''
return ()
| 260 | 0 |
from importlib import import_module
from .logging import get_logger
UpperCAmelCase : int = get_logger(__name__)
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None ) -> Tuple:
'''simple docstring'''
a__ : int =attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
a__ : Any =module._original_module if isinstance(lowerCAmelCase__ , _PatchedModuleObj ) else module
class __lowerCAmelCase :
_lowercase : List[Any] = []
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None ) -> List[str]:
'''simple docstring'''
a__ : Optional[int] =obj
a__ : Tuple =target
a__ : Tuple =new
a__ : str =target.split("." )[0]
a__ : str ={}
a__ : int =attrs or []
def __enter__( self ) -> str:
'''simple docstring'''
*a__ , a__ : List[Any] =self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCAmelCase__ ) ):
try:
a__ : Optional[int] =import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
a__ : Optional[int] =getattr(self.obj , lowerCAmelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCAmelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
a__ : Dict =obj_attr
# patch at top level
setattr(self.obj , lowerCAmelCase__ , _PatchedModuleObj(lowerCAmelCase__ , attrs=self.attrs ) )
a__ : Union[str, Any] =getattr(self.obj , lowerCAmelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCAmelCase__ , lowerCAmelCase__ , _PatchedModuleObj(getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) , attrs=self.attrs ) )
a__ : int =getattr(lowerCAmelCase__ , lowerCAmelCase__ )
# finally set the target attribute
setattr(lowerCAmelCase__ , lowerCAmelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
a__ : Any =getattr(import_module(".".join(lowerCAmelCase__ ) ) , lowerCAmelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCAmelCase__ ) is attr_value:
a__ : Optional[Any] =getattr(self.obj , lowerCAmelCase__ )
setattr(self.obj , lowerCAmelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
a__ : Dict =globals()["__builtins__"][target_attr]
setattr(self.obj , lowerCAmelCase__ , self.new )
else:
raise RuntimeError(F'''Tried to patch attribute {target_attr} instead of a submodule.''' )
def __exit__( self , *lowerCAmelCase__ ) -> str:
'''simple docstring'''
for attr in list(self.original ):
setattr(self.obj , lowerCAmelCase__ , self.original.pop(lowerCAmelCase__ ) )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
self.__enter__()
self._active_patches.append(self )
def _lowercase ( self ) -> str:
'''simple docstring'''
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 95 |
'''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 : Union[str, Any] =False
class _lowercase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def lowerCamelCase_ ( self: Optional[int] ):
lowerCamelCase__ : Tuple = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
lowerCamelCase__ : List[Any] = torch.manual_seed(0 )
lowerCamelCase__ : List[Any] = pipe(
image=UpperCamelCase__ , generator=UpperCamelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
lowerCamelCase__ : List[str] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase__ : Tuple = np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 41 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ = 1_00 ) -> int:
__lowerCamelCase = set()
__lowerCamelCase = 0
__lowerCamelCase = n + 1 # maximum limit
for a in range(2 , lowercase_ ):
for b in range(2 , lowercase_ ):
__lowerCamelCase = a**b # calculates the current power
collect_powers.add(lowercase_ ) # adds the result to the set
return len(lowercase_ )
if __name__ == "__main__":
print("Number of terms ", solution(int(str(input()).strip())))
| 362 | '''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a__ :
def __init__( self : List[Any] , a : Tuple , a : int , a : int ):
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
__lowerCamelCase = img
__lowerCamelCase = img.shape[1]
__lowerCamelCase = img.shape[0]
__lowerCamelCase = dst_width
__lowerCamelCase = dst_height
__lowerCamelCase = self.src_w / self.dst_w
__lowerCamelCase = self.src_h / self.dst_h
__lowerCamelCase = __lowerCamelCase = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for i in range(self.dst_h ):
for j in range(self.dst_w ):
__lowerCamelCase = self.img[self.get_y(a )][self.get_x(a )]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : int ):
"""simple docstring"""
return int(self.ratio_x * x )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : int ):
"""simple docstring"""
return int(self.ratio_y * y )
if __name__ == "__main__":
__UpperCAmelCase , __UpperCAmelCase =8_0_0, 6_0_0
__UpperCAmelCase =imread("image_data/lena.jpg", 1)
__UpperCAmelCase =NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 237 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase ( A__ ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCamelCase_ , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(UpperCamelCase_ , '''num_attention_heads''' ) )
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=13 , UpperCamelCase_=64 , UpperCamelCase_=3 , UpperCamelCase_=3 , UpperCamelCase_=2 , UpperCamelCase_=1 , UpperCamelCase_=16 , UpperCamelCase_=[128, 256, 384] , UpperCamelCase_=[4, 6, 8] , UpperCamelCase_=[2, 3, 4] , UpperCamelCase_=[16, 16, 16] , UpperCamelCase_=0 , UpperCamelCase_=[2, 2, 2] , UpperCamelCase_=[2, 2, 2] , UpperCamelCase_=0.02 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=2 , ):
'''simple docstring'''
UpperCamelCase__ :Any = parent
UpperCamelCase__ :List[Any] = batch_size
UpperCamelCase__ :str = image_size
UpperCamelCase__ :Dict = num_channels
UpperCamelCase__ :Optional[int] = kernel_size
UpperCamelCase__ :List[str] = stride
UpperCamelCase__ :Dict = padding
UpperCamelCase__ :Optional[int] = hidden_sizes
UpperCamelCase__ :int = num_attention_heads
UpperCamelCase__ :List[Any] = depths
UpperCamelCase__ :int = key_dim
UpperCamelCase__ :int = drop_path_rate
UpperCamelCase__ :int = patch_size
UpperCamelCase__ :List[Any] = attention_ratio
UpperCamelCase__ :Optional[int] = mlp_ratio
UpperCamelCase__ :Optional[int] = initializer_range
UpperCamelCase__ :Any = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
UpperCamelCase__ :Optional[int] = is_training
UpperCamelCase__ :Optional[Any] = use_labels
UpperCamelCase__ :Dict = num_labels
UpperCamelCase__ :str = initializer_range
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ :int = None
if self.use_labels:
UpperCamelCase__ :str = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__ :Optional[Any] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = LevitModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :int = model(UpperCamelCase_ )
UpperCamelCase__ :int = (self.image_size, self.image_size)
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = image_size[0], image_size[1]
for _ in range(4 ):
UpperCamelCase__ :int = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
UpperCamelCase__ :Optional[Any] = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = self.num_labels
UpperCamelCase__ :List[Any] = LevitForImageClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Optional[int] = model(UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = config_and_inputs
UpperCamelCase__ :List[str] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( A__ , A__ , unittest.TestCase ):
"""simple docstring"""
_a = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
_a = (
{
'feature-extraction': LevitModel,
'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_a = False
_a = False
_a = False
_a = False
_a = False
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = LevitModelTester(self )
UpperCamelCase__ :Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ , hidden_size=37 )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
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 ):
'''simple docstring'''
return
@unittest.skip(reason='''Levit does not use inputs_embeds''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not support input and output embeddings''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''Levit does not output attentions''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :Dict = model_class(UpperCamelCase_ )
UpperCamelCase__ :int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ :Tuple = [*signature.parameters.keys()]
UpperCamelCase__ :Tuple = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
def check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase__ :str = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
UpperCamelCase__ :Union[str, Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase__ :List[str] = outputs.hidden_states
UpperCamelCase__ :int = len(self.model_tester.depths ) + 1
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
UpperCamelCase__ :Any = (self.model_tester.image_size, self.model_tester.image_size)
UpperCamelCase__ , UpperCamelCase__ :Optional[Any] = image_size[0], image_size[1]
for _ in range(4 ):
UpperCamelCase__ :Optional[Any] = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
UpperCamelCase__ :Union[str, Any] = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
UpperCamelCase__ , UpperCamelCase__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :Any = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ :Union[str, Any] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = super()._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCamelCase__ , UpperCamelCase__ :int = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :str = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(UpperCamelCase_ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
UpperCamelCase__ :List[Any] = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.train()
UpperCamelCase__ :Tuple = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
UpperCamelCase__ :Any = model(**UpperCamelCase_ ).loss
loss.backward()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCamelCase__ :int = False
UpperCamelCase__ :Dict = True
for model_class in self.all_model_classes:
if model_class in get_values(UpperCamelCase_ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
UpperCamelCase__ :Any = model_class(UpperCamelCase_ )
model.gradient_checkpointing_enable()
model.to(UpperCamelCase_ )
model.train()
UpperCamelCase__ :List[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
UpperCamelCase__ :Any = model(**UpperCamelCase_ ).loss
loss.backward()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :int = [
{'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
{'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
{'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(UpperCamelCase_ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}''' ):
UpperCamelCase__ :List[Any] = problem_type['''title''']
UpperCamelCase__ :Tuple = problem_type['''num_labels''']
UpperCamelCase__ :int = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.train()
UpperCamelCase__ :Optional[Any] = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
if problem_type["num_labels"] > 1:
UpperCamelCase__ :Tuple = inputs['''labels'''].unsqueeze(1 ).repeat(1 , problem_type['''num_labels'''] )
UpperCamelCase__ :Union[str, Any] = inputs['''labels'''].to(problem_type['''dtype'''] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=UpperCamelCase_ ) as warning_list:
UpperCamelCase__ :Optional[int] = model(**UpperCamelCase_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ :Dict = LevitModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def a ( ) -> int:
'''simple docstring'''
UpperCamelCase__ :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[str] = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
UpperCamelCase_ )
UpperCamelCase__ :Dict = self.default_image_processor
UpperCamelCase__ :Union[str, Any] = prepare_img()
UpperCamelCase__ :List[Any] = image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
UpperCamelCase__ :List[Any] = model(**UpperCamelCase_ )
# verify the logits
UpperCamelCase__ :int = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
UpperCamelCase__ :int = torch.tensor([1.0448, -0.3745, -1.8317] ).to(UpperCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4 ) ) | 97 |
'''simple docstring'''
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = []
UpperCamelCase__ :int = 1
while len(__a ) < 1e6:
constant.append(str(__a ) )
i += 1
UpperCamelCase__ :Union[str, Any] = ''''''.join(__a )
return (
int(constant[0] )
* int(constant[9] )
* int(constant[99] )
* int(constant[999] )
* int(constant[9999] )
* int(constant[99999] )
* int(constant[999999] )
)
if __name__ == "__main__":
print(solution()) | 97 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_a = logging.getLogger(__name__)
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class _UpperCAmelCase:
lowercase__ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowercase__ = field(
default=lowerCamelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase__ = field(
default=lowerCamelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase__ = field(
default=lowerCamelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class _UpperCAmelCase:
lowercase__ = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
lowercase__ = field(metadata={'help': 'Should contain the data files for the task.'} )
lowercase__ = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowercase__ = field(
default=lowerCamelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1 ), training_args.fpaa, )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''', __snake_case )
# Set seed
set_seed(training_args.seed )
try:
_UpperCamelCase = processors[data_args.task_name]()
_UpperCamelCase = processor.get_labels()
_UpperCamelCase = len(__snake_case )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCamelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=__snake_case, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
_UpperCamelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
_UpperCamelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path ), config=__snake_case, cache_dir=model_args.cache_dir, )
# Get datasets
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=__snake_case, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
if training_args.do_train
else None
)
_UpperCamelCase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=__snake_case, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
if training_args.do_eval
else None
)
def compute_metrics(__snake_case ) -> Dict:
_UpperCamelCase = np.argmax(p.predictions, axis=1 )
return {"acc": simple_accuracy(__snake_case, p.label_ids )}
# Data collator
_UpperCamelCase = DataCollatorWithPadding(__snake_case, pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCamelCase = Trainer(
model=__snake_case, args=__snake_case, train_dataset=__snake_case, eval_dataset=__snake_case, compute_metrics=__snake_case, data_collator=__snake_case, )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
_UpperCamelCase = trainer.evaluate()
_UpperCamelCase = os.path.join(training_args.output_dir, '''eval_results.txt''' )
if trainer.is_world_master():
with open(__snake_case, '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''', __snake_case, __snake_case )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__snake_case )
return results
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
main()
if __name__ == "__main__":
main() | 361 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
_a = get_logger(__name__)
class _UpperCAmelCase:
def __init__( self , __a = None) -> List[str]:
'''simple docstring'''
_UpperCamelCase = (
os.path.join(__a , config.EXTRACTED_DATASETS_DIR) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCamelCase = Extractor
def UpperCAmelCase ( self , __a) -> str:
'''simple docstring'''
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCamelCase = os.path.abspath(__a)
return os.path.join(self.extract_dir , hash_url_to_filename(__a))
def UpperCAmelCase ( self , __a , __a) -> bool:
'''simple docstring'''
return force_extract or (
not os.path.isfile(__a) and not (os.path.isdir(__a) and os.listdir(__a))
)
def UpperCAmelCase ( self , __a , __a = False) -> str:
'''simple docstring'''
_UpperCamelCase = self.extractor.infer_extractor_format(__a)
if not extractor_format:
return input_path
_UpperCamelCase = self._get_output_path(__a)
if self._do_extract(__a , __a):
self.extractor.extract(__a , __a , __a)
return output_path
class _UpperCAmelCase( lowerCamelCase ):
@classmethod
@abstractmethod
def UpperCAmelCase ( cls , __a , **__a) -> bool:
'''simple docstring'''
...
@staticmethod
@abstractmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
...
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase ):
lowercase__ = []
@staticmethod
def UpperCAmelCase ( __a , __a) -> Any:
'''simple docstring'''
with open(__a , '''rb''') as f:
return f.read(__a)
@classmethod
def UpperCAmelCase ( cls , __a , __a = b"") -> bool:
'''simple docstring'''
if not magic_number:
_UpperCamelCase = max(len(__a) for cls_magic_number in cls.magic_numbers)
try:
_UpperCamelCase = cls.read_magic_number(__a , __a)
except OSError:
return False
return any(magic_number.startswith(__a) for cls_magic_number in cls.magic_numbers)
class _UpperCAmelCase( lowerCamelCase ):
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> bool:
'''simple docstring'''
return tarfile.is_tarfile(__a)
@staticmethod
def UpperCAmelCase ( __a , __a) -> List[str]:
'''simple docstring'''
def resolved(__a) -> str:
return os.path.realpath(os.path.abspath(__a))
def badpath(__a , __a) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(__a , __a)).startswith(__a)
def badlink(__a , __a) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCamelCase = resolved(os.path.join(__a , os.path.dirname(info.name)))
return badpath(info.linkname , base=__a)
_UpperCamelCase = resolved(__a)
for finfo in members:
if badpath(finfo.name , __a):
logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)''')
elif finfo.issym() and badlink(__a , __a):
logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}''')
elif finfo.islnk() and badlink(__a , __a):
logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}''')
else:
yield finfo
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
os.makedirs(__a , exist_ok=__a)
_UpperCamelCase = tarfile.open(__a)
tar_file.extractall(__a , members=TarExtractor.safemembers(__a , __a))
tar_file.close()
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\x1F\x8B']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
with gzip.open(__a , '''rb''') as gzip_file:
with open(__a , '''wb''') as extracted_file:
shutil.copyfileobj(__a , __a)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [
b'PK\x03\x04',
b'PK\x05\x06', # empty archive
b'PK\x07\x08', # spanned archive
]
@classmethod
def UpperCAmelCase ( cls , __a , __a = b"") -> bool:
'''simple docstring'''
if super().is_extractable(__a , magic_number=__a):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(__a , '''rb''') as fp:
_UpperCamelCase = _EndRecData(__a)
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET]) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCamelCase = fp.read(__a) # CD is where we expect it to be
if len(__a) == sizeCentralDir:
_UpperCamelCase = struct.unpack(__a , __a) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
os.makedirs(__a , exist_ok=__a)
with zipfile.ZipFile(__a , '''r''') as zip_file:
zip_file.extractall(__a)
zip_file.close()
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\xFD\x37\x7A\x58\x5A\x00']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
with lzma.open(__a) as compressed_file:
with open(__a , '''wb''') as extracted_file:
shutil.copyfileobj(__a , __a)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00'] # RAR_ID # RAR5_ID
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
if not config.RARFILE_AVAILABLE:
raise ImportError('''Please pip install rarfile''')
import rarfile
os.makedirs(__a , exist_ok=__a)
_UpperCamelCase = rarfile.RarFile(__a)
rf.extractall(__a)
rf.close()
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\x28\xb5\x2F\xFD']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
if not config.ZSTANDARD_AVAILABLE:
raise ImportError('''Please pip install zstandard''')
import zstandard as zstd
_UpperCamelCase = zstd.ZstdDecompressor()
with open(__a , '''rb''') as ifh, open(__a , '''wb''') as ofh:
dctx.copy_stream(__a , __a)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\x42\x5A\x68']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
with bza.open(__a , '''rb''') as compressed_file:
with open(__a , '''wb''') as extracted_file:
shutil.copyfileobj(__a , __a)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\x37\x7A\xBC\xAF\x27\x1C']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
if not config.PY7ZR_AVAILABLE:
raise ImportError('''Please pip install py7zr''')
import pyazr
os.makedirs(__a , exist_ok=__a)
with pyazr.SevenZipFile(__a , '''r''') as archive:
archive.extractall(__a)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = [b'\x04\x22\x4D\x18']
@staticmethod
def UpperCAmelCase ( __a , __a) -> None:
'''simple docstring'''
if not config.LZ4_AVAILABLE:
raise ImportError('''Please pip install lz4''')
import lza.frame
with lza.frame.open(__a , '''rb''') as compressed_file:
with open(__a , '''wb''') as extracted_file:
shutil.copyfileobj(__a , __a)
class _UpperCAmelCase:
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
lowercase__ = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def UpperCAmelCase ( cls) -> Any:
'''simple docstring'''
return max(
len(__a)
for extractor in cls.extractors.values()
if issubclass(__a , __a)
for extractor_magic_number in extractor.magic_numbers)
@staticmethod
def UpperCAmelCase ( __a , __a) -> List[str]:
'''simple docstring'''
try:
return MagicNumberBaseExtractor.read_magic_number(__a , magic_number_length=__a)
except OSError:
return b""
@classmethod
def UpperCAmelCase ( cls , __a , __a = False) -> bool:
'''simple docstring'''
warnings.warn(
'''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'infer_extractor_format\' instead.''' , category=__a , )
_UpperCamelCase = cls.infer_extractor_format(__a)
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def UpperCAmelCase ( cls , __a) -> str: # <Added version="2.4.0"/>
'''simple docstring'''
_UpperCamelCase = cls._get_magic_number_max_length()
_UpperCamelCase = cls._read_magic_number(__a , __a)
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(__a , magic_number=__a):
return extractor_format
@classmethod
def UpperCAmelCase ( cls , __a , __a , __a = None , __a = "deprecated" , ) -> None:
'''simple docstring'''
os.makedirs(os.path.dirname(__a) , exist_ok=__a)
# Prevent parallel extractions
_UpperCamelCase = str(Path(__a).with_suffix('''.lock'''))
with FileLock(__a):
shutil.rmtree(__a , ignore_errors=__a)
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(__a , __a): # passed as positional arg
warnings.warn(
'''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
'''Use \'extractor_format\' instead.''' , category=__a , )
_UpperCamelCase = extractor if extractor != '''deprecated''' else extractor_format
else:
_UpperCamelCase = cls.extractors[extractor_format]
return extractor.extract(__a , __a)
else:
warnings.warn(
'''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
'''exception in 3.0.0.''' , category=__a , )
for extractor in cls.extractors.values():
if extractor.is_extractable(__a):
return extractor.extract(__a , __a)
| 100 | 0 |
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
__lowerCAmelCase : Any = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , _snake_case : str , _snake_case : Optional[int] , _snake_case : int=None , _snake_case : Any=1 ):
__lowercase : Optional[Any] = tokenizer
__lowercase : Optional[Any] = dataset
__lowercase : Dict = len(_snake_case ) if n_tasks is None else n_tasks
__lowercase : Optional[int] = n_copies
def __iter__( self : Union[str, Any] ):
__lowercase : Tuple = []
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() )
__lowercase : Dict = self.tokenizer(_snake_case , padding=_snake_case , 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 __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Optional[int] , _snake_case : Optional[Any] , _snake_case : int , _snake_case : Optional[int] ):
__lowercase : List[Any] = start_length
__lowercase : Optional[Any] = eof_strings
__lowercase : List[Any] = tokenizer
def __call__( self : str , _snake_case : Any , _snake_case : Optional[Any] , **_snake_case : List[Any] ):
__lowercase : List[Any] = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
__lowercase : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(_snake_case )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Optional[Any]:
__lowercase : Optional[int] = re.split('''(%s)''' % '''|'''.join(__lowerCAmelCase ) , __lowerCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=20 , **__lowerCAmelCase ) -> List[Any]:
__lowercase : int = defaultdict(__lowerCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__lowerCAmelCase ) ):
with torch.no_grad():
__lowercase : List[str] = batch['''ids'''].shape[-1]
__lowercase : List[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
__lowercase : Tuple = batch['''task_id'''].repeat(__lowerCAmelCase )
__lowercase : str = accelerator.pad_across_processes(
__lowerCAmelCase , dim=1 , pad_index=tokenizer.pad_token_id )
__lowercase , __lowercase : List[Any] = accelerator.gather((generated_tokens, generated_tasks) )
__lowercase : Any = generated_tokens.cpu().numpy()
__lowercase : List[str] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__lowerCAmelCase , __lowerCAmelCase ):
gen_token_dict[task].append(__lowerCAmelCase )
__lowercase : str = [[] for _ in range(__lowerCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
__lowercase : Union[str, Any] = tokenizer.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
code_gens[task].append(remove_last_block(__lowerCAmelCase ) )
return code_gens
def UpperCAmelCase_ ( ) -> List[str]:
# Setup configuration
__lowercase : List[str] = HfArgumentParser(__lowerCAmelCase )
__lowercase : int = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
__lowercase : Tuple = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
__lowercase : Any = '''false'''
if args.num_workers is None:
__lowercase : Dict = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
__lowercase : List[str] = Accelerator()
set_seed(args.seed , device_specific=__lowerCAmelCase )
# Load model and tokenizer
__lowercase : List[str] = AutoTokenizer.from_pretrained(args.model_ckpt )
__lowercase : int = tokenizer.eos_token
__lowercase : str = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
__lowercase : 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
__lowercase : Any = load_dataset('''openai_humaneval''' )
__lowercase : Tuple = load_metric('''code_eval''' )
__lowercase : Any = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
__lowercase : str = args.n_samples // args.batch_size
__lowercase : List[Any] = TokenizedDataset(__lowerCAmelCase , human_eval['''test'''] , n_copies=__lowerCAmelCase , n_tasks=__lowerCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
__lowercase : List[str] = DataLoader(__lowerCAmelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
__lowercase : 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
__lowercase , __lowercase : Union[str, Any] = accelerator.prepare(__lowerCAmelCase , __lowerCAmelCase )
__lowercase : Optional[Any] = complete_code(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , n_tasks=__lowerCAmelCase , batch_size=args.batch_size , **__lowerCAmelCase , )
if accelerator.is_main_process:
__lowercase : Optional[Any] = []
for task in tqdm(range(__lowerCAmelCase ) ):
__lowercase : Optional[int] = human_eval['''test'''][task]['''test''']
__lowercase : str = F'check({human_eval["test"][task]["entry_point"]})'
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
__lowercase , __lowercase : Tuple = 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()
| 156 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
__lowerCAmelCase : List[Any] = logging.get_logger(__name__)
__lowerCAmelCase : int = {
"artists_file": "artists.json",
"lyrics_file": "lyrics.json",
"genres_file": "genres.json",
}
__lowerCAmelCase : List[Any] = {
"artists_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
},
"genres_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
},
"lyrics_file": {
"jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
},
}
__lowerCAmelCase : Dict = {
"jukebox": 512,
}
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Tuple = VOCAB_FILES_NAMES
A__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
A__ : List[str] = PRETRAINED_LYRIC_TOKENS_SIZES
A__ : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self : List[Any] , _snake_case : List[str] , _snake_case : Any , _snake_case : Tuple , _snake_case : Dict=["v3", "v2", "v2"] , _snake_case : Tuple=512 , _snake_case : Any=5 , _snake_case : List[Any]="<|endoftext|>" , **_snake_case : Union[str, Any] , ):
__lowercase : Dict = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else unk_token
super().__init__(
unk_token=_snake_case , n_genres=_snake_case , version=_snake_case , max_n_lyric_tokens=_snake_case , **_snake_case , )
__lowercase : List[str] = version
__lowercase : Union[str, Any] = max_n_lyric_tokens
__lowercase : Dict = n_genres
with open(_snake_case , encoding='''utf-8''' ) as vocab_handle:
__lowercase : str = json.load(_snake_case )
with open(_snake_case , encoding='''utf-8''' ) as vocab_handle:
__lowercase : Optional[int] = json.load(_snake_case )
with open(_snake_case , encoding='''utf-8''' ) as vocab_handle:
__lowercase : Optional[int] = json.load(_snake_case )
__lowercase : Dict = r'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder ) == 79:
__lowercase : int = oov.replace(r'''\-\'''' , r'''\-+\'''' )
__lowercase : Union[str, Any] = regex.compile(_snake_case )
__lowercase : int = {v: k for k, v in self.artists_encoder.items()}
__lowercase : Tuple = {v: k for k, v in self.genres_encoder.items()}
__lowercase : Dict = {v: k for k, v in self.lyrics_encoder.items()}
@property
def snake_case_ ( self : Tuple ):
return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )
def snake_case_ ( self : Optional[Any] ):
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder )
def snake_case_ ( self : int , _snake_case : Tuple , _snake_case : Dict , _snake_case : Dict ):
__lowercase : Union[str, Any] = [self.artists_encoder.get(_snake_case , 0 ) for artist in list_artists]
for genres in range(len(_snake_case ) ):
__lowercase : Union[str, Any] = [self.genres_encoder.get(_snake_case , 0 ) for genre in list_genres[genres]]
__lowercase : str = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))
__lowercase : int = [[self.lyrics_encoder.get(_snake_case , 0 ) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def snake_case_ ( self : Dict , _snake_case : Any ):
return list(_snake_case )
def snake_case_ ( self : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : List[Any] , _snake_case : List[str] , **_snake_case : Optional[int] ):
__lowercase , __lowercase , __lowercase : Optional[int] = self.prepare_for_tokenization(_snake_case , _snake_case , _snake_case )
__lowercase : List[Any] = self._tokenize(_snake_case )
return artist, genre, lyrics
def snake_case_ ( self : str , _snake_case : str , _snake_case : str , _snake_case : str , _snake_case : bool = False ):
for idx in range(len(self.version ) ):
if self.version[idx] == "v3":
__lowercase : Union[str, Any] = artists[idx].lower()
__lowercase : str = [genres[idx].lower()]
else:
__lowercase : Any = self._normalize(artists[idx] ) + '''.v2'''
__lowercase : Tuple = [
self._normalize(_snake_case ) + '''.v2''' for genre in genres[idx].split('''_''' )
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
__lowercase : Optional[int] = regex.compile(r'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+''' )
__lowercase : Dict = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
__lowercase : List[Any] = {vocab[index]: index + 1 for index in range(len(_snake_case ) )}
__lowercase : List[str] = 0
__lowercase : Any = len(_snake_case ) + 1
__lowercase : str = self.vocab
__lowercase : Union[str, Any] = {v: k for k, v in self.vocab.items()}
__lowercase : Dict = ''''''
else:
__lowercase : Tuple = regex.compile(r'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+''' )
__lowercase : List[Any] = self._run_strip_accents(_snake_case )
__lowercase : Tuple = lyrics.replace('''\\''' , '''\n''' )
__lowercase : str = self.out_of_vocab.sub('''''' , _snake_case ), [], []
return artists, genres, lyrics
def snake_case_ ( self : Optional[int] , _snake_case : List[str] ):
__lowercase : Any = unicodedata.normalize('''NFD''' , _snake_case )
__lowercase : Optional[int] = []
for char in text:
__lowercase : Union[str, Any] = unicodedata.category(_snake_case )
if cat == "Mn":
continue
output.append(_snake_case )
return "".join(_snake_case )
def snake_case_ ( self : Optional[int] , _snake_case : str ):
__lowercase : List[str] = (
[chr(_snake_case ) for i in range(ord('''a''' ) , ord('''z''' ) + 1 )]
+ [chr(_snake_case ) for i in range(ord('''A''' ) , ord('''Z''' ) + 1 )]
+ [chr(_snake_case ) for i in range(ord('''0''' ) , ord('''9''' ) + 1 )]
+ ['''.''']
)
__lowercase : Optional[Any] = frozenset(_snake_case )
__lowercase : Union[str, Any] = re.compile(r'''_+''' )
__lowercase : Optional[int] = ''''''.join([c if c in accepted else '''_''' for c in text.lower()] )
__lowercase : int = pattern.sub('''_''' , _snake_case ).strip('''_''' )
return text
def snake_case_ ( self : List[Any] , _snake_case : List[str] ):
return " ".join(_snake_case )
def snake_case_ ( self : List[str] , _snake_case : Any , _snake_case : Optional[Union[str, TensorType]] = None , _snake_case : bool = False ):
# Convert to TensorType
if not isinstance(_snake_case , _snake_case ):
__lowercase : Optional[Any] = TensorType(_snake_case )
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
'''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.''' )
import tensorflow as tf
__lowercase : int = tf.constant
__lowercase : Optional[int] = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.''' )
import torch
__lowercase : Union[str, Any] = torch.tensor
__lowercase : Dict = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.''' )
import jax.numpy as jnp # noqa: F811
__lowercase : Union[str, Any] = jnp.array
__lowercase : Optional[int] = _is_jax
else:
__lowercase : Tuple = np.asarray
__lowercase : str = _is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
__lowercase : Union[str, Any] = [inputs]
if not is_tensor(_snake_case ):
__lowercase : int = as_tensor(_snake_case )
except: # noqa E722
raise ValueError(
'''Unable to create tensor, you should probably activate truncation and/or padding '''
'''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.''' )
return inputs
def __call__( self : str , _snake_case : Optional[int] , _snake_case : Tuple , _snake_case : Tuple="" , _snake_case : Tuple="pt" ):
__lowercase : List[str] = [0, 0, 0]
__lowercase : List[str] = [artist] * len(self.version )
__lowercase : List[Any] = [genres] * len(self.version )
__lowercase , __lowercase , __lowercase : Tuple = self.tokenize(_snake_case , _snake_case , _snake_case )
__lowercase , __lowercase , __lowercase : List[str] = self._convert_token_to_id(_snake_case , _snake_case , _snake_case )
__lowercase : Optional[Any] = [-INFINITY] * len(full_tokens[-1] )
__lowercase : int = [
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=_snake_case )
for i in range(len(self.version ) )
]
return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks} )
def snake_case_ ( self : Optional[int] , _snake_case : str , _snake_case : Optional[str] = None ):
if not os.path.isdir(_snake_case ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase : int = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file'''] )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=_snake_case ) )
__lowercase : int = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file'''] )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=_snake_case ) )
__lowercase : Union[str, Any] = os.path.join(
_snake_case , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file'''] )
with open(_snake_case , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=_snake_case ) )
return (artists_file, genres_file, lyrics_file)
def snake_case_ ( self : str , _snake_case : Tuple , _snake_case : str , _snake_case : Dict ):
__lowercase : List[str] = self.artists_decoder.get(_snake_case )
__lowercase : Optional[Any] = [self.genres_decoder.get(_snake_case ) for genre in genres_index]
__lowercase : Dict = [self.lyrics_decoder.get(_snake_case ) for character in lyric_index]
return artist, genres, lyrics
| 156 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( a__ : int ) -> list[int]:
if num <= 0:
_UpperCamelCase = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(a__ )
_UpperCamelCase = [True] * (num + 1)
_UpperCamelCase = []
_UpperCamelCase = 2
_UpperCamelCase = int(math.sqrt(a__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(a__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , a__ ):
if sieve[i] is True:
_UpperCamelCase = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(a__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 54 | """simple docstring"""
import numpy as np
def lowercase ( a__ : Optional[Any] , a__ : str , a__ : Union[str, Any] , a__ : Any , a__ : List[str] ) -> Dict:
_UpperCamelCase = int(np.ceil((x_end - xa) / h ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(a__ ):
_UpperCamelCase = f(a__ , y[k] )
_UpperCamelCase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_UpperCamelCase = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_UpperCamelCase = f(x + h , y[k] + h * ka )
_UpperCamelCase = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 54 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a__ : Optional[int] ={
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : str =[
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
a__ : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 53 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCAmelCase__ ( unittest.TestCase):
'''simple docstring'''
@parameterized.expand([(None,), ("foo.json",)])
def _lowerCamelCase ( self , __lowerCamelCase) -> List[str]:
_A : str = GenerationConfig(
do_sample=__lowerCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__lowerCamelCase , config_name=__lowerCamelCase)
_A : Tuple = GenerationConfig.from_pretrained(__lowerCamelCase , config_name=__lowerCamelCase)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , __lowerCamelCase)
self.assertEqual(loaded_config.temperature , 0.7)
self.assertEqual(loaded_config.length_penalty , 1.0)
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 5_0)
self.assertEqual(loaded_config.max_length , 2_0)
self.assertEqual(loaded_config.max_time , __lowerCamelCase)
def _lowerCamelCase ( self) -> Optional[int]:
_A : Optional[int] = AutoConfig.from_pretrained("gpt2")
_A : int = GenerationConfig.from_model_config(__lowerCamelCase)
_A : List[Any] = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(__lowerCamelCase , __lowerCamelCase)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id)
def _lowerCamelCase ( self) -> Optional[Any]:
_A : Optional[Any] = GenerationConfig()
_A : List[Any] = {
"max_new_tokens": 1_0_2_4,
"foo": "bar",
}
_A : List[str] = copy.deepcopy(__lowerCamelCase)
_A : int = generation_config.update(**__lowerCamelCase)
# update_kwargs was not modified (no side effects)
self.assertEqual(__lowerCamelCase , __lowerCamelCase)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_0_2_4)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(__lowerCamelCase , {"foo": "bar"})
def _lowerCamelCase ( self) -> Any:
_A : int = GenerationConfig()
_A : int = "bar"
with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
generation_config.save_pretrained(__lowerCamelCase)
_A : Any = GenerationConfig.from_pretrained(__lowerCamelCase)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , "bar")
_A : Optional[Any] = GenerationConfig.from_model_config(__lowerCamelCase)
assert not hasattr(__lowerCamelCase , "foo") # no new kwargs should be initialized if from config
def _lowerCamelCase ( self) -> List[str]:
_A : Union[str, Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0)
self.assertEqual(default_config.do_sample , __lowerCamelCase)
self.assertEqual(default_config.num_beams , 1)
_A : Optional[int] = GenerationConfig(
do_sample=__lowerCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7)
self.assertEqual(config.do_sample , __lowerCamelCase)
self.assertEqual(config.num_beams , 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__lowerCamelCase)
_A : Optional[int] = GenerationConfig.from_pretrained(__lowerCamelCase , temperature=1.0)
self.assertEqual(loaded_config.temperature , 1.0)
self.assertEqual(loaded_config.do_sample , __lowerCamelCase)
self.assertEqual(loaded_config.num_beams , 1) # default value
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase):
'''simple docstring'''
@classmethod
def _lowerCamelCase ( cls) -> Optional[int]:
_A : Dict = TOKEN
HfFolder.save_token(__lowerCamelCase)
@classmethod
def _lowerCamelCase ( cls) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id="test-generation-config")
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-generation-config-org")
except HTTPError:
pass
def _lowerCamelCase ( self) -> Any:
_A : Optional[int] = GenerationConfig(
do_sample=__lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("test-generation-config" , use_auth_token=self._token)
_A : Union[str, Any] = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__lowerCamelCase , getattr(__lowerCamelCase , __lowerCamelCase))
# Reset repo
delete_repo(token=self._token , repo_id="test-generation-config")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__lowerCamelCase , repo_id="test-generation-config" , push_to_hub=__lowerCamelCase , use_auth_token=self._token)
_A : Optional[Any] = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__lowerCamelCase , getattr(__lowerCamelCase , __lowerCamelCase))
def _lowerCamelCase ( self) -> Union[str, Any]:
_A : Union[str, Any] = GenerationConfig(
do_sample=__lowerCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub("valid_org/test-generation-config-org" , use_auth_token=self._token)
_A : int = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__lowerCamelCase , getattr(__lowerCamelCase , __lowerCamelCase))
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-generation-config-org")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__lowerCamelCase , repo_id="valid_org/test-generation-config-org" , push_to_hub=__lowerCamelCase , use_auth_token=self._token)
_A : Optional[int] = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__lowerCamelCase , getattr(__lowerCamelCase , __lowerCamelCase))
| 11 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[str] = {
'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
'Salesforce/blip-vqa-capfit-large': (
'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
),
'Salesforce/blip-image-captioning-base': (
'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
),
'Salesforce/blip-image-captioning-large': (
'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
),
'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
'Salesforce/blip-itm-large-flikr': (
'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
),
}
class a__( lowerCamelCase__ ):
lowercase__ = """blip_text_model"""
def __init__( self : int , __snake_case : Dict=3_05_24 , __snake_case : str=7_68 , __snake_case : str=7_68 , __snake_case : List[str]=30_72 , __snake_case : Optional[int]=7_68 , __snake_case : List[Any]=12 , __snake_case : Dict=8 , __snake_case : Tuple=5_12 , __snake_case : Any="gelu" , __snake_case : Any=1e-1_2 , __snake_case : Optional[Any]=0.0 , __snake_case : Union[str, Any]=0.0 , __snake_case : Union[str, Any]=0.02 , __snake_case : Optional[Any]=3_05_22 , __snake_case : List[str]=2 , __snake_case : Any=0 , __snake_case : Optional[int]=1_02 , __snake_case : Tuple=True , __snake_case : Dict=True , **__snake_case : Optional[int] , ):
super().__init__(
pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , sep_token_id=__snake_case , **__snake_case , )
a : List[Any] = vocab_size
a : int = hidden_size
a : List[Any] = encoder_hidden_size
a : Optional[int] = intermediate_size
a : int = projection_dim
a : Optional[Any] = hidden_dropout_prob
a : Dict = num_hidden_layers
a : Any = num_attention_heads
a : int = max_position_embeddings
a : Dict = layer_norm_eps
a : Tuple = hidden_act
a : str = initializer_range
a : List[str] = attention_probs_dropout_prob
a : Any = is_decoder
a : Any = use_cache
@classmethod
def lowercase_ ( cls : Optional[Any] , __snake_case : Union[str, os.PathLike] , **__snake_case : List[str] ):
cls._set_token_in_kwargs(__snake_case )
a , a : Optional[int] = cls.get_config_dict(__snake_case , **__snake_case )
# get the text config dict if we are loading from BlipConfig
if config_dict.get('model_type' ) == "blip":
a : List[Any] = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__snake_case , **__snake_case )
class a__( lowerCamelCase__ ):
lowercase__ = """blip_vision_model"""
def __init__( self : Dict , __snake_case : Optional[Any]=7_68 , __snake_case : Dict=30_72 , __snake_case : str=5_12 , __snake_case : Any=12 , __snake_case : int=12 , __snake_case : Optional[Any]=3_84 , __snake_case : str=16 , __snake_case : str="gelu" , __snake_case : Union[str, Any]=1e-5 , __snake_case : Any=0.0 , __snake_case : List[Any]=1e-1_0 , **__snake_case : Tuple , ):
super().__init__(**__snake_case )
a : int = hidden_size
a : Optional[int] = intermediate_size
a : str = projection_dim
a : str = num_hidden_layers
a : Optional[int] = num_attention_heads
a : Union[str, Any] = patch_size
a : Optional[int] = image_size
a : int = initializer_range
a : Tuple = attention_dropout
a : List[str] = layer_norm_eps
a : List[str] = hidden_act
@classmethod
def lowercase_ ( cls : Dict , __snake_case : Union[str, os.PathLike] , **__snake_case : Union[str, Any] ):
cls._set_token_in_kwargs(__snake_case )
a , a : int = cls.get_config_dict(__snake_case , **__snake_case )
# get the vision config dict if we are loading from BlipConfig
if config_dict.get('model_type' ) == "blip":
a : List[str] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__snake_case , **__snake_case )
class a__( lowerCamelCase__ ):
lowercase__ = """blip"""
lowercase__ = True
def __init__( self : List[Any] , __snake_case : Optional[Any]=None , __snake_case : Optional[Any]=None , __snake_case : Optional[int]=5_12 , __snake_case : Tuple=2.6592 , __snake_case : Tuple=2_56 , **__snake_case : Any , ):
super().__init__(**__snake_case )
if text_config is None:
a : Tuple = {}
logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.' )
if vision_config is None:
a : List[str] = {}
logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.' )
a : Optional[Any] = BlipTextConfig(**__snake_case )
a : str = BlipVisionConfig(**__snake_case )
a : Tuple = self.vision_config.hidden_size
a : Any = projection_dim
a : List[Any] = logit_scale_init_value
a : Tuple = 1.0
a : Optional[Any] = 0.02
a : str = image_text_hidden_size
@classmethod
def lowercase_ ( cls : int , __snake_case : BlipTextConfig , __snake_case : BlipVisionConfig , **__snake_case : Dict ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__snake_case )
def lowercase_ ( self : Optional[int] ):
a : int = copy.deepcopy(self.__dict__ )
a : Any = self.text_config.to_dict()
a : Optional[Any] = self.vision_config.to_dict()
a : Optional[int] = self.__class__.model_type
return output | 96 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Optional[int] = ['NllbTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase: Dict = ['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
lowerCAmelCase: Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 96 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class UpperCAmelCase_ :
lowerCamelCase__ = XGLMConfig
lowerCamelCase__ = {}
lowerCamelCase__ = 'gelu'
def __init__( self, __a, __a=14, __a=7, __a=True, __a=True, __a=True, __a=99, __a=32, __a=2, __a=4, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=0.02, ):
'''simple docstring'''
_lowerCAmelCase : Dict = parent
_lowerCAmelCase : Tuple = batch_size
_lowerCAmelCase : Dict = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Optional[Any] = use_input_mask
_lowerCAmelCase : Union[str, Any] = use_labels
_lowerCAmelCase : Dict = vocab_size
_lowerCAmelCase : List[str] = d_model
_lowerCAmelCase : int = num_hidden_layers
_lowerCAmelCase : List[Any] = num_attention_heads
_lowerCAmelCase : int = ffn_dim
_lowerCAmelCase : Any = activation_function
_lowerCAmelCase : str = activation_dropout
_lowerCAmelCase : List[Any] = attention_dropout
_lowerCAmelCase : Dict = max_position_embeddings
_lowerCAmelCase : Any = initializer_range
_lowerCAmelCase : Optional[Any] = None
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Optional[Any] = 2
_lowerCAmelCase : Dict = 1
def snake_case__ ( self):
'''simple docstring'''
return XGLMConfig.from_pretrained("facebook/xglm-564M")
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length], self.vocab_size), clip_value_min=0, clip_value_max=3)
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length])
_lowerCAmelCase : int = self.get_config()
_lowerCAmelCase : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
)
def snake_case__ ( self):
'''simple docstring'''
return XGLMConfig(
vocab_size=self.vocab_size, d_model=self.hidden_size, num_layers=self.num_hidden_layers, attention_heads=self.num_attention_heads, ffn_dim=self.ffn_dim, activation_function=self.activation_function, activation_dropout=self.activation_dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=__a, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=__a, )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : List[Any] = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( a , a , unittest.TestCase):
lowerCamelCase__ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
lowerCamelCase__ = (TFXGLMForCausalLM,) if is_tf_available() else ()
lowerCamelCase__ = (
{'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = TFXGLMModelTester(self)
_lowerCAmelCase : int = ConfigTester(self, config_class=__a, n_embd=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : Any = TFXGLMModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
def snake_case__ ( self):
'''simple docstring'''
super().test_resize_token_embeddings()
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
@slow
def snake_case__ ( self, __a=True):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
_lowerCAmelCase : Optional[Any] = tf.convert_to_tensor([[2, 268, 9865]], dtype=tf.intaa) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
_lowerCAmelCase : Tuple = [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
# fmt: on
_lowerCAmelCase : Tuple = model.generate(__a, do_sample=__a, num_beams=1)
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist(), __a)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
_lowerCAmelCase : List[str] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
tf.random.set_seed(0)
_lowerCAmelCase : Union[str, Any] = tokenizer("Today is a nice day and", return_tensors="tf")
_lowerCAmelCase : Union[str, Any] = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(":/CPU:0"):
_lowerCAmelCase : Tuple = model.generate(__a, do_sample=__a, seed=[7, 0])
_lowerCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0], skip_special_tokens=__a)
_lowerCAmelCase : Optional[Any] = (
"Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
)
self.assertEqual(__a, __a)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
_lowerCAmelCase : Any = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
_lowerCAmelCase : Union[str, Any] = "left"
# use different length sentences to test batching
_lowerCAmelCase : Tuple = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When",
"Hello, my dog is a little",
]
_lowerCAmelCase : Tuple = tokenizer(__a, return_tensors="tf", padding=__a)
_lowerCAmelCase : List[str] = inputs["input_ids"]
_lowerCAmelCase : Any = model.generate(input_ids=__a, attention_mask=inputs["attention_mask"], max_new_tokens=12)
_lowerCAmelCase : List[Any] = tokenizer(sentences[0], return_tensors="tf").input_ids
_lowerCAmelCase : int = model.generate(input_ids=__a, max_new_tokens=12)
_lowerCAmelCase : List[Any] = tokenizer(sentences[1], return_tensors="tf").input_ids
_lowerCAmelCase : Optional[Any] = model.generate(input_ids=__a, max_new_tokens=12)
_lowerCAmelCase : List[Any] = tokenizer.batch_decode(__a, skip_special_tokens=__a)
_lowerCAmelCase : Union[str, Any] = tokenizer.decode(output_non_padded[0], skip_special_tokens=__a)
_lowerCAmelCase : Union[str, Any] = tokenizer.decode(output_padded[0], skip_special_tokens=__a)
_lowerCAmelCase : Union[str, Any] = [
"This is an extremelly long sentence that only exists to test the ability of the model to cope with "
"left-padding, such as in batched generation. The output for the sequence below should be the same "
"regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
"a single",
"Hello, my dog is a little bit of a shy one, but he is very friendly",
]
self.assertListEqual(__a, __a)
self.assertListEqual(__a, [non_padded_sentence, padded_sentence])
| 36 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : torch.FloatTensor
lowerCAmelCase : Optional[torch.FloatTensor] = None
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Dict , __UpperCamelCase : Optional[int]=0.999 , __UpperCamelCase : Union[str, Any]="cosine" , ) -> Tuple:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCamelCase : List[str] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCamelCase : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
UpperCAmelCase_ = []
for i in range(__UpperCamelCase ):
UpperCAmelCase_ = i / num_diffusion_timesteps
UpperCAmelCase_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCamelCase ) / alpha_bar_fn(__UpperCamelCase ) , __UpperCamelCase ) )
return torch.tensor(__UpperCamelCase , dtype=torch.floataa )
class a ( _A , _A ):
'''simple docstring'''
lowerCAmelCase : Any = 1
@register_to_config
def __init__( self : List[Any] , __snake_case : int = 10_00 , __snake_case : float = 0.0_001 , __snake_case : float = 0.02 , __snake_case : str = "linear" , __snake_case : Optional[Union[np.ndarray, List[float]]] = None , __snake_case : bool = True , __snake_case : bool = True , __snake_case : int = 0 , __snake_case : str = "epsilon" , __snake_case : float = 1.0 , **__snake_case : List[Any] , ):
if kwargs.get('''set_alpha_to_one''' , __snake_case ) is not None:
UpperCAmelCase_ = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , __snake_case , standard_warn=__snake_case )
UpperCAmelCase_ = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
UpperCAmelCase_ = torch.tensor(__snake_case , dtype=torch.floataa )
elif beta_schedule == "linear":
UpperCAmelCase_ = torch.linspace(__snake_case , __snake_case , __snake_case , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
UpperCAmelCase_ = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __snake_case , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
UpperCAmelCase_ = betas_for_alpha_bar(__snake_case )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
UpperCAmelCase_ = 1.0 - self.betas
UpperCAmelCase_ = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
UpperCAmelCase_ = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
UpperCAmelCase_ = 1.0
# setable values
UpperCAmelCase_ = None
UpperCAmelCase_ = torch.from_numpy(np.arange(0 , __snake_case ).copy().astype(np.intaa ) )
def lowerCamelCase_ ( self : str , __snake_case : torch.FloatTensor , __snake_case : Optional[int] = None ):
return sample
def lowerCamelCase_ ( self : int , __snake_case : int , __snake_case : Union[str, torch.device] = None ):
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
UpperCAmelCase_ = num_inference_steps
UpperCAmelCase_ = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
UpperCAmelCase_ = (np.arange(0 , __snake_case ) * step_ratio).round().copy().astype(np.intaa )
UpperCAmelCase_ = torch.from_numpy(__snake_case ).to(__snake_case )
self.timesteps += self.config.steps_offset
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : torch.FloatTensor , __snake_case : int , __snake_case : torch.FloatTensor , __snake_case : float = 0.0 , __snake_case : bool = False , __snake_case : Optional[torch.FloatTensor] = None , __snake_case : bool = True , ):
# 1. get previous step value (=t+1)
UpperCAmelCase_ = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
UpperCAmelCase_ = self.alphas_cumprod[timestep]
UpperCAmelCase_ = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
UpperCAmelCase_ = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
UpperCAmelCase_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
UpperCAmelCase_ = model_output
elif self.config.prediction_type == "sample":
UpperCAmelCase_ = model_output
UpperCAmelCase_ = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase_ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
UpperCAmelCase_ = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase_ = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
UpperCAmelCase_ = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=__snake_case , pred_original_sample=__snake_case )
def __len__( self : Optional[Any] ):
return self.config.num_train_timesteps
| 177 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
'tokenization_roberta': ['RobertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['RobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaForCausalLM',
'RobertaForMaskedLM',
'RobertaForMultipleChoice',
'RobertaForQuestionAnswering',
'RobertaForSequenceClassification',
'RobertaForTokenClassification',
'RobertaModel',
'RobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaForCausalLM',
'TFRobertaForMaskedLM',
'TFRobertaForMultipleChoice',
'TFRobertaForQuestionAnswering',
'TFRobertaForSequenceClassification',
'TFRobertaForTokenClassification',
'TFRobertaMainLayer',
'TFRobertaModel',
'TFRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'FlaxRobertaForCausalLM',
'FlaxRobertaForMaskedLM',
'FlaxRobertaForMultipleChoice',
'FlaxRobertaForQuestionAnswering',
'FlaxRobertaForSequenceClassification',
'FlaxRobertaForTokenClassification',
'FlaxRobertaModel',
'FlaxRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 177 | 1 |
"""simple docstring"""
from math import sqrt
def a_ ( _lowerCAmelCase : int = 100_0000 ):
'''simple docstring'''
lowercase__ : int = 0
lowercase__ : int = 0
lowercase__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(_lowerCAmelCase , sum_shortest_sides // 2 )
- max(1 , sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : Dict = 0
def _UpperCAmelCase ( self ) -> Optional[int]:
lowercase__ : Tuple = AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32' )
self.assertIsInstance(a , a )
def _UpperCAmelCase ( self ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : str = Path(a ) / 'preprocessor_config.json'
lowercase__ : str = Path(a ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(a , 'w' ) , )
json.dump({'model_type': 'clip'} , open(a , 'w' ) )
lowercase__ : Union[str, Any] = AutoImageProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def _UpperCAmelCase ( self ) -> List[str]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : str = Path(a ) / 'preprocessor_config.json'
lowercase__ : int = Path(a ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(a , 'w' ) , )
json.dump({'model_type': 'clip'} , open(a , 'w' ) )
lowercase__ : List[str] = AutoImageProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def _UpperCAmelCase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
lowercase__ : Optional[int] = Path(a ) / 'preprocessor_config.json'
lowercase__ : Optional[int] = Path(a ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(a , 'w' ) , )
json.dump({'model_type': 'clip'} , open(a , 'w' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
lowercase__ : int = AutoImageProcessor.from_pretrained(a ).to_dict()
config_dict.pop('image_processor_type' )
lowercase__ : Tuple = CLIPImageProcessor(**a )
# save in new folder
model_config.save_pretrained(a )
config.save_pretrained(a )
lowercase__ : Union[str, Any] = AutoImageProcessor.from_pretrained(a )
# make sure private variable is not incorrectly saved
lowercase__ : Optional[int] = json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(a , a )
def _UpperCAmelCase ( self ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Dict = Path(a ) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(a , 'w' ) , )
lowercase__ : List[str] = AutoImageProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
with self.assertRaisesRegex(
a , 'clip-base is not a local folder and is not a valid model identifier' ):
lowercase__ : Any = AutoImageProcessor.from_pretrained('clip-base' )
def _UpperCAmelCase ( self ) -> List[Any]:
with self.assertRaisesRegex(
a , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
lowercase__ : Dict = AutoImageProcessor.from_pretrained(a , revision='aaaaaa' )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
with self.assertRaisesRegex(
a , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
lowercase__ : int = AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model' )
def _UpperCAmelCase ( self ) -> Optional[int]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(a ):
lowercase__ : List[Any] = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a ):
lowercase__ : Optional[int] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=a )
lowercase__ : Union[str, Any] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=a )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a )
lowercase__ : str = AutoImageProcessor.from_pretrained(a , trust_remote_code=a )
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor' )
def _UpperCAmelCase ( self ) -> int:
try:
AutoConfig.register('custom' , a )
AutoImageProcessor.register(a , a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a ):
AutoImageProcessor.register(a , a )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : Optional[Any] = Path(a ) / 'preprocessor_config.json'
lowercase__ : List[Any] = Path(a ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(a , 'w' ) , )
json.dump({'model_type': 'clip'} , open(a , 'w' ) )
lowercase__ : Union[str, Any] = CustomImageProcessor.from_pretrained(a )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a )
lowercase__ : Optional[int] = AutoImageProcessor.from_pretrained(a )
self.assertIsInstance(a , a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _UpperCAmelCase ( self ) -> Dict:
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : Union[str, Any] = True
try:
AutoConfig.register('custom' , a )
AutoImageProcessor.register(a , a )
# If remote code is not set, the default is to use local
lowercase__ : int = AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
lowercase__ : Optional[int] = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=a )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
lowercase__ : int = AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=a )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(not hasattr(a , 'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 77 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )
SCREAMING_SNAKE_CASE : List[Any] = ''''''
while len(a__ ) % 3 != 0:
SCREAMING_SNAKE_CASE : Optional[int] = '''0''' + bin_string
SCREAMING_SNAKE_CASE : Dict = [
bin_string[index : index + 3]
for index in range(len(a__ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for index, val in enumerate(a__ ):
oct_val += int(2 ** (2 - index) * int(a__ ) )
oct_string += str(a__ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 367 |
import math
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(a__ )
def UpperCAmelCase_( a__ = 1 / 12_345 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : int = 3
while True:
SCREAMING_SNAKE_CASE : Union[str, Any] = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(a__ ):
SCREAMING_SNAKE_CASE : List[str] = int(a__ )
total_partitions += 1
if check_partition_perfect(a__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(a__ )
integer += 1
if __name__ == "__main__":
print(F"{solution() = }")
| 19 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
raise ValueError('''partitions can not > number_of_bytes!''' )
__a = number_of_bytes // partitions
__a = []
for i in range(lowerCAmelCase__ ):
__a = i * bytes_per_partition + 1
__a = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(f'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
snake_case_ = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def __magic_name__ ( self : str , __lowercase : str , __lowercase : List[str] , __lowercase : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AudioClassificationPipeline(model=__lowercase , feature_extractor=__lowercase )
# test with a raw waveform
SCREAMING_SNAKE_CASE__ : Optional[int] =np.zeros((3_40_00,) )
SCREAMING_SNAKE_CASE__ : str =np.zeros((1_40_00,) )
return audio_classifier, [audioa, audio]
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict =examples
SCREAMING_SNAKE_CASE__ : str =audio_classifier(__lowercase )
# by default a model is initialized with num_labels=2
self.assertEqual(
__lowercase , [
{'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
{'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =audio_classifier(__lowercase , top_k=1 )
self.assertEqual(
__lowercase , [
{'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
] , )
self.run_torchaudio(__lowercase )
@require_torchaudio
def __magic_name__ ( self : Union[str, Any] , __lowercase : str ) -> Optional[Any]:
import datasets
# test with a local file
SCREAMING_SNAKE_CASE__ : Optional[int] =datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
SCREAMING_SNAKE_CASE__ : int =dataset[0]['''audio''']['''array''']
SCREAMING_SNAKE_CASE__ : Optional[Any] =audio_classifier(__lowercase )
self.assertEqual(
__lowercase , [
{'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
{'''score''': ANY(__lowercase ), '''label''': ANY(__lowercase )},
] , )
@require_torch
def __magic_name__ ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict ='''anton-l/wav2vec2-random-tiny-classifier'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipeline('''audio-classification''' , model=__lowercase )
SCREAMING_SNAKE_CASE__ : str =np.ones((80_00,) )
SCREAMING_SNAKE_CASE__ : List[str] =audio_classifier(__lowercase , top_k=4 )
SCREAMING_SNAKE_CASE__ : Dict =[
{'''score''': 0.0842, '''label''': '''no'''},
{'''score''': 0.0838, '''label''': '''up'''},
{'''score''': 0.0837, '''label''': '''go'''},
{'''score''': 0.0834, '''label''': '''right'''},
]
SCREAMING_SNAKE_CASE__ : List[str] =[
{'''score''': 0.0845, '''label''': '''stop'''},
{'''score''': 0.0844, '''label''': '''on'''},
{'''score''': 0.0841, '''label''': '''right'''},
{'''score''': 0.0834, '''label''': '''left'''},
]
self.assertIn(nested_simplify(__lowercase , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
SCREAMING_SNAKE_CASE__ : List[str] ={'''array''': np.ones((80_00,) ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
SCREAMING_SNAKE_CASE__ : Tuple =audio_classifier(__lowercase , top_k=4 )
self.assertIn(nested_simplify(__lowercase , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def __magic_name__ ( self : Dict ) -> Any:
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''superb/wav2vec2-base-superb-ks'''
SCREAMING_SNAKE_CASE__ : Optional[int] =pipeline('''audio-classification''' , model=__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =datasets.load_dataset('''anton-l/superb_dummy''' , '''ks''' , split='''test''' )
SCREAMING_SNAKE_CASE__ : List[str] =np.array(dataset[3]['''speech'''] , dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : int =audio_classifier(__lowercase , top_k=4 )
self.assertEqual(
nested_simplify(__lowercase , decimals=3 ) , [
{'''score''': 0.981, '''label''': '''go'''},
{'''score''': 0.007, '''label''': '''up'''},
{'''score''': 0.006, '''label''': '''_unknown_'''},
{'''score''': 0.001, '''label''': '''down'''},
] , )
@require_tf
@unittest.skip('''Audio classification is not implemented for TF''' )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
pass | 152 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
# See all BART models at https://huggingface.co/models?filter=bart
}
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'bart'
lowerCamelCase = ['past_key_values']
lowerCamelCase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Tuple,lowercase_ : Optional[int]=5_0_2_6_5,lowercase_ : List[str]=1_0_2_4,lowercase_ : Any=1_2,lowercase_ : Optional[Any]=4_0_9_6,lowercase_ : str=1_6,lowercase_ : int=1_2,lowercase_ : Optional[Any]=4_0_9_6,lowercase_ : Any=1_6,lowercase_ : Any=0.0,lowercase_ : str=0.0,lowercase_ : Optional[Any]="gelu",lowercase_ : List[str]=1_0_2_4,lowercase_ : List[Any]=0.1,lowercase_ : Union[str, Any]=0.0,lowercase_ : Optional[int]=0.0,lowercase_ : List[Any]=0.02,lowercase_ : int=0.0,lowercase_ : Optional[Any]=False,lowercase_ : List[Any]=True,lowercase_ : Union[str, Any]=3,lowercase_ : int=1,lowercase_ : int=0,lowercase_ : List[str]=2,lowercase_ : Optional[int]=True,lowercase_ : Tuple=2,lowercase_ : List[str]=2,**lowercase_ : Dict,)-> List[Any]:
'''simple docstring'''
A__ = vocab_size
A__ = max_position_embeddings
A__ = d_model
A__ = encoder_ffn_dim
A__ = encoder_layers
A__ = encoder_attention_heads
A__ = decoder_ffn_dim
A__ = decoder_layers
A__ = decoder_attention_heads
A__ = dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = activation_function
A__ = init_std
A__ = encoder_layerdrop
A__ = decoder_layerdrop
A__ = classifier_dropout
A__ = use_cache
A__ = encoder_layers
A__ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)
# ensure backward compatibility for BART CNN models
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_ ):
A__ = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
'The config can simply be saved and uploaded again to be fixed.' )
class A ( _UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : Dict )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
A__ = {0: 'batch'}
A__ = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
A__ = {0: 'batch', 1: 'decoder_sequence'}
A__ = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(lowercase_,direction='inputs' )
elif self.task == "causal-lm":
# TODO: figure this case out.
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
A__ , A__ = self.num_layers
for i in range(lowercase_ ):
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
else:
A__ = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
] )
return common_inputs
@property
def snake_case__ ( self : Optional[Any] )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = super().outputs
else:
A__ = super(lowercase_,self ).outputs
if self.use_past:
A__ , A__ = self.num_layers
for i in range(lowercase_ ):
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
A__ = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def snake_case__ ( self : Tuple,lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
# Generate decoder inputs
A__ = seq_length if not self.use_past else 1
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
A__ = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
A__ = dict(**lowercase_,**lowercase_ )
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
A__ = common_inputs['decoder_input_ids'].shape[1]
A__ , A__ = self.num_attention_heads
A__ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
A__ = decoder_seq_length + 3
A__ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
A__ = torch.cat(
[common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_ )],dim=1 )
A__ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
A__ , A__ = self.num_layers
A__ = min(lowercase_,lowercase_ )
A__ = max(lowercase_,lowercase_ ) - min_num_layers
A__ = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
A__ = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(lowercase_,lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def snake_case__ ( self : List[str],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,lowercase_,lowercase_,lowercase_,lowercase_ )
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__ , A__ = self.num_layers
A__ , A__ = self.num_attention_heads
A__ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
A__ = common_inputs['attention_mask'].dtype
A__ = torch.cat(
[common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_ )],dim=1 )
A__ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def snake_case__ ( self : Union[str, Any],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = compute_effective_axis_dimension(
lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
A__ = tokenizer.num_special_tokens_to_add(lowercase_ )
A__ = compute_effective_axis_dimension(
lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
A__ = [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
A__ = dict(tokenizer(lowercase_,return_tensors=lowercase_ ) )
return common_inputs
def snake_case__ ( self : Union[str, Any],lowercase_ : PreTrainedTokenizer,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional[TensorType] = None,)-> Mapping[str, Any]:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
elif self.task == "causal-lm":
A__ = self._generate_dummy_inputs_for_causal_lm(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
else:
A__ = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_ )
return common_inputs
def snake_case__ ( self : int,lowercase_ : Tuple,lowercase_ : int,lowercase_ : int,lowercase_ : str )-> str:
'''simple docstring'''
if self.task in ["default", "seq2seq-lm"]:
A__ = super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_ )
else:
A__ = super(lowercase_,self )._flatten_past_key_values_(
lowercase_,lowercase_,lowercase_,lowercase_ )
| 282 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
A__ = [
0X6_a_0_9_e_6_6_7,
0Xb_b_6_7_a_e_8_5,
0X3_c_6_e_f_3_7_2,
0Xa_5_4_f_f_5_3_a,
0X5_1_0_e_5_2_7_f,
0X9_b_0_5_6_8_8_c,
0X1_f_8_3_d_9_a_b,
0X5_b_e_0_c_d_1_9,
]
# Initialize round constants
A__ = [
0X4_2_8_a_2_f_9_8,
0X7_1_3_7_4_4_9_1,
0Xb_5_c_0_f_b_c_f,
0Xe_9_b_5_d_b_a_5,
0X3_9_5_6_c_2_5_b,
0X5_9_f_1_1_1_f_1,
0X9_2_3_f_8_2_a_4,
0Xa_b_1_c_5_e_d_5,
0Xd_8_0_7_a_a_9_8,
0X1_2_8_3_5_b_0_1,
0X2_4_3_1_8_5_b_e,
0X5_5_0_c_7_d_c_3,
0X7_2_b_e_5_d_7_4,
0X8_0_d_e_b_1_f_e,
0X9_b_d_c_0_6_a_7,
0Xc_1_9_b_f_1_7_4,
0Xe_4_9_b_6_9_c_1,
0Xe_f_b_e_4_7_8_6,
0X0_f_c_1_9_d_c_6,
0X2_4_0_c_a_1_c_c,
0X2_d_e_9_2_c_6_f,
0X4_a_7_4_8_4_a_a,
0X5_c_b_0_a_9_d_c,
0X7_6_f_9_8_8_d_a,
0X9_8_3_e_5_1_5_2,
0Xa_8_3_1_c_6_6_d,
0Xb_0_0_3_2_7_c_8,
0Xb_f_5_9_7_f_c_7,
0Xc_6_e_0_0_b_f_3,
0Xd_5_a_7_9_1_4_7,
0X0_6_c_a_6_3_5_1,
0X1_4_2_9_2_9_6_7,
0X2_7_b_7_0_a_8_5,
0X2_e_1_b_2_1_3_8,
0X4_d_2_c_6_d_f_c,
0X5_3_3_8_0_d_1_3,
0X6_5_0_a_7_3_5_4,
0X7_6_6_a_0_a_b_b,
0X8_1_c_2_c_9_2_e,
0X9_2_7_2_2_c_8_5,
0Xa_2_b_f_e_8_a_1,
0Xa_8_1_a_6_6_4_b,
0Xc_2_4_b_8_b_7_0,
0Xc_7_6_c_5_1_a_3,
0Xd_1_9_2_e_8_1_9,
0Xd_6_9_9_0_6_2_4,
0Xf_4_0_e_3_5_8_5,
0X1_0_6_a_a_0_7_0,
0X1_9_a_4_c_1_1_6,
0X1_e_3_7_6_c_0_8,
0X2_7_4_8_7_7_4_c,
0X3_4_b_0_b_c_b_5,
0X3_9_1_c_0_c_b_3,
0X4_e_d_8_a_a_4_a,
0X5_b_9_c_c_a_4_f,
0X6_8_2_e_6_f_f_3,
0X7_4_8_f_8_2_e_e,
0X7_8_a_5_6_3_6_f,
0X8_4_c_8_7_8_1_4,
0X8_c_c_7_0_2_0_8,
0X9_0_b_e_f_f_f_a,
0Xa_4_5_0_6_c_e_b,
0Xb_e_f_9_a_3_f_7,
0Xc_6_7_1_7_8_f_2,
]
A__ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def snake_case__ ( lowercase_ : bytes )-> bytes:
'''simple docstring'''
A__ = B'\x80' + (B'\x00' * (6_3 - (len(lowercase_ ) + 8) % 6_4))
A__ = struct.pack('>Q',(len(lowercase_ ) * 8) )
return data + padding + big_endian_integer
def snake_case__ ( self : Optional[int] )-> None:
'''simple docstring'''
A__ = [
self.preprocessed_data[x : x + 6_4]
for x in range(0,len(self.preprocessed_data ),6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
A__ = list(struct.unpack('>16L',lowercase_ ) )
# add 48 0-ed integers
words += [0] * 4_8
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = self.hashes
for index in range(0,6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
A__ = (
self.ror(words[index - 1_5],7 )
^ self.ror(words[index - 1_5],1_8 )
^ (words[index - 1_5] >> 3)
)
A__ = (
self.ror(words[index - 2],1_7 )
^ self.ror(words[index - 2],1_9 )
^ (words[index - 2] >> 1_0)
)
A__ = (
words[index - 1_6] + sa + words[index - 7] + sa
) % 0X1_0_0_0_0_0_0_0_0
# Compression
A__ = self.ror(lowercase_,6 ) ^ self.ror(lowercase_,1_1 ) ^ self.ror(lowercase_,2_5 )
A__ = (e & f) ^ ((~e & 0Xf_f_f_f_f_f_f_f) & g)
A__ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0_0_0_0_0_0_0_0
A__ = self.ror(lowercase_,2 ) ^ self.ror(lowercase_,1_3 ) ^ self.ror(lowercase_,2_2 )
A__ = (a & b) ^ (a & c) ^ (b & c)
A__ = (sa + maj) % 0X1_0_0_0_0_0_0_0_0
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = (
g,
f,
e,
((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
c,
b,
a,
((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
)
A__ = [a, b, c, d, e, f, g, h]
# Modify final values
A__ = [
((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
A__ = ''.join([hex(lowercase_ )[2:].zfill(8 ) for value in self.hashes] )
def snake_case__ ( self : Union[str, Any],lowercase_ : int,lowercase_ : int )-> int:
'''simple docstring'''
return 0Xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : List[str] )-> None:
'''simple docstring'''
import hashlib
A__ = bytes('Test String','utf-8' )
self.assertEqual(SHAaaa(lowercase_ ).hash,hashlib.shaaaa(lowercase_ ).hexdigest() )
def _snake_case( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
A__ = argparse.ArgumentParser()
parser.add_argument(
'-s' , '--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument(
'-f' , '--file' , dest='input_file' , help='Hash contents of a file' )
A__ = parser.parse_args()
A__ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
A__ = f.read()
else:
A__ = bytes(SCREAMING_SNAKE_CASE__ , 'utf-8' )
print(SHAaaa(SCREAMING_SNAKE_CASE__ ).hash )
if __name__ == "__main__":
main()
| 282 | 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
lowercase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
__UpperCAmelCase : Optional[datasets.Features] = None
__UpperCAmelCase : str = "utf-8"
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : bool = True # deprecated
__UpperCAmelCase : Optional[int] = None # deprecated
__UpperCAmelCase : int = 1_0 << 2_0 # 10MB
__UpperCAmelCase : Optional[bool] = None
class __lowerCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = JsonConfig
def __UpperCAmelCase ( self ):
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
__a = 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 __UpperCAmelCase ( self , _a ):
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}''' )
__a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_a , (str, list, tuple) ):
__a = data_files
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
__a = []
for split_name, files in data_files.items():
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
splits.append(datasets.SplitGenerator(name=_a , gen_kwargs={'''files''': files} ) )
return splits
def __UpperCAmelCase ( self , _a ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
__a = self.config.features.arrow_schema.field(_a ).type
__a = pa_table.append_column(_a , pa.array([None] * len(_a ) , type=_a ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__a = table_cast(_a , self.config.features.arrow_schema )
return pa_table
def __UpperCAmelCase ( self , _a ):
for file_idx, file in enumerate(itertools.chain.from_iterable(_a ) ):
# 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(_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
# We keep only the field we are interested in
__a = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_a , (list, tuple) ):
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
else:
__a = dataset
__a = pa.Table.from_pydict(_a )
yield file_idx, self._cast_table(_a )
# If the file has one json object per line
else:
with open(_a , '''rb''' ) as f:
__a = 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
__a = max(self.config.chunksize // 32 , 16 << 10 )
__a = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
__a = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_a )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__a = batch.decode(self.config.encoding , errors=_a ).encode('''utf-8''' )
try:
while True:
try:
__a = paj.read_json(
io.BytesIO(_a ) , read_options=paj.ReadOptions(block_size=_a ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_a , pa.ArrowInvalid )
and "straddling" not in str(_a )
or block_size > len(_a )
):
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(_a )} 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(
_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_a , _a ): # list is the only sequence type supported in JSON
try:
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
__a = pa.Table.from_pydict(_a )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(_a )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {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(_a )
batch_idx += 1
| 45 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
__a = """hf-internal-testing/tiny-random-t5"""
__a = AutoTokenizer.from_pretrained(__lowercase )
__a = AutoModelForSeqaSeqLM.from_pretrained(__lowercase )
__a = tokenizer("""This is me""" , return_tensors="""pt""" )
__a = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
__a = model.generate(**__lowercase )
__a = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowercase )
__a = AutoModelForSeqaSeqLM.from_pretrained(__lowercase )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
__a = model_reloaded.generate(**__lowercase )
self.assertTrue(torch.allclose(__lowercase , __lowercase ) )
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
__a = """hf-internal-testing/tiny-random-t5"""
__a = AutoModelForSeqaSeqLM.from_pretrained(__lowercase )
__a = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(__lowercase ):
model.save_pretrained(__lowercase )
__a = model.reverse_bettertransformer()
model.save_pretrained(__lowercase )
| 302 | 0 |
"""simple docstring"""
import os
_UpperCamelCase = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = 0
UpperCAmelCase = 0
while index < len(_snake_case ) - 1:
UpperCAmelCase = SYMBOLS[numerals[index]]
UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = """"""
UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _a ( _snake_case = "/p089_roman.txt" ):
"""simple docstring"""
UpperCAmelCase = 0
with open(os.path.dirname(_snake_case ) + roman_numerals_filename ) as filea:
UpperCAmelCase = filea.readlines()
for line in lines:
UpperCAmelCase = line.strip()
UpperCAmelCase = parse_roman_numerals(_snake_case )
UpperCAmelCase = generate_roman_numerals(_snake_case )
savings += len(_snake_case ) - len(_snake_case )
return savings
if __name__ == "__main__":
print(F"""{solution() = }""")
| 234 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ["""GLPNFeatureExtractor"""]
_UpperCamelCase = ["""GLPNImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GLPNForDepthEstimation""",
"""GLPNLayer""",
"""GLPNModel""",
"""GLPNPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_glpn import GLPNFeatureExtractor
from .image_processing_glpn import GLPNImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_glpn import (
GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
GLPNForDepthEstimation,
GLPNLayer,
GLPNModel,
GLPNPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 234 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = DistilBertTokenizer
snake_case_ = DistilBertTokenizerFast
snake_case_ = True
@slow
def lowercase_ ( self ) -> List[Any]:
'''simple docstring'''
__lowerCamelCase = DistilBertTokenizer.from_pretrained('distilbert-base-uncased' )
__lowerCamelCase = tokenizer.encode('sequence builders' , add_special_tokens=lowerCamelCase__ )
__lowerCamelCase = tokenizer.encode('multi-sequence build' , add_special_tokens=lowerCamelCase__ )
__lowerCamelCase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
__lowerCamelCase = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
| 90 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( lowerCamelCase__ ):
@staticmethod
def snake_case ( _snake_case ):
"""simple docstring"""
_lowerCAmelCase = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=_snake_case , default=_snake_case , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=_snake_case , help="""Name of the model to download""" )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = model
_lowerCAmelCase = cache
_lowerCAmelCase = force
_lowerCAmelCase = trust_remote_code
def snake_case ( self ):
"""simple docstring"""
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 82 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[int] = {
'''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = """instructblip_vision_model"""
def __init__( self : Union[str, Any] , __lowerCamelCase : str=1_408 , __lowerCamelCase : str=6_144 , __lowerCamelCase : List[Any]=39 , __lowerCamelCase : str=16 , __lowerCamelCase : Optional[int]=224 , __lowerCamelCase : Dict=14 , __lowerCamelCase : str="gelu" , __lowerCamelCase : int=1E-6 , __lowerCamelCase : Union[str, Any]=0.0 , __lowerCamelCase : Tuple=1E-10 , __lowerCamelCase : List[str]=True , **__lowerCamelCase : str , ):
super().__init__(**__lowerCamelCase )
UpperCamelCase :Dict = hidden_size
UpperCamelCase :List[Any] = intermediate_size
UpperCamelCase :List[Any] = num_hidden_layers
UpperCamelCase :Tuple = num_attention_heads
UpperCamelCase :Any = patch_size
UpperCamelCase :Optional[int] = image_size
UpperCamelCase :Any = initializer_range
UpperCamelCase :Any = attention_dropout
UpperCamelCase :Dict = layer_norm_eps
UpperCamelCase :int = hidden_act
UpperCamelCase :Optional[Any] = qkv_bias
@classmethod
def _A ( cls : Union[str, Any] , __lowerCamelCase : Union[str, os.PathLike] , **__lowerCamelCase : List[str] ):
cls._set_token_in_kwargs(__lowerCamelCase )
UpperCamelCase :str = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase :Optional[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : str = """instructblip_qformer"""
def __init__( self : Optional[Any] , __lowerCamelCase : Optional[int]=30_522 , __lowerCamelCase : Dict=768 , __lowerCamelCase : int=12 , __lowerCamelCase : str=12 , __lowerCamelCase : Optional[Any]=3_072 , __lowerCamelCase : List[Any]="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Union[str, Any]=512 , __lowerCamelCase : List[Any]=0.02 , __lowerCamelCase : Optional[int]=1E-12 , __lowerCamelCase : Tuple=0 , __lowerCamelCase : int="absolute" , __lowerCamelCase : str=2 , __lowerCamelCase : str=1_408 , **__lowerCamelCase : Tuple , ):
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
UpperCamelCase :str = vocab_size
UpperCamelCase :Tuple = hidden_size
UpperCamelCase :Any = num_hidden_layers
UpperCamelCase :Optional[int] = num_attention_heads
UpperCamelCase :Dict = hidden_act
UpperCamelCase :Tuple = intermediate_size
UpperCamelCase :Tuple = hidden_dropout_prob
UpperCamelCase :int = attention_probs_dropout_prob
UpperCamelCase :List[str] = max_position_embeddings
UpperCamelCase :Optional[int] = initializer_range
UpperCamelCase :List[str] = layer_norm_eps
UpperCamelCase :int = position_embedding_type
UpperCamelCase :int = cross_attention_frequency
UpperCamelCase :Union[str, Any] = encoder_hidden_size
@classmethod
def _A ( cls : Optional[int] , __lowerCamelCase : Union[str, os.PathLike] , **__lowerCamelCase : Any ):
cls._set_token_in_kwargs(__lowerCamelCase )
UpperCamelCase :Optional[Any] = cls.get_config_dict(__lowerCamelCase , **__lowerCamelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
UpperCamelCase :Optional[Any] = config_dict["""qformer_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__lowerCamelCase , **__lowerCamelCase )
class _SCREAMING_SNAKE_CASE ( _a ):
snake_case__ : Tuple = """instructblip"""
snake_case__ : Optional[Any] = True
def __init__( self : Any , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : List[str]=None , __lowerCamelCase : Dict=32 , **__lowerCamelCase : int ):
super().__init__(**__lowerCamelCase )
if vision_config is None:
UpperCamelCase :List[Any] = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
UpperCamelCase :Dict = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
UpperCamelCase :Union[str, Any] = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
UpperCamelCase :Dict = InstructBlipVisionConfig(**__lowerCamelCase )
UpperCamelCase :str = InstructBlipQFormerConfig(**__lowerCamelCase )
UpperCamelCase :Optional[Any] = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
UpperCamelCase :Optional[int] = CONFIG_MAPPING[text_model_type](**__lowerCamelCase )
UpperCamelCase :str = self.text_config.tie_word_embeddings
UpperCamelCase :Union[str, Any] = self.text_config.is_encoder_decoder
UpperCamelCase :List[str] = num_query_tokens
UpperCamelCase :Any = self.vision_config.hidden_size
UpperCamelCase :int = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCamelCase :Optional[Any] = 1.0
UpperCamelCase :Optional[int] = 0.02
@classmethod
def _A ( cls : Union[str, Any] , __lowerCamelCase : InstructBlipVisionConfig , __lowerCamelCase : InstructBlipQFormerConfig , __lowerCamelCase : PretrainedConfig , **__lowerCamelCase : List[Any] , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowerCamelCase , )
def _A ( self : Tuple ):
UpperCamelCase :int = copy.deepcopy(self.__dict__ )
UpperCamelCase :Tuple = self.vision_config.to_dict()
UpperCamelCase :int = self.qformer_config.to_dict()
UpperCamelCase :Union[str, Any] = self.text_config.to_dict()
UpperCamelCase :Any = self.__class__.model_type
return output
| 350 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[int] = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''JukeboxVQVAEConfig''',
],
'''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
'''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''JukeboxModel''',
'''JukeboxPreTrainedModel''',
'''JukeboxVQVAE''',
'''JukeboxPrior''',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
UpperCAmelCase_ : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 62 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( _A , _A , _A=None ) -> Union[str, Any]:
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Parameter(_A )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
SCREAMING_SNAKE_CASE : int = nn.Parameter(_A )
def __lowercase ( _A , _A , _A ) -> Optional[int]:
# set torch weights for 1-to-1 comparison
SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE : str = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(_A ).transpose(1 , 2 ).contiguous().view(-1 , _A ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_A ).transpose(1 , 2 ).contiguous().view(-1 , _A ) , )
set_param(
torch_layer.output.dense , torch.tensor(_A ).view(-1 , _A ).contiguous().transpose(0 , 1 ) , )
def __lowercase ( _A , _A , _A ) -> List[Any]:
# set torch weights for 1-to-1 comparison
SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[2] )
SCREAMING_SNAKE_CASE : int = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(_A ).transpose(1 , 2 ).contiguous().view(-1 , _A ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(_A ).transpose(1 , 2 ).contiguous().view(-1 , _A ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_A ).transpose(1 , 2 ).contiguous().view(-1 , _A ) , )
set_param(
torch_layer.output.dense , torch.tensor(_A ).view(-1 , _A ).contiguous().transpose(0 , 1 ) , )
def __lowercase ( _A , _A , _A ) -> Tuple:
# layernorm 1
SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
SCREAMING_SNAKE_CASE : str = np.asarray(layer_norm_a[0] )
SCREAMING_SNAKE_CASE : List[str] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(_A ) , torch.tensor(_A ) , )
# lsh weights + output
SCREAMING_SNAKE_CASE : List[str] = weights[0][1]
if len(_A ) < 4:
set_layer_weights_in_torch_lsh(_A , torch_block.attention , _A )
else:
set_layer_weights_in_torch_local(_A , torch_block.attention , _A )
# intermediate weighs
SCREAMING_SNAKE_CASE : Union[str, Any] = weights[2][0][1][2]
# Chunked Feed Forward
if len(_A ) == 4:
SCREAMING_SNAKE_CASE : Dict = intermediate_weights[2]
# layernorm 2
SCREAMING_SNAKE_CASE : Tuple = np.asarray(intermediate_weights[0][0] )
SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(_A ) , torch.tensor(_A ) , )
# intermediate dense
SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(intermediate_weights[1][0] )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(_A ).transpose(0 , 1 ).contiguous() , torch.tensor(_A ) , )
# intermediate out
SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][0] )
SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(_A ).transpose(0 , 1 ).contiguous() , torch.tensor(_A ) , )
def __lowercase ( _A , _A , _A ) -> Dict:
# reformer model
SCREAMING_SNAKE_CASE : int = torch_model.reformer
# word embeds
SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(_A ) , )
if isinstance(weights[3] , _A ):
SCREAMING_SNAKE_CASE : Dict = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
SCREAMING_SNAKE_CASE : Any = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F"{position_embeddings[emb_idx]} emb does not match"
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.tensor(_A ) )
SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
_A ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
SCREAMING_SNAKE_CASE : Optional[Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(_A , _A , _A )
# output layer norm
SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[7][0] )
SCREAMING_SNAKE_CASE : int = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(_A ) , torch.tensor(_A ) , )
# output embeddings
SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[9][0] )
SCREAMING_SNAKE_CASE : Any = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(_A ).transpose(0 , 1 ).contiguous() , torch.tensor(_A ) , )
def __lowercase ( _A , _A , _A ) -> List[str]:
# Initialise PyTorch model
SCREAMING_SNAKE_CASE : Tuple = ReformerConfig.from_json_file(_A )
print(F"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE : int = ReformerModelWithLMHead(_A )
with open(_A , """rb""" ) as f:
SCREAMING_SNAKE_CASE : Optional[Any] = pickle.load(_A )["""weights"""]
set_model_weights_in_torch(_A , _A , config.hidden_size )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _A )
if __name__ == "__main__":
UpperCAmelCase__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer 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."""
)
UpperCAmelCase__ : Any = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 245 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[Any] ="""table-transformer"""
UpperCAmelCase__ : Union[str, Any] =["""past_key_values"""]
UpperCAmelCase__ : Any ={
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self : Tuple , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : int=3 , UpperCAmelCase__ : Any=1_0_0 , UpperCAmelCase__ : Optional[Any]=6 , UpperCAmelCase__ : Dict=2_0_4_8 , UpperCAmelCase__ : Any=8 , UpperCAmelCase__ : List[str]=6 , UpperCAmelCase__ : Union[str, Any]=2_0_4_8 , UpperCAmelCase__ : int=8 , UpperCAmelCase__ : List[Any]=0.0 , UpperCAmelCase__ : Optional[Any]=0.0 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Dict="relu" , UpperCAmelCase__ : List[Any]=2_5_6 , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : Any=0.0 , UpperCAmelCase__ : Optional[int]=0.0 , UpperCAmelCase__ : Tuple=0.02 , UpperCAmelCase__ : List[str]=1.0 , UpperCAmelCase__ : Optional[int]=False , UpperCAmelCase__ : int="sine" , UpperCAmelCase__ : Dict="resnet50" , UpperCAmelCase__ : int=True , UpperCAmelCase__ : List[Any]=False , UpperCAmelCase__ : Union[str, Any]=1 , UpperCAmelCase__ : List[Any]=5 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : Any=1 , UpperCAmelCase__ : Any=1 , UpperCAmelCase__ : int=5 , UpperCAmelCase__ : Union[str, Any]=2 , UpperCAmelCase__ : int=0.1 , **UpperCAmelCase__ : Union[str, Any] , ) ->Dict:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE : str = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE : Optional[int] = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE : List[str] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE : Optional[Any] = config_class.from_dict(UpperCAmelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = None, None, None
SCREAMING_SNAKE_CASE : List[Any] = use_timm_backbone
SCREAMING_SNAKE_CASE : List[Any] = backbone_config
SCREAMING_SNAKE_CASE : Dict = num_channels
SCREAMING_SNAKE_CASE : Union[str, Any] = num_queries
SCREAMING_SNAKE_CASE : Optional[Any] = d_model
SCREAMING_SNAKE_CASE : Any = encoder_ffn_dim
SCREAMING_SNAKE_CASE : str = encoder_layers
SCREAMING_SNAKE_CASE : List[str] = encoder_attention_heads
SCREAMING_SNAKE_CASE : Tuple = decoder_ffn_dim
SCREAMING_SNAKE_CASE : Optional[Any] = decoder_layers
SCREAMING_SNAKE_CASE : str = decoder_attention_heads
SCREAMING_SNAKE_CASE : List[Any] = dropout
SCREAMING_SNAKE_CASE : List[Any] = attention_dropout
SCREAMING_SNAKE_CASE : Optional[int] = activation_dropout
SCREAMING_SNAKE_CASE : Tuple = activation_function
SCREAMING_SNAKE_CASE : int = init_std
SCREAMING_SNAKE_CASE : str = init_xavier_std
SCREAMING_SNAKE_CASE : Tuple = encoder_layerdrop
SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_layerdrop
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layers
SCREAMING_SNAKE_CASE : Any = auxiliary_loss
SCREAMING_SNAKE_CASE : List[Any] = position_embedding_type
SCREAMING_SNAKE_CASE : List[Any] = backbone
SCREAMING_SNAKE_CASE : Optional[Any] = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Optional[Any] = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE : List[Any] = class_cost
SCREAMING_SNAKE_CASE : Tuple = bbox_cost
SCREAMING_SNAKE_CASE : Dict = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE : Dict = mask_loss_coefficient
SCREAMING_SNAKE_CASE : Optional[Any] = dice_loss_coefficient
SCREAMING_SNAKE_CASE : Tuple = bbox_loss_coefficient
SCREAMING_SNAKE_CASE : List[Any] = giou_loss_coefficient
SCREAMING_SNAKE_CASE : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=UpperCAmelCase__ , **UpperCAmelCase__ )
@property
def _lowercase ( self : List[str] ) ->int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def _lowercase ( self : Any ) ->int:
"""simple docstring"""
return self.d_model
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] =version.parse("""1.11""" )
@property
def _lowercase ( self : Union[str, Any] ) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def _lowercase ( self : Optional[Any] ) ->float:
"""simple docstring"""
return 1e-5
@property
def _lowercase ( self : Tuple ) ->int:
"""simple docstring"""
return 1_2
| 245 | 1 |
"""simple docstring"""
def lowercase__(A ) ->List[Any]:
"""simple docstring"""
lowercase__ : Optional[Any]= []
if len(SCREAMING_SNAKE_CASE_ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ):
lowercase__ : List[str]= nums.pop(0 )
lowercase__ : Optional[int]= permute(SCREAMING_SNAKE_CASE_ )
for perm in permutations:
perm.append(SCREAMING_SNAKE_CASE_ )
result.extend(SCREAMING_SNAKE_CASE_ )
nums.append(SCREAMING_SNAKE_CASE_ )
return result
def lowercase__(A ) ->Dict:
"""simple docstring"""
def backtrack(A ):
if start == len(SCREAMING_SNAKE_CASE_ ) - 1:
output.append(nums[:] )
else:
for i in range(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) ):
lowercase__, lowercase__ : Tuple= nums[i], nums[start]
backtrack(start + 1 )
lowercase__, lowercase__ : Optional[int]= nums[i], nums[start] # backtrack
lowercase__ : Optional[int]= []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
a : int = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 364 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
lowercase__ : List[Any]= data
lowercase__ : Node | None= None
lowercase__ : Node | None= None
def lowercase__(A ) ->None: # In Order traversal of the tree
"""simple docstring"""
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase__(A ) ->int:
"""simple docstring"""
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase__(A ) ->bool:
"""simple docstring"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase__() ->None: # Main function for testing.
"""simple docstring"""
lowercase__ : Tuple= Node(1 )
lowercase__ : Optional[int]= Node(2 )
lowercase__ : List[str]= Node(3 )
lowercase__ : Tuple= Node(4 )
lowercase__ : Optional[int]= Node(5 )
lowercase__ : Any= Node(6 )
lowercase__ : Optional[Any]= Node(7 )
lowercase__ : Optional[int]= Node(8 )
lowercase__ : List[str]= Node(9 )
print(is_full_binary_tree(A ) )
print(depth_of_tree(A ) )
print("Tree is: " )
display(A )
if __name__ == "__main__":
main()
| 150 | 0 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase_ : int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase_ : List[str] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"""{len(upper_files)} files contain uppercase characters:""")
print('\n'.join(upper_files) + '\n')
lowerCAmelCase_ : Dict = [file for file in filepaths if ' ' in file]
if space_files:
print(f"""{len(space_files)} files contain space characters:""")
print('\n'.join(space_files) + '\n')
lowerCAmelCase_ : Any = [file for file in filepaths if '-' in file]
if hyphen_files:
print(f"""{len(hyphen_files)} files contain hyphen characters:""")
print('\n'.join(hyphen_files) + '\n')
lowerCAmelCase_ : Optional[Any] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"""{len(nodir_files)} files are not in a directory:""")
print('\n'.join(nodir_files) + '\n')
lowerCAmelCase_ : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 63 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
UpperCamelCase__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case__ , unittest.TestCase ):
_a : int = CamembertTokenizer
_a : Dict = CamembertTokenizerFast
_a : Tuple = True
_a : List[Any] = True
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = "<pad>"
__lowerCAmelCase = 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 ):
"""simple docstring"""
__lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>NOTUSED" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(_A ) , 1_0_0_4 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_5 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = CamembertTokenizer(_A )
tokenizer.save_pretrained(self.tmpdirname )
__lowerCAmelCase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowerCAmelCase = tokenizer.convert_ids_to_tokens(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = "I was born in 92000, and this is falsé."
__lowerCAmelCase = tokenizer.tokenize(_A )
__lowerCAmelCase = rust_tokenizer.tokenize(_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = tokenizer.encode(_A , add_special_tokens=_A )
__lowerCAmelCase = rust_tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__lowerCAmelCase = self.get_rust_tokenizer()
__lowerCAmelCase = tokenizer.encode(_A )
__lowerCAmelCase = rust_tokenizer.encode(_A )
self.assertListEqual(_A , _A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowerCAmelCase = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name="camembert-base" , revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf" , sequences=_A , )
| 92 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
A__ = [
0X6_a_0_9_e_6_6_7,
0Xb_b_6_7_a_e_8_5,
0X3_c_6_e_f_3_7_2,
0Xa_5_4_f_f_5_3_a,
0X5_1_0_e_5_2_7_f,
0X9_b_0_5_6_8_8_c,
0X1_f_8_3_d_9_a_b,
0X5_b_e_0_c_d_1_9,
]
# Initialize round constants
A__ = [
0X4_2_8_a_2_f_9_8,
0X7_1_3_7_4_4_9_1,
0Xb_5_c_0_f_b_c_f,
0Xe_9_b_5_d_b_a_5,
0X3_9_5_6_c_2_5_b,
0X5_9_f_1_1_1_f_1,
0X9_2_3_f_8_2_a_4,
0Xa_b_1_c_5_e_d_5,
0Xd_8_0_7_a_a_9_8,
0X1_2_8_3_5_b_0_1,
0X2_4_3_1_8_5_b_e,
0X5_5_0_c_7_d_c_3,
0X7_2_b_e_5_d_7_4,
0X8_0_d_e_b_1_f_e,
0X9_b_d_c_0_6_a_7,
0Xc_1_9_b_f_1_7_4,
0Xe_4_9_b_6_9_c_1,
0Xe_f_b_e_4_7_8_6,
0X0_f_c_1_9_d_c_6,
0X2_4_0_c_a_1_c_c,
0X2_d_e_9_2_c_6_f,
0X4_a_7_4_8_4_a_a,
0X5_c_b_0_a_9_d_c,
0X7_6_f_9_8_8_d_a,
0X9_8_3_e_5_1_5_2,
0Xa_8_3_1_c_6_6_d,
0Xb_0_0_3_2_7_c_8,
0Xb_f_5_9_7_f_c_7,
0Xc_6_e_0_0_b_f_3,
0Xd_5_a_7_9_1_4_7,
0X0_6_c_a_6_3_5_1,
0X1_4_2_9_2_9_6_7,
0X2_7_b_7_0_a_8_5,
0X2_e_1_b_2_1_3_8,
0X4_d_2_c_6_d_f_c,
0X5_3_3_8_0_d_1_3,
0X6_5_0_a_7_3_5_4,
0X7_6_6_a_0_a_b_b,
0X8_1_c_2_c_9_2_e,
0X9_2_7_2_2_c_8_5,
0Xa_2_b_f_e_8_a_1,
0Xa_8_1_a_6_6_4_b,
0Xc_2_4_b_8_b_7_0,
0Xc_7_6_c_5_1_a_3,
0Xd_1_9_2_e_8_1_9,
0Xd_6_9_9_0_6_2_4,
0Xf_4_0_e_3_5_8_5,
0X1_0_6_a_a_0_7_0,
0X1_9_a_4_c_1_1_6,
0X1_e_3_7_6_c_0_8,
0X2_7_4_8_7_7_4_c,
0X3_4_b_0_b_c_b_5,
0X3_9_1_c_0_c_b_3,
0X4_e_d_8_a_a_4_a,
0X5_b_9_c_c_a_4_f,
0X6_8_2_e_6_f_f_3,
0X7_4_8_f_8_2_e_e,
0X7_8_a_5_6_3_6_f,
0X8_4_c_8_7_8_1_4,
0X8_c_c_7_0_2_0_8,
0X9_0_b_e_f_f_f_a,
0Xa_4_5_0_6_c_e_b,
0Xb_e_f_9_a_3_f_7,
0Xc_6_7_1_7_8_f_2,
]
A__ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def snake_case__ ( lowercase_ : bytes )-> bytes:
'''simple docstring'''
A__ = B'\x80' + (B'\x00' * (6_3 - (len(lowercase_ ) + 8) % 6_4))
A__ = struct.pack('>Q',(len(lowercase_ ) * 8) )
return data + padding + big_endian_integer
def snake_case__ ( self : Optional[int] )-> None:
'''simple docstring'''
A__ = [
self.preprocessed_data[x : x + 6_4]
for x in range(0,len(self.preprocessed_data ),6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
A__ = list(struct.unpack('>16L',lowercase_ ) )
# add 48 0-ed integers
words += [0] * 4_8
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = self.hashes
for index in range(0,6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
A__ = (
self.ror(words[index - 1_5],7 )
^ self.ror(words[index - 1_5],1_8 )
^ (words[index - 1_5] >> 3)
)
A__ = (
self.ror(words[index - 2],1_7 )
^ self.ror(words[index - 2],1_9 )
^ (words[index - 2] >> 1_0)
)
A__ = (
words[index - 1_6] + sa + words[index - 7] + sa
) % 0X1_0_0_0_0_0_0_0_0
# Compression
A__ = self.ror(lowercase_,6 ) ^ self.ror(lowercase_,1_1 ) ^ self.ror(lowercase_,2_5 )
A__ = (e & f) ^ ((~e & 0Xf_f_f_f_f_f_f_f) & g)
A__ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0_0_0_0_0_0_0_0
A__ = self.ror(lowercase_,2 ) ^ self.ror(lowercase_,1_3 ) ^ self.ror(lowercase_,2_2 )
A__ = (a & b) ^ (a & c) ^ (b & c)
A__ = (sa + maj) % 0X1_0_0_0_0_0_0_0_0
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = (
g,
f,
e,
((d + tempa) % 0X1_0_0_0_0_0_0_0_0),
c,
b,
a,
((tempa + tempa) % 0X1_0_0_0_0_0_0_0_0),
)
A__ = [a, b, c, d, e, f, g, h]
# Modify final values
A__ = [
((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
A__ = ''.join([hex(lowercase_ )[2:].zfill(8 ) for value in self.hashes] )
def snake_case__ ( self : Union[str, Any],lowercase_ : int,lowercase_ : int )-> int:
'''simple docstring'''
return 0Xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : List[str] )-> None:
'''simple docstring'''
import hashlib
A__ = bytes('Test String','utf-8' )
self.assertEqual(SHAaaa(lowercase_ ).hash,hashlib.shaaaa(lowercase_ ).hexdigest() )
def _snake_case( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
A__ = argparse.ArgumentParser()
parser.add_argument(
'-s' , '--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument(
'-f' , '--file' , dest='input_file' , help='Hash contents of a file' )
A__ = parser.parse_args()
A__ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
A__ = f.read()
else:
A__ = bytes(SCREAMING_SNAKE_CASE__ , 'utf-8' )
print(SHAaaa(SCREAMING_SNAKE_CASE__ ).hash )
if __name__ == "__main__":
main()
| 282 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
_SCREAMING_SNAKE_CASE : int = {
"169M": 12,
"430M": 24,
"1B5": 24,
"3B": 32,
"7B": 32,
"14B": 40,
}
_SCREAMING_SNAKE_CASE : Dict = {
"169M": 768,
"430M": 1024,
"1B5": 2048,
"3B": 2560,
"7B": 4096,
"14B": 5120,
}
def UpperCamelCase_( snake_case : Union[str, Any] ):
'''simple docstring'''
snake_case_ = list(state_dict.keys() )
for name in state_dict_keys:
snake_case_ = state_dict.pop(snake_case )
# emb -> embedding
if name.startswith("emb." ):
snake_case_ = name.replace("emb." , "embeddings." )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith("blocks.0.ln0" ):
snake_case_ = name.replace("blocks.0.ln0" , "blocks.0.pre_ln" )
# att -> attention
snake_case_ = re.sub(r"blocks\.(\d+)\.att" , r"blocks.\1.attention" , snake_case )
# ffn -> feed_forward
snake_case_ = re.sub(r"blocks\.(\d+)\.ffn" , r"blocks.\1.feed_forward" , snake_case )
# time_mix_k -> time_mix_key and reshape
if name.endswith(".time_mix_k" ):
snake_case_ = name.replace(".time_mix_k" , ".time_mix_key" )
# time_mix_v -> time_mix_value and reshape
if name.endswith(".time_mix_v" ):
snake_case_ = name.replace(".time_mix_v" , ".time_mix_value" )
# time_mix_r -> time_mix_key and reshape
if name.endswith(".time_mix_r" ):
snake_case_ = name.replace(".time_mix_r" , ".time_mix_receptance" )
if name != "head.weight":
snake_case_ = "rwkv." + name
snake_case_ = weight
return state_dict
def UpperCamelCase_( snake_case : List[Any] , snake_case : str , snake_case : Optional[Any] , snake_case : str=None , snake_case : Union[str, Any]=None , snake_case : Any=False , snake_case : Tuple=None ):
'''simple docstring'''
if tokenizer_file is None:
print("No `--tokenizer_file` provided, we will use the default tokenizer." )
snake_case_ = 5_0_2_7_7
snake_case_ = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b" )
else:
snake_case_ = PreTrainedTokenizerFast(tokenizer_file=snake_case )
snake_case_ = len(snake_case )
tokenizer.save_pretrained(snake_case )
# 2. Build the config
snake_case_ = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
snake_case_ = candidate
break
if size is None:
raise ValueError("Could not infer the size, please provide it with the `--size` argument." )
if size not in possible_sizes:
raise ValueError(f'`size` should be one of {possible_sizes}, got {size}.' )
snake_case_ = RwkvConfig(
vocab_size=snake_case , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(snake_case )
# 3. Download model file then convert state_dict
snake_case_ = hf_hub_download(snake_case , snake_case )
snake_case_ = torch.load(snake_case , map_location="cpu" )
snake_case_ = convert_state_dict(snake_case )
# 4. Split in shards and save
snake_case_ , snake_case_ = shard_checkpoint(snake_case )
for shard_file, shard in shards.items():
torch.save(snake_case , os.path.join(snake_case , snake_case ) )
if index is not None:
snake_case_ = os.path.join(snake_case , snake_case )
# Save the index as well
with open(snake_case , "w" , encoding="utf-8" ) as f:
snake_case_ = json.dumps(snake_case , indent=2 , sort_keys=snake_case ) + "\n"
f.write(snake_case )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
"Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model." )
snake_case_ = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
snake_case_ = torch.load(os.path.join(snake_case , snake_case ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(snake_case , snake_case ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError("Please provide a `model_name` to push the model to the Hub." )
snake_case_ = AutoModelForCausalLM.from_pretrained(snake_case )
model.push_to_hub(snake_case , max_shard_size="2GB" )
tokenizer.push_to_hub(snake_case )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--repo_id", default=None, type=str, required=True, help="Repo ID from which to pull the checkpoint."
)
parser.add_argument(
"--checkpoint_file", default=None, type=str, required=True, help="Name of the checkpoint file in the repo."
)
parser.add_argument(
"--output_dir", default=None, type=str, required=True, help="Where to save the converted model."
)
parser.add_argument(
"--tokenizer_file",
default=None,
type=str,
help="Path to the tokenizer file to use (if not provided, only the model is converted).",
)
parser.add_argument(
"--size",
default=None,
type=str,
help="Size of the model. Will be inferred from the `checkpoint_file` if not passed.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
help="Push to the Hub the converted model.",
)
parser.add_argument(
"--model_name",
default=None,
type=str,
help="Name of the pushed model on the Hub, including the username / organization.",
)
_SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 85 |
'''simple docstring'''
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snake_case_ = min(snake_case , snake_case )
while j > 0:
c[j] += c[j - 1]
j -= 1
return c[r]
print(binomial_coefficient(n=10, r=5))
| 85 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = """https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE__ = {
"""jukebox-1b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""1b_lyrics/prior_level_2.pth.tar""",
],
"""jukebox-5b-lyrics""": [
"""5b/vqvae.pth.tar""",
"""5b/prior_level_0.pth.tar""",
"""5b/prior_level_1.pth.tar""",
"""5b_lyrics/prior_level_2.pth.tar""",
],
}
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Dict ):
'''simple docstring'''
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
lowercase_ = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
lowercase_ = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
lowercase_ = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
lowercase_ = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
lowercase_ = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
lowercase_ = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
lowercase_ = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
lowercase_ = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Any , __lowerCamelCase: int , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[int] ):
'''simple docstring'''
lowercase_ = {}
import re
lowercase_ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
lowercase_ = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
lowercase_ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
lowercase_ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
lowercase_ = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
lowercase_ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
lowercase_ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
lowercase_ = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
lowercase_ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(__lowerCamelCase ):
lowercase_ = re_encoder_block_conv_in.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[2] ) * 2 + int(groups[3] )
lowercase_ = F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
lowercase_ = re_encoder_block_conv_in.sub(__lowerCamelCase , __lowerCamelCase )
elif re_encoder_block_resnet.fullmatch(__lowerCamelCase ):
lowercase_ = re_encoder_block_resnet.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[2] ) * 2 + int(groups[3] )
lowercase_ = {"1": 1, "3": 2}[groups[-2]]
lowercase_ = F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
lowercase_ = F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
lowercase_ = prefix + resnet_block
lowercase_ = re_encoder_block_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase ):
lowercase_ = re_encoder_block_proj_out.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
lowercase_ = re_encoder_block_proj_out.sub(__lowerCamelCase , __lowerCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(__lowerCamelCase ):
lowercase_ = re_decoder_block_conv_out.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowercase_ = F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
lowercase_ = re_decoder_block_conv_out.sub(__lowerCamelCase , __lowerCamelCase )
elif re_decoder_block_resnet.fullmatch(__lowerCamelCase ):
lowercase_ = re_decoder_block_resnet.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
lowercase_ = {"1": 1, "3": 2}[groups[-2]]
lowercase_ = F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
lowercase_ = F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
lowercase_ = prefix + resnet_block
lowercase_ = re_decoder_block_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_decoder_block_proj_in.fullmatch(__lowerCamelCase ):
lowercase_ = re_decoder_block_proj_in.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
lowercase_ = re_decoder_block_proj_in.sub(__lowerCamelCase , __lowerCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(__lowerCamelCase ):
lowercase_ = re_prior_cond_conv_out.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowercase_ = F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
lowercase_ = re_prior_cond_conv_out.sub(__lowerCamelCase , __lowerCamelCase )
elif re_prior_cond_resnet.fullmatch(__lowerCamelCase ):
lowercase_ = re_prior_cond_resnet.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
lowercase_ = {"1": 1, "3": 2}[groups[-2]]
lowercase_ = F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
lowercase_ = F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
lowercase_ = prefix + resnet_block
lowercase_ = re_prior_cond_resnet.sub(__lowerCamelCase , __lowerCamelCase )
elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase ):
lowercase_ = re_prior_cond_proj_in.match(__lowerCamelCase )
lowercase_ = regex_match.groups()
lowercase_ = F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
lowercase_ = re_prior_cond_proj_in.sub(__lowerCamelCase , __lowerCamelCase )
# keep original key
else:
lowercase_ = original_key
lowercase_ = replace_key(__lowerCamelCase )
if F'{key_prefix}.{key}' not in model_state_dict or key is None:
print(F'failed converting {original_key} to {key}, does not match' )
# handle missmatched shape
elif value.shape != model_state_dict[F'{key_prefix}.{key}'].shape:
lowercase_ = model_state_dict[F'{key_prefix}.{key}']
print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match' )
lowercase_ = original_key
lowercase_ = original_key
lowercase_ = value
return new_dict
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Tuple=None , __lowerCamelCase: List[str]=None ):
'''simple docstring'''
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' ):
lowercase_ = requests.get(F'{PREFIX}{file}' , allow_redirects=__lowerCamelCase )
os.makedirs(F'{pytorch_dump_folder_path}/' , exist_ok=__lowerCamelCase )
open(F'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' , "wb" ).write(r.content )
lowercase_ = MODEL_MAPPING[model_name.split("/" )[-1]]
lowercase_ = JukeboxConfig.from_pretrained(__lowerCamelCase )
lowercase_ = JukeboxModel(__lowerCamelCase )
lowercase_ = []
lowercase_ = {}
for i, dict_name in enumerate(__lowerCamelCase ):
lowercase_ = torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["model"]
lowercase_ = {}
for k in old_dic.keys():
if k.endswith(".b" ):
lowercase_ = old_dic[k]
elif k.endswith(".w" ):
lowercase_ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
lowercase_ = old_dic[k]
else:
lowercase_ = old_dic[k]
lowercase_ = "vqvae" if i == 0 else F'priors.{3 - i}'
lowercase_ = fix_jukebox_keys(__lowerCamelCase , model.state_dict() , __lowerCamelCase , __lowerCamelCase )
weight_dict.append(__lowerCamelCase )
lowercase_ = weight_dict.pop(0 )
model.vqvae.load_state_dict(__lowerCamelCase )
for i in range(len(__lowerCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
with open(F'{pytorch_dump_folder_path}/mapping.json' , "w" ) as txtfile:
json.dump(__lowerCamelCase , __lowerCamelCase )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
return weight_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""jukebox-5b-lyrics""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""jukebox-5b-lyrics-converted""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 297 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __lowerCamelCase ( snake_case_ ):
"""simple docstring"""
lowerCAmelCase__ = 42
class __lowerCamelCase ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_to_config
def __init__( self , UpperCAmelCase = 16 , UpperCAmelCase = 88 , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = 0.0 , UpperCAmelCase = 32 , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = "geglu" , UpperCAmelCase = True , UpperCAmelCase = True , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
lowercase_ = num_attention_heads
lowercase_ = attention_head_dim
lowercase_ = num_attention_heads * attention_head_dim
lowercase_ = in_channels
lowercase_ = torch.nn.GroupNorm(num_groups=UpperCAmelCase , num_channels=UpperCAmelCase , eps=1e-6 , affine=UpperCAmelCase )
lowercase_ = nn.Linear(UpperCAmelCase , UpperCAmelCase )
# 3. Define transformers blocks
lowercase_ = nn.ModuleList(
[
BasicTransformerBlock(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , dropout=UpperCAmelCase , cross_attention_dim=UpperCAmelCase , activation_fn=UpperCAmelCase , attention_bias=UpperCAmelCase , double_self_attention=UpperCAmelCase , norm_elementwise_affine=UpperCAmelCase , )
for d in range(UpperCAmelCase )
] )
lowercase_ = nn.Linear(UpperCAmelCase , UpperCAmelCase )
def A__ ( self , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=1 , UpperCAmelCase=None , UpperCAmelCase = True , ) -> Optional[Any]:
'''simple docstring'''
lowercase_ , lowercase_ , lowercase_ , lowercase_ = hidden_states.shape
lowercase_ = batch_frames // num_frames
lowercase_ = hidden_states
lowercase_ = hidden_states[None, :].reshape(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
lowercase_ = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
lowercase_ = self.norm(UpperCAmelCase )
lowercase_ = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , UpperCAmelCase , UpperCAmelCase )
lowercase_ = self.proj_in(UpperCAmelCase )
# 2. Blocks
for block in self.transformer_blocks:
lowercase_ = block(
UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , timestep=UpperCAmelCase , cross_attention_kwargs=UpperCAmelCase , class_labels=UpperCAmelCase , )
# 3. Output
lowercase_ = self.proj_out(UpperCAmelCase )
lowercase_ = (
hidden_states[None, None, :]
.reshape(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
lowercase_ = hidden_states.reshape(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
lowercase_ = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=UpperCAmelCase )
| 297 | 1 |
from __future__ import annotations
import math
def a ( A__ : int ) -> list[int]:
"""simple docstring"""
if num <= 0:
_lowercase =F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(A__ )
_lowercase =[True] * (num + 1)
_lowercase =[]
_lowercase =2
_lowercase =int(math.sqrt(A__ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(A__ )
# Set multiples of start be False
for i in range(start * start , num + 1 , A__ ):
if sieve[i] is True:
_lowercase =False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(A__ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input('Enter a positive integer: ').strip())))
| 205 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class __lowerCAmelCase ( TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
def __init__( self , lowerCAmelCase=None , **lowerCAmelCase ) -> Optional[Any]:
'''simple docstring'''
super().__init__(features=lowerCAmelCase )
_lowercase =torch_tensor_kwargs
import torch # noqa import torch at initialization
def A__ ( self , lowerCAmelCase ) -> int:
'''simple docstring'''
import torch
if isinstance(lowerCAmelCase , lowerCAmelCase ) and column:
if all(
isinstance(lowerCAmelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(lowerCAmelCase )
return column
def A__ ( self , lowerCAmelCase ) -> str:
'''simple docstring'''
import torch
if isinstance(lowerCAmelCase , (str, bytes, type(lowerCAmelCase )) ):
return value
elif isinstance(lowerCAmelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_lowercase ={}
if isinstance(lowerCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
_lowercase ={'dtype': torch.intaa}
elif isinstance(lowerCAmelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_lowercase ={'dtype': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCAmelCase , PIL.Image.Image ):
_lowercase =np.asarray(lowerCAmelCase )
return torch.tensor(lowerCAmelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def A__ ( self , lowerCAmelCase ) -> str:
'''simple docstring'''
import torch
# support for torch, tf, jax etc.
if hasattr(lowerCAmelCase , '__array__' ) and not isinstance(lowerCAmelCase , torch.Tensor ):
_lowercase =data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCAmelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCAmelCase ) for substruct in data_struct] )
elif isinstance(lowerCAmelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCAmelCase ) for substruct in data_struct] )
return self._tensorize(lowerCAmelCase )
def A__ ( self , lowerCAmelCase ) -> Tuple:
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCAmelCase , map_list=lowerCAmelCase )
def A__ ( self , lowerCAmelCase ) -> Mapping:
'''simple docstring'''
_lowercase =self.numpy_arrow_extractor().extract_row(lowerCAmelCase )
_lowercase =self.python_features_decoder.decode_row(lowerCAmelCase )
return self.recursive_tensorize(lowerCAmelCase )
def A__ ( self , lowerCAmelCase ) -> "torch.Tensor":
'''simple docstring'''
_lowercase =self.numpy_arrow_extractor().extract_column(lowerCAmelCase )
_lowercase =self.python_features_decoder.decode_column(lowerCAmelCase , pa_table.column_names[0] )
_lowercase =self.recursive_tensorize(lowerCAmelCase )
_lowercase =self._consolidate(lowerCAmelCase )
return column
def A__ ( self , lowerCAmelCase ) -> Mapping:
'''simple docstring'''
_lowercase =self.numpy_arrow_extractor().extract_batch(lowerCAmelCase )
_lowercase =self.python_features_decoder.decode_batch(lowerCAmelCase )
_lowercase =self.recursive_tensorize(lowerCAmelCase )
for column_name in batch:
_lowercase =self._consolidate(batch[column_name] )
return batch
| 205 | 1 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class a :
_snake_case : str
_snake_case : str = None
@staticmethod
def lowerCAmelCase_ ( ):
raise NotImplementedError
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : str , **__lowerCAmelCase : Union[str, Any] ):
raise NotImplementedError
def lowerCAmelCase_ ( self : Optional[Any] , __lowerCAmelCase : Union[str, Any] ):
raise NotImplementedError
def lowerCAmelCase_ ( self : List[Any] ):
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCAmelCase_ ( cls : List[str] ):
return f'''`pip install {cls.pip_package or cls.name}`'''
class a ( lowerCAmelCase_ ):
_snake_case : Any = 'optuna'
@staticmethod
def lowerCAmelCase_ ( ):
return is_optuna_available()
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : str , **__lowerCAmelCase : Union[str, Any] ):
return run_hp_search_optuna(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : List[str] ):
return default_hp_space_optuna(__lowerCAmelCase )
class a ( lowerCAmelCase_ ):
_snake_case : Optional[int] = 'ray'
_snake_case : Dict = '\'ray[tune]\''
@staticmethod
def lowerCAmelCase_ ( ):
return is_ray_available()
def lowerCAmelCase_ ( self : Dict , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int , __lowerCAmelCase : str , **__lowerCAmelCase : List[Any] ):
return run_hp_search_ray(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : List[Any] ):
return default_hp_space_ray(__lowerCAmelCase )
class a ( lowerCAmelCase_ ):
_snake_case : Optional[Any] = 'sigopt'
@staticmethod
def lowerCAmelCase_ ( ):
return is_sigopt_available()
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : str , **__lowerCAmelCase : int ):
return run_hp_search_sigopt(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : Tuple ):
return default_hp_space_sigopt(__lowerCAmelCase )
class a ( lowerCAmelCase_ ):
_snake_case : Optional[Any] = 'wandb'
@staticmethod
def lowerCAmelCase_ ( ):
return is_wandb_available()
def lowerCAmelCase_ ( self : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : int , __lowerCAmelCase : str , **__lowerCAmelCase : List[str] ):
return run_hp_search_wandb(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : List[str] , __lowerCAmelCase : Dict ):
return default_hp_space_wandb(__lowerCAmelCase )
UpperCAmelCase__ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowercase ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(lowercase ) > 1:
logger.info(
f'''{len(lowercase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 30 | """simple docstring"""
import os
import pytest
from attr import dataclass
UpperCAmelCase__ = """us-east-1""" # defaults region
@dataclass
class a :
_snake_case : str
_snake_case : Tuple = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_snake_case : List[Any] = {
'task_name': 'mnli',
'per_device_train_batch_size': 16,
'per_device_eval_batch_size': 16,
'do_train': True,
'do_eval': True,
'do_predict': True,
'output_dir': '/opt/ml/model',
'overwrite_output_dir': True,
'max_steps': 5_00,
'save_steps': 55_00,
}
_snake_case : Optional[Any] = {**hyperparameters, 'max_steps': 10_00}
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def lowerCAmelCase_ ( self : Dict ):
return f'''{self.framework}-transfromers-test'''
@property
def lowerCAmelCase_ ( self : Union[str, Any] ):
return f'''./tests/sagemaker/scripts/{self.framework}'''
@property
def lowerCAmelCase_ ( self : Dict ):
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="""class""" )
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = SageMakerTestEnvironment(framework=request.cls.framework )
| 30 | 1 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [chr(i + 65 ) for i in range(26 )]
# Remove duplicate characters from key
__lowerCAmelCase = remove_duplicates(key.upper() )
__lowerCAmelCase = len(_UpperCamelCase )
# First fill cipher with key characters
__lowerCAmelCase = {alphabet[i]: char for i, char in enumerate(_UpperCamelCase )}
# Then map remaining characters in alphabet to
# the alphabet from the beginning
for i in range(len(_UpperCamelCase ) , 26 ):
__lowerCAmelCase = alphabet[i - offset]
# Ensure we are not mapping letters to letters previously mapped
while char in key:
offset -= 1
__lowerCAmelCase = alphabet[i - offset]
__lowerCAmelCase = char
return cipher_alphabet
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return "".join(cipher_map.get(_UpperCamelCase , _UpperCamelCase ) for ch in message.upper() )
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = {v: k for k, v in cipher_map.items()}
return "".join(rev_cipher_map.get(_UpperCamelCase , _UpperCamelCase ) for ch in message.upper() )
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = input("Enter message to encode or decode: " ).strip()
__lowerCAmelCase = input("Enter keyword: " ).strip()
__lowerCAmelCase = input("Encipher or decipher? E/D:" ).strip()[0].lower()
try:
__lowerCAmelCase = {"e": encipher, "d": decipher}[option]
except KeyError:
raise KeyError("invalid input option" )
__lowerCAmelCase = create_cipher_map(_UpperCamelCase )
print(func(_UpperCamelCase , _UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
__lowerCAmelCase = 2
__lowerCAmelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
__lowerCAmelCase = i
while n % i == 0:
__lowerCAmelCase = n // i
i += 1
return int(_UpperCamelCase )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 57 | 1 |
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 lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = old_name
if "patch_embed" in old_name:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = old_name.split("." )
if layer == "0":
_SCREAMING_SNAKE_CASE : Any = old_name.replace("0", "convolution1" )
elif layer == "1":
_SCREAMING_SNAKE_CASE : Dict = old_name.replace("1", "batchnorm_before" )
elif layer == "3":
_SCREAMING_SNAKE_CASE : List[Any] = old_name.replace("3", "convolution2" )
else:
_SCREAMING_SNAKE_CASE : List[Any] = old_name.replace("4", "batchnorm_after" )
if "network" in old_name and re.search(R"\d\.\d", __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : str = R"\b\d{2}\b"
if bool(re.search(__lowerCamelCase, __lowerCamelCase ) ):
_SCREAMING_SNAKE_CASE : Optional[Any] = re.search(R"\d\.\d\d.", __lowerCamelCase ).group()
else:
_SCREAMING_SNAKE_CASE : List[Any] = re.search(R"\d\.\d.", __lowerCamelCase ).group()
if int(match[0] ) < 6:
_SCREAMING_SNAKE_CASE : Tuple = old_name.replace(__lowerCamelCase, "" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = trimmed_name.replace("network", match[0] + ".meta4D_layers.blocks." + match[2:-1] )
_SCREAMING_SNAKE_CASE : List[Any] = "intermediate_stages." + trimmed_name
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = old_name.replace(__lowerCamelCase, "" )
if int(match[2] ) < num_meta4D_last_stage:
_SCREAMING_SNAKE_CASE : Dict = trimmed_name.replace("network", "meta4D_layers.blocks." + match[2] )
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = str(int(match[2] ) - num_meta4D_last_stage )
_SCREAMING_SNAKE_CASE : Any = trimmed_name.replace("network", "meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
_SCREAMING_SNAKE_CASE : Union[str, Any] = trimmed_name.replace("norm1", "layernorm1" )
elif "norm2" in old_name:
_SCREAMING_SNAKE_CASE : Optional[int] = trimmed_name.replace("norm2", "layernorm2" )
elif "fc1" in old_name:
_SCREAMING_SNAKE_CASE : str = trimmed_name.replace("fc1", "linear_in" )
elif "fc2" in old_name:
_SCREAMING_SNAKE_CASE : Optional[Any] = trimmed_name.replace("fc2", "linear_out" )
_SCREAMING_SNAKE_CASE : str = "last_stage." + trimmed_name
elif "network" in old_name and re.search(R".\d.", __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[str] = old_name.replace("network", "intermediate_stages" )
if "fc" in new_name:
_SCREAMING_SNAKE_CASE : str = new_name.replace("fc", "convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
_SCREAMING_SNAKE_CASE : Tuple = new_name.replace("norm1", "batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
_SCREAMING_SNAKE_CASE : Any = new_name.replace("norm2", "batchnorm_after" )
if "proj" in new_name:
_SCREAMING_SNAKE_CASE : Any = new_name.replace("proj", "projection" )
if "dist_head" in new_name:
_SCREAMING_SNAKE_CASE : Optional[int] = new_name.replace("dist_head", "distillation_classifier" )
elif "head" in new_name:
_SCREAMING_SNAKE_CASE : Union[str, Any] = new_name.replace("head", "classifier" )
elif "patch_embed" in new_name:
_SCREAMING_SNAKE_CASE : str = "efficientformer." + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
_SCREAMING_SNAKE_CASE : Any = new_name.replace("norm", "layernorm" )
_SCREAMING_SNAKE_CASE : int = "efficientformer." + new_name
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = "efficientformer.encoder." + new_name
return new_name
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase ):
for key in checkpoint.copy().keys():
_SCREAMING_SNAKE_CASE : Dict = checkpoint.pop(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Union[str, Any] = val
return checkpoint
def lowerCamelCase__ ():
_SCREAMING_SNAKE_CASE : str = "http://images.cocodataset.org/val2017/000000039769.jpg"
_SCREAMING_SNAKE_CASE : Tuple = Image.open(requests.get(__lowerCamelCase, stream=__lowerCamelCase ).raw )
return image
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = torch.load(__lowerCamelCase, map_location="cpu" )["model"]
_SCREAMING_SNAKE_CASE : Dict = EfficientFormerConfig.from_json_file(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = EfficientFormerForImageClassificationWithTeacher(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
_SCREAMING_SNAKE_CASE : List[Any] = config.depths[-1] - config.num_metaad_blocks + 1
_SCREAMING_SNAKE_CASE : List[str] = convert_torch_checkpoint(__lowerCamelCase, __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
model.eval()
_SCREAMING_SNAKE_CASE : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
# prepare image
_SCREAMING_SNAKE_CASE : Union[str, Any] = prepare_img()
_SCREAMING_SNAKE_CASE : Optional[int] = 256
_SCREAMING_SNAKE_CASE : List[Any] = 224
_SCREAMING_SNAKE_CASE : List[str] = EfficientFormerImageProcessor(
size={"shortest_edge": image_size}, crop_size={"height": crop_size, "width": crop_size}, resample=pillow_resamplings["bicubic"], )
_SCREAMING_SNAKE_CASE : Tuple = processor(images=__lowerCamelCase, return_tensors="pt" ).pixel_values
# original processing pipeline
_SCREAMING_SNAKE_CASE : Any = Compose(
[
Resize(__lowerCamelCase, interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(__lowerCamelCase ),
ToTensor(),
Normalize(__lowerCamelCase, __lowerCamelCase ),
] )
_SCREAMING_SNAKE_CASE : Optional[int] = image_transforms(__lowerCamelCase ).unsqueeze(0 )
assert torch.allclose(__lowerCamelCase, __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = model(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = outputs.logits
_SCREAMING_SNAKE_CASE : Tuple = (1, 1000)
if "l1" in model_name:
_SCREAMING_SNAKE_CASE : int = 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, :10], __lowerCamelCase, atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
_SCREAMING_SNAKE_CASE : int = 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, :10], __lowerCamelCase, atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
_SCREAMING_SNAKE_CASE : Dict = 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(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
processor.save_pretrained(__lowerCamelCase )
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=__lowerCamelCase, )
processor.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""", commit_message="Add image processor", use_temp_dir=__lowerCamelCase, )
if __name__ == "__main__":
UpperCamelCase__ =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)
UpperCamelCase__ =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,
) | 352 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = 'timesformer'
def __init__( self , __lowerCamelCase=2_2_4 , __lowerCamelCase=1_6 , __lowerCamelCase=3 , __lowerCamelCase=8 , __lowerCamelCase=7_6_8 , __lowerCamelCase=1_2 , __lowerCamelCase=1_2 , __lowerCamelCase=3_0_7_2 , __lowerCamelCase="gelu" , __lowerCamelCase=0.0 , __lowerCamelCase=0.0 , __lowerCamelCase=0.02 , __lowerCamelCase=1E-6 , __lowerCamelCase=True , __lowerCamelCase="divided_space_time" , __lowerCamelCase=0 , **__lowerCamelCase , ) -> List[str]:
super().__init__(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = image_size
_SCREAMING_SNAKE_CASE : str = patch_size
_SCREAMING_SNAKE_CASE : str = num_channels
_SCREAMING_SNAKE_CASE : str = num_frames
_SCREAMING_SNAKE_CASE : Dict = hidden_size
_SCREAMING_SNAKE_CASE : Any = num_hidden_layers
_SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
_SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
_SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
_SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : int = initializer_range
_SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
_SCREAMING_SNAKE_CASE : List[str] = qkv_bias
_SCREAMING_SNAKE_CASE : Tuple = attention_type
_SCREAMING_SNAKE_CASE : Union[str, Any] = drop_path_rate | 325 | 0 |
import argparse
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
########################################################################
# This is a fully working simple example to use Accelerate
#
# 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[int] = 1_6
A : Tuple = 3_2
def UpperCamelCase ( __magic_name__ : Accelerator , __magic_name__ : int = 16 ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = AutoTokenizer.from_pretrained("""bert-base-cased""" )
lowercase__ = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__magic_name__ : Tuple ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase )
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():
lowercase__ = datasets.map(
__lowerCAmelCase , batched=__lowerCAmelCase , 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
lowercase__ = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__magic_name__ : Optional[int] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase__ = 16
elif accelerator.mixed_precision != "no":
lowercase__ = 8
else:
lowercase__ = None
return tokenizer.pad(
__lowerCAmelCase , padding="""longest""" , max_length=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
lowercase__ = DataLoader(
tokenized_datasets["""train"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase , drop_last=__lowerCAmelCase )
lowercase__ = DataLoader(
tokenized_datasets["""validation"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : Union[str, Any] ) -> Any:
"""simple docstring"""
lowercase__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ = config["""lr"""]
lowercase__ = int(config["""num_epochs"""] )
lowercase__ = int(config["""seed"""] )
lowercase__ = int(config["""batch_size"""] )
lowercase__ = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
lowercase__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase__ = batch_size // MAX_GPU_BATCH_SIZE
lowercase__ = MAX_GPU_BATCH_SIZE
set_seed(__lowerCAmelCase )
lowercase__ , lowercase__ = get_dataloaders(__lowerCAmelCase , __lowerCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=__lowerCAmelCase )
# 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).
lowercase__ = model.to(accelerator.device )
# Instantiate optimizer
lowercase__ = AdamW(params=model.parameters() , lr=__lowerCAmelCase )
# Instantiate scheduler
lowercase__ = get_linear_schedule_with_warmup(
optimizer=__lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(__lowerCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# 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.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = accelerator.prepare(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Now we train the model
for epoch in range(__lowerCAmelCase ):
model.train()
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowercase__ = model(**__lowerCAmelCase )
lowercase__ = outputs.loss
lowercase__ = loss / gradient_accumulation_steps
accelerator.backward(__lowerCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__lowerCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase__ = model(**__lowerCAmelCase )
lowercase__ = outputs.logits.argmax(dim=-1 )
lowercase__ , lowercase__ = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=__lowerCAmelCase , references=__lowerCAmelCase , )
lowercase__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , __lowerCAmelCase )
def UpperCamelCase ( ) -> Any:
"""simple docstring"""
lowercase__ = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=__lowerCAmelCase , default=__lowerCAmelCase , 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.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
lowercase__ = parser.parse_args()
lowercase__ = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(__lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
main()
| 305 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 270 | 0 |
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_lowerCAmelCase : Optional[int] = str(bin(_lowerCamelCase ) )[2:] # remove the leading "0b"
_lowerCAmelCase : List[str] = str(bin(_lowerCamelCase ) )[2:]
_lowerCAmelCase : Dict = max(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
return "0b" + "".join(
str(int("1" in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(_lowerCamelCase ) , b_binary.zfill(_lowerCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 300 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_snake_case = True
from torch.cuda.amp import autocast
_snake_case = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
lowerCamelCase__ = field(
default=a , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCamelCase__ = field(
default=a , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'})
lowerCamelCase__ = field(
default=a , metadata={'help': 'Whether to log verbose messages or not.'} , )
lowerCamelCase__ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'})
lowerCamelCase__ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'})
lowerCamelCase__ = field(
default=0.9_9_9_9_9_5 , metadata={'help': 'Decay of gumbel temperature during training.'})
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
_lowerCAmelCase : Optional[Any] = logging.WARNING
if model_args.verbose_logging:
_lowerCAmelCase : Dict = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
_lowerCAmelCase : str = logging.INFO
logger.setLevel(_lowerCamelCase )
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ = field(
default=a , metadata={'help': 'The name of the dataset to use (via the datasets library).'})
lowerCamelCase__ = field(
default=a , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
lowerCamelCase__ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
lowerCamelCase__ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
lowerCamelCase__ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
lowerCamelCase__ = field(
default=a , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'})
lowerCamelCase__ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
lowerCamelCase__ = field(
default=a , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowerCamelCase__ = field(
default=2_0.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'})
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = "longest"
lowerCamelCase__ = None
lowerCamelCase__ = None
def __call__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Any = self.feature_extractor.pad(
__a, max_length=self.max_length, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", )
_lowerCAmelCase : Tuple = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1])
_lowerCAmelCase : Optional[Any] = batch["input_values"].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
_lowerCAmelCase : List[str] = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1)).to(
torch.long)
_lowerCAmelCase : Dict = torch.zeros(
(batch_size, mask_indices_seq_length), dtype=torch.long, device=batch["input_values"].device)
# these two operations makes sure that all values
# before the output lengths indices are attended to
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : Union[str, Any] = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
# sample randomly masked indices
_lowerCAmelCase : Optional[Any] = _compute_mask_indices(
(batch_size, mask_indices_seq_length), self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=__a, min_masks=2, )
return batch
class UpperCAmelCase_ ( a):
def __init__( self, *__a, __a=1, __a=0, __a=1.0, **__a):
'''simple docstring'''
super().__init__(*__a, **__a)
_lowerCAmelCase : Dict = 0
_lowerCAmelCase : List[str] = max_gumbel_temp
_lowerCAmelCase : List[Any] = min_gumbel_temp
_lowerCAmelCase : int = gumbel_temp_decay
def snake_case__ ( self, __a, __a):
'''simple docstring'''
model.train()
_lowerCAmelCase : str = self._prepare_inputs(__a)
if self.use_amp:
with autocast():
_lowerCAmelCase : Any = self.compute_loss(__a, __a)
else:
_lowerCAmelCase : Dict = self.compute_loss(__a, __a)
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
_lowerCAmelCase : List[str] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
_lowerCAmelCase : Union[str, Any] = loss.sum() / (inputs["mask_time_indices"]).sum()
else:
raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']")
if self.args.gradient_accumulation_steps > 1:
_lowerCAmelCase : List[str] = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__a).backward()
elif self.use_apex:
with amp.scale_loss(__a, self.optimizer) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__a)
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp))
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp))
return loss.detach()
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Any = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = parser.parse_args_into_dataclasses()
configure_logger(_lowerCamelCase , _lowerCamelCase )
# Downloading and loading a dataset from the hub.
_lowerCAmelCase : List[Any] = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
_lowerCAmelCase : int = DatasetDict()
_lowerCAmelCase : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]" , cache_dir=model_args.cache_dir , )
_lowerCAmelCase : List[str] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]" , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
_lowerCAmelCase : List[str] = DatasetDict()
_lowerCAmelCase : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
_lowerCAmelCase : Union[str, Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}" , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
_lowerCAmelCase : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=_lowerCamelCase )
def prepare_dataset(_lowerCamelCase ):
# check that all files have the correct sampling rate
_lowerCAmelCase , _lowerCAmelCase : Any = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
_lowerCAmelCase : Dict = datasets.map(
_lowerCamelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names )
# filter audio files that are too long
_lowerCAmelCase : Tuple = vectorized_datasets.filter(
lambda _lowerCamelCase : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(_lowerCamelCase ):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
_lowerCAmelCase : Dict = vectorized_datasets.map(
_lowerCamelCase , batched=_lowerCamelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["train"].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
_lowerCAmelCase : Tuple = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
" ``config.feat_extract_norm='layer'" )
_lowerCAmelCase : Union[str, Any] = WavaVecaForPreTraining(_lowerCamelCase )
_lowerCAmelCase : int = DataCollatorForWavaVecaPretraining(model=_lowerCamelCase , feature_extractor=_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = WavaVecaPreTrainer(
model=_lowerCamelCase , data_collator=_lowerCamelCase , args=_lowerCamelCase , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=_lowerCamelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 300 | 1 |
from math import isclose, sqrt
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )-> Dict:
lowerCAmelCase_ : List[Any] = point_y / 4 / point_x
lowerCAmelCase_ : Any = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCAmelCase_ : Tuple = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCAmelCase_ : str = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCAmelCase_ : Tuple = outgoing_gradient**2 + 4
lowerCAmelCase_ : List[str] = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCAmelCase_ : Dict = (point_y - outgoing_gradient * point_x) ** 2 - 100
lowerCAmelCase_ : Optional[Any] = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCAmelCase_ : List[Any] = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCAmelCase_ : Dict = x_minus if isclose(_lowercase , _lowercase ) else x_plus
lowerCAmelCase_ : Optional[Any] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def lowerCAmelCase ( lowerCAmelCase_ = 1.4 , lowerCAmelCase_ = -9.6 )-> Tuple:
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : float = first_x_coord
lowerCAmelCase_ : float = first_y_coord
lowerCAmelCase_ : float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCAmelCase_ : List[Any] = next_point(_lowercase , _lowercase , _lowercase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f"""{solution() = }""") | 262 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: Any ) -> str:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=snake_case , )
assert hasattr(self , """env""" )
def lowerCAmelCase_ ( self: int , snake_case: Dict ) -> List[Any]:
# configuration for running training on smdistributed Model Parallel
snake_case_ :Tuple = {
"""enabled""": True,
"""processes_per_host""": 8,
}
snake_case_ :List[Any] = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
snake_case_ :Tuple = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
snake_case_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=snake_case , instance_type=self.instance_type , debugger_hook_config=snake_case , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=snake_case , py_version="""py36""" , )
def lowerCAmelCase_ ( self: Any , snake_case: Tuple ) -> List[str]:
TrainingJobAnalytics(snake_case ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def lowerCAmelCase_ ( self: Dict , snake_case: Dict ) -> List[Any]:
# create estimator
snake_case_ :List[Any] = self.create_estimator(snake_case )
# run training
estimator.fit()
# result dataframe
snake_case_ :Any = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
snake_case_ :Tuple = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
snake_case_ :Dict = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
snake_case_ :int = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , snake_case )
| 66 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
def is_in_circle(_snake_case : float , _snake_case : float ) -> bool:
__a =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__a =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(_snake_case ) )
# The ratio of the area for circle to square is pi/4.
__a =proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def UpperCamelCase_( _snake_case : int , _snake_case : Callable[[float], float] , _snake_case : float = 0.0 , _snake_case : float = 1.0 , ):
"""simple docstring"""
return mean(
function_to_integrate(uniform(_snake_case , _snake_case ) ) for _ in range(_snake_case ) ) * (max_value - min_value)
def UpperCamelCase_( _snake_case : int , _snake_case : float = 0.0 , _snake_case : float = 1.0 ):
"""simple docstring"""
def identity_function(_snake_case : float ) -> float:
return x
__a =area_under_curve_estimator(
_snake_case , _snake_case , _snake_case , _snake_case )
__a =(max_value * max_value - min_value * min_value) / 2
print('******************' )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print('******************' )
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
def function_to_integrate(_snake_case : float ) -> float:
return sqrt(4.0 - x * x )
__a =area_under_curve_estimator(
_snake_case , _snake_case , 0.0 , 2.0 )
print('******************' )
print('Estimating pi using area_under_curve_estimator' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print('******************' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 308 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> Optional[Any]:
'''simple docstring'''
__a =()
for resnet, attn in zip(self.resnets , self.attentions ):
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> int:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=__snake_case , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_downsample:
__a =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case=True ) -> Optional[int]:
'''simple docstring'''
__a =()
for resnet in self.resnets:
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
output_states += (hidden_states,)
if self.add_downsample:
__a =self.downsamplers_a(__snake_case )
output_states += (hidden_states,)
return hidden_states, output_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =[]
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =resnets
__a =attentions
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a =[]
for i in range(self.num_layers ):
__a =self.in_channels if (i == self.num_layers - 1) else self.out_channels
__a =self.prev_output_channel if i == 0 else self.out_channels
__a =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
if self.add_upsample:
__a =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[Any]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
__a =res_hidden_states_tuple[-1]
__a =res_hidden_states_tuple[:-1]
__a =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
if self.add_upsample:
__a =self.upsamplers_a(__snake_case )
return hidden_states
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 0.0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = jnp.floataa
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
# there is always at least one resnet
__a =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
__a =[]
for _ in range(self.num_layers ):
__a =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__snake_case )
__a =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__snake_case )
__a =resnets
__a =attentions
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case=True ) -> List[str]:
'''simple docstring'''
__a =self.resnets[0](__snake_case , __snake_case )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
__a =attn(__snake_case , __snake_case , deterministic=__snake_case )
__a =resnet(__snake_case , __snake_case , deterministic=__snake_case )
return hidden_states
| 308 | 1 |
'''simple docstring'''
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
snake_case_ : Any = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class lowercase__ ( lowercase , unittest.TestCase ):
lowercase__ = XLNetTokenizer
lowercase__ = XLNetTokenizerFast
lowercase__ = True
lowercase__ = True
def UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCamelCase : List[Any] = XLNetTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = '<s>'
_UpperCamelCase : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) ,lowerCamelCase__ )
def UpperCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = 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(lowerCamelCase__ ) ,1006 )
def UpperCamelCase_ ( self : int ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,1000 )
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = XLNetTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )
_UpperCamelCase : Dict = tokenizer.tokenize('This is a test' )
self.assertListEqual(lowerCamelCase__ ,['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,[285, 46, 10, 170, 382] )
_UpperCamelCase : Union[str, Any] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowerCamelCase__ ,[
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',
'.',
] ,)
_UpperCamelCase : Union[str, Any] = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
_UpperCamelCase : Optional[Any] = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ ,[
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 UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
_UpperCamelCase : Any = XLNetTokenizer(lowerCamelCase__ ,do_lower_case=lowerCamelCase__ )
_UpperCamelCase : Any = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowerCamelCase__ ,[
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 UpperCamelCase_ ( self : str ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = XLNetTokenizer(lowerCamelCase__ ,do_lower_case=lowerCamelCase__ )
_UpperCamelCase : Any = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
lowerCamelCase__ ,[
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 UpperCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = XLNetTokenizer.from_pretrained('xlnet-base-cased' )
_UpperCamelCase : List[Any] = tokenizer.encode('sequence builders' ,add_special_tokens=lowerCamelCase__ )
_UpperCamelCase : Tuple = tokenizer.encode('multi-sequence build' ,add_special_tokens=lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
_UpperCamelCase : Any = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ ,lowerCamelCase__ )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
# fmt: off
_UpperCamelCase : Any = {'input_ids': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 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, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 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, 13894, 3380, 23, 95, 18, 17634, 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=lowerCamelCase__ ,model_name='xlnet-base-cased' ,revision='c841166438c31ec7ca9a106dee7bb312b73ae511' ,)
| 83 |
"""simple docstring"""
# Copyright 2023 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 173 | 0 |
"""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
__A = logging.get_logger(__name__)
__A = '▁'
__A = {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}
__A = {
'vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
},
'monolingual_vocab_file': {
'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
},
}
__A = {'vinai/bartpho-syllable': 1024}
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
lowerCamelCase = VOCAB_FILES_NAMES
lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase="<s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="</s>" , __UpperCAmelCase="<s>" , __UpperCAmelCase="<unk>" , __UpperCAmelCase="<pad>" , __UpperCAmelCase="<mask>" , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase =AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase ) if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else mask_token
_lowerCAmelCase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , )
_lowerCAmelCase =vocab_file
_lowerCAmelCase =monolingual_vocab_file
_lowerCAmelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__UpperCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_lowerCAmelCase ={}
_lowerCAmelCase =0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__UpperCAmelCase ) not in self.fairseq_tokens_to_ids:
_lowerCAmelCase =cnt
cnt += 1
with open(__UpperCAmelCase , """r""" , encoding="""utf-8""" ) as f:
for line in f.readlines():
_lowerCAmelCase =line.strip().split()[0]
_lowerCAmelCase =len(self.fairseq_tokens_to_ids )
if str(__UpperCAmelCase ) not in self.fairseq_tokens_to_ids:
_lowerCAmelCase =len(self.fairseq_tokens_to_ids )
_lowerCAmelCase ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Dict:
_lowerCAmelCase =self.__dict__.copy()
_lowerCAmelCase =None
_lowerCAmelCase =self.sp_model.serialized_model_proto()
return state
def __setstate__( self , __UpperCAmelCase ) -> List[Any]:
_lowerCAmelCase =d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_lowerCAmelCase ={}
_lowerCAmelCase =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
_lowerCAmelCase =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__UpperCAmelCase )) + [1]
return [1] + ([0] * len(__UpperCAmelCase )) + [1, 1] + ([0] * len(__UpperCAmelCase )) + [1]
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[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 ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def _lowerCAmelCase ( self ) -> List[Any]:
_lowerCAmelCase ={self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> List[str]:
return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Optional[int]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Union[str, Any]:
return self.fairseq_ids_to_tokens[index]
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Union[str, Any]:
_lowerCAmelCase ="""""".join(__UpperCAmelCase ).replace(__UpperCAmelCase , """ """ ).strip()
return out_string
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase = None ) -> Tuple[str]:
if not os.path.isdir(__UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_lowerCAmelCase =os.path.join(
__UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_lowerCAmelCase =os.path.join(
__UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__UpperCAmelCase , """wb""" ) as fi:
_lowerCAmelCase =self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__UpperCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __UpperCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__UpperCAmelCase , """w""" , encoding="""utf-8""" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__UpperCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 356 |
"""simple docstring"""
def _lowerCamelCase(__UpperCamelCase ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =len(__UpperCamelCase )
for i in range(n - 1 ):
for j in range(i + 1 , __UpperCamelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _lowerCamelCase(__UpperCamelCase ) -> List[Any]:
if len(__UpperCamelCase ) <= 1:
return arr, 0
_lowerCAmelCase =len(__UpperCamelCase ) // 2
_lowerCAmelCase =arr[0:mid]
_lowerCAmelCase =arr[mid:]
_lowerCAmelCase , _lowerCAmelCase =count_inversions_recursive(__UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =count_inversions_recursive(__UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =_count_cross_inversions(__UpperCamelCase , __UpperCamelCase )
_lowerCAmelCase =inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase ) -> Any:
_lowerCAmelCase =[]
_lowerCAmelCase =_lowerCAmelCase =_lowerCAmelCase =0
while i < len(__UpperCamelCase ) and j < len(__UpperCamelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__UpperCamelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__UpperCamelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _lowerCamelCase() -> str:
_lowerCAmelCase =[10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
_lowerCAmelCase =count_inversions_bf(__UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ , __UpperCamelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
_lowerCAmelCase =count_inversions_bf(__UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , __UpperCamelCase )
# an empty list should also have zero inversions
_lowerCAmelCase =[]
_lowerCAmelCase =count_inversions_bf(__UpperCamelCase )
_lowerCAmelCase , _lowerCAmelCase =count_inversions_recursive(__UpperCamelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , __UpperCamelCase )
if __name__ == "__main__":
main()
| 341 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowercase( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self: List[str] ):
'''simple docstring'''
super().tearDown()
gc.collect()
def UpperCamelCase_ ( self: List[Any] ):
'''simple docstring'''
_snake_case , _snake_case : Union[str, Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""", revision="""bf16""", dtype=jnp.bfloataa, )
_snake_case : str = """A painting of a squirrel eating a burger"""
_snake_case : Union[str, Any] = jax.device_count()
_snake_case : str = num_samples * [prompt]
_snake_case : Union[str, Any] = sd_pipe.prepare_inputs(a_ )
_snake_case : List[Any] = replicate(a_ )
_snake_case : str = shard(a_ )
_snake_case : List[Any] = jax.random.PRNGKey(0 )
_snake_case : Tuple = jax.random.split(a_, jax.device_count() )
_snake_case : Union[str, Any] = sd_pipe(a_, a_, a_, num_inference_steps=25, jit=a_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_snake_case : Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case : List[Any] = images[0, 253:256, 253:256, -1]
_snake_case : Optional[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case : Any = jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def UpperCamelCase_ ( self: Tuple ):
'''simple docstring'''
_snake_case : str = """stabilityai/stable-diffusion-2"""
_snake_case , _snake_case : Dict = FlaxDPMSolverMultistepScheduler.from_pretrained(a_, subfolder="""scheduler""" )
_snake_case , _snake_case : Any = FlaxStableDiffusionPipeline.from_pretrained(
a_, scheduler=a_, revision="""bf16""", dtype=jnp.bfloataa, )
_snake_case : Tuple = scheduler_params
_snake_case : Dict = """A painting of a squirrel eating a burger"""
_snake_case : str = jax.device_count()
_snake_case : List[Any] = num_samples * [prompt]
_snake_case : str = sd_pipe.prepare_inputs(a_ )
_snake_case : List[str] = replicate(a_ )
_snake_case : Tuple = shard(a_ )
_snake_case : Union[str, Any] = jax.random.PRNGKey(0 )
_snake_case : Dict = jax.random.split(a_, jax.device_count() )
_snake_case : str = sd_pipe(a_, a_, a_, num_inference_steps=25, jit=a_ )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
_snake_case : Tuple = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
_snake_case : List[Any] = images[0, 253:256, 253:256, -1]
_snake_case : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
_snake_case : Dict = jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 64 |
import argparse
from collections import defaultdict
import yaml
A : str = '''docs/source/en/_toctree.yml'''
def __lowerCamelCase ( __a :str ) -> List[Any]:
"""simple docstring"""
A__ = defaultdict(__a )
A__ = []
A__ = []
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(__a )
A__ = new_doc_list
A__ = [key for key, value in counts.items() if value > 1]
A__ = []
for duplicate_key in duplicates:
A__ = list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key} )
if len(__a ) > 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] )
A__ = sorted(__a , key=lambda __a : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(__a ) > 1:
raise ValueError("""{doc_list} has two 'overview' docs which is not allowed.""" )
overview_doc.extend(__a )
# Sort
return overview_doc
def __lowerCamelCase ( __a :Any=False ) -> List[str]:
"""simple docstring"""
with open(__a , 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[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
A__ = api_doc[scheduler_idx]["""sections"""]
A__ = clean_doc_toc(__a )
A__ = False
if new_scheduler_doc != scheduler_doc:
A__ = True
if overwrite:
A__ = new_scheduler_doc
if diff:
if overwrite:
A__ = api_doc
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__a , allow_unicode=__a ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
def __lowerCamelCase ( __a :Optional[int]=False ) -> Dict:
"""simple docstring"""
with open(__a , 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[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
A__ = False
A__ = api_doc[pipeline_idx]["""sections"""]
A__ = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
A__ = pipeline_doc["""section"""]
A__ = clean_doc_toc(__a )
if overwrite:
A__ = new_sub_pipeline_doc
new_pipeline_docs.append(__a )
# sort overall pipeline doc
A__ = clean_doc_toc(__a )
if new_pipeline_docs != pipeline_docs:
A__ = True
if overwrite:
A__ = new_pipeline_docs
if diff:
if overwrite:
A__ = api_doc
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__a , allow_unicode=__a ) )
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__":
A : Tuple = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A : Optional[Any] = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 274 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowercase__ = StableUnCLIPImgaImgPipeline
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
lowercase__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowercase__ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase__ = frozenset([] )
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = 3_2
lowercase_ = embedder_hidden_size
# image encoding components
lowercase_ = CLIPImageProcessor(crop_size=3_2 , size=3_2)
torch.manual_seed(0)
lowercase_ = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowercase_ , projection_dim=lowercase_ , num_hidden_layers=5 , num_attention_heads=4 , image_size=3_2 , intermediate_size=3_7 , patch_size=1 , ))
# regular denoising components
torch.manual_seed(0)
lowercase_ = StableUnCLIPImageNormalizer(embedding_dim=lowercase_)
lowercase_ = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""")
torch.manual_seed(0)
lowercase_ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
torch.manual_seed(0)
lowercase_ = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowercase_ , projection_dim=3_2 , 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 , ))
torch.manual_seed(0)
lowercase_ = UNetaDConditionModel(
sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(3_2, 6_4) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowercase_ , layers_per_block=1 , upcast_attention=lowercase_ , use_linear_projection=lowercase_ , )
torch.manual_seed(0)
lowercase_ = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.00_085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=lowercase_ , steps_offset=1 , )
torch.manual_seed(0)
lowercase_ = AutoencoderKL()
lowercase_ = {
# image encoding components
"""feature_extractor""": feature_extractor,
"""image_encoder""": image_encoder.eval(),
# image noising components
"""image_normalizer""": image_normalizer.eval(),
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder.eval(),
"""unet""": unet.eval(),
"""scheduler""": scheduler,
"""vae""": vae.eval(),
}
return components
def _UpperCAmelCase ( self : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int=0 , lowerCAmelCase_ : Dict=True):
"""simple docstring"""
if str(lowercase_).startswith("""mps"""):
lowercase_ = torch.manual_seed(lowercase_)
else:
lowercase_ = torch.Generator(device=lowercase_).manual_seed(lowercase_)
lowercase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(lowercase_)).to(lowercase_)
if pil_image:
lowercase_ = input_image * 0.5 + 0.5
lowercase_ = input_image.clamp(0 , 1)
lowercase_ = input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
lowercase_ = DiffusionPipeline.numpy_to_pil(lowercase_)[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase_ = self.get_dummy_components()
lowercase_ = StableUnCLIPImgaImgPipeline(**lowercase_)
lowercase_ = sd_pipe.to(lowercase_)
sd_pipe.set_progress_bar_config(disable=lowercase_)
lowercase_ = self.get_dummy_inputs(lowercase_)
inputs.update({"""image_embeds""": None})
lowercase_ = sd_pipe(**lowercase_).images
lowercase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
lowercase_ = np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
def _UpperCAmelCase ( self : Any):
"""simple docstring"""
lowercase_ = torch_device in ["""cpu""", """mps"""]
self._test_attention_slicing_forward_pass(test_max_difference=lowercase_)
def _UpperCAmelCase ( self : Union[str, Any]):
"""simple docstring"""
lowercase_ = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=lowercase_)
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowercase_)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : List[Any]):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCAmelCase ( self : Optional[Any]):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
lowercase_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy""")
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-l-img2img""" , torch_dtype=torch.floataa)
pipe.to(lowercase_)
pipe.set_progress_bar_config(disable=lowercase_)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="""cpu""").manual_seed(0)
lowercase_ = pipe(lowercase_ , """anime turle""" , generator=lowercase_ , output_type="""np""")
lowercase_ = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_)
def _UpperCAmelCase ( self : str):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
lowercase_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy""")
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa)
pipe.to(lowercase_)
pipe.set_progress_bar_config(disable=lowercase_)
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = torch.Generator(device="""cpu""").manual_seed(0)
lowercase_ = pipe(lowercase_ , """anime turle""" , generator=lowercase_ , output_type="""np""")
lowercase_ = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_)
def _UpperCAmelCase ( self : int):
"""simple docstring"""
lowercase_ = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png""")
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"""fusing/stable-unclip-2-1-h-img2img""" , torch_dtype=torch.floataa)
lowercase_ = pipe.to(lowercase_)
pipe.set_progress_bar_config(disable=lowercase_)
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowercase_ = pipe(
lowercase_ , """anime turtle""" , num_inference_steps=2 , output_type="""np""" , )
lowercase_ = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 1_0**9
| 369 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ = """"""
else:
lowercase_ = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight''' )
lowercase_ = state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ = in_proj_weight[
: config.hidden_size, :
]
lowercase_ = in_proj_bias[: config.hidden_size]
lowercase_ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ = in_proj_bias[-config.hidden_size :]
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> List[str]:
'''simple docstring'''
lowercase_ = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.bias""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = dct.pop(__lowerCAmelCase )
lowercase_ = val
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
'''simple docstring'''
lowercase_ = ViTMSNConfig()
lowercase_ = 10_00
lowercase_ = """datasets/huggingface/label-files"""
lowercase_ = """imagenet-1k-id2label.json"""
lowercase_ = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase ) , """r""" ) )
lowercase_ = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
lowercase_ = idalabel
lowercase_ = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
lowercase_ = 3_84
lowercase_ = 15_36
lowercase_ = 6
elif "l16" in checkpoint_url:
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
elif "b4" in checkpoint_url:
lowercase_ = 4
elif "l7" in checkpoint_url:
lowercase_ = 7
lowercase_ = 10_24
lowercase_ = 40_96
lowercase_ = 24
lowercase_ = 16
lowercase_ = 0.1
lowercase_ = ViTMSNModel(__lowerCAmelCase )
lowercase_ = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )["""target_encoder"""]
lowercase_ = ViTImageProcessor(size=config.image_size )
remove_projection_head(__lowerCAmelCase )
lowercase_ = create_rename_keys(__lowerCAmelCase , base_model=__lowerCAmelCase )
for src, dest in rename_keys:
rename_key(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase , base_model=__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
lowercase_ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
lowercase_ = ViTImageProcessor(
size=config.image_size , image_mean=__lowerCAmelCase , image_std=__lowerCAmelCase )
lowercase_ = image_processor(images=__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
lowercase_ = model(**__lowerCAmelCase )
lowercase_ = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
lowercase_ = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
lowercase_ = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
lowercase_ = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
lowercase_ = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
lowercase_ = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :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__":
UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
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."
)
UpperCAmelCase : Tuple = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313 | 0 |
"""simple docstring"""
from typing import Any
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
_validation(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
# Creates data structures and fill initial step
UpperCamelCase = {}
UpperCamelCase = {}
for state in states_space:
UpperCamelCase = observations_space[0]
UpperCamelCase = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
UpperCamelCase = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase = observations_space[o]
UpperCamelCase = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
UpperCamelCase = ''''''
UpperCamelCase = -1
for k_state in states_space:
UpperCamelCase = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
UpperCamelCase = probability
UpperCamelCase = k_state
# Update probabilities and pointers dicts
UpperCamelCase = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
UpperCamelCase = arg_max
# The final observation
UpperCamelCase = observations_space[len(_SCREAMING_SNAKE_CASE ) - 1]
# argmax for given final observation
UpperCamelCase = ''''''
UpperCamelCase = -1
for k_state in states_space:
UpperCamelCase = probabilities[(k_state, final_observation)]
if probability > max_probability:
UpperCamelCase = probability
UpperCamelCase = k_state
UpperCamelCase = arg_max
# Process pointers backwards
UpperCamelCase = last_state
UpperCamelCase = []
for o in range(len(_SCREAMING_SNAKE_CASE ) - 1 , -1 , -1 ):
result.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase = pointers[previous, observations_space[o]]
result.reverse()
return result
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
_validate_not_empty(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
_validate_lists(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_validate_dicts(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There\'s an empty parameter" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_validate_list(_SCREAMING_SNAKE_CASE , "observations_space" )
_validate_list(_SCREAMING_SNAKE_CASE , "states_space" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not isinstance(_object , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = F"{var_name} must be a list"
raise ValueError(_SCREAMING_SNAKE_CASE )
else:
for x in _object:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = F"{var_name} must be a list of strings"
raise ValueError(_SCREAMING_SNAKE_CASE )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
_validate_dict(_SCREAMING_SNAKE_CASE , "initial_probabilities" , _SCREAMING_SNAKE_CASE )
_validate_nested_dict(_SCREAMING_SNAKE_CASE , "transition_probabilities" )
_validate_nested_dict(_SCREAMING_SNAKE_CASE , "emission_probabilities" )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_validate_dict(_object , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for x in _object.values():
_validate_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if not isinstance(_object , _SCREAMING_SNAKE_CASE ):
UpperCamelCase = F"{var_name} must be a dict"
raise ValueError(_SCREAMING_SNAKE_CASE )
if not all(isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for x in _object ):
UpperCamelCase = F"{var_name} all keys must be strings"
raise ValueError(_SCREAMING_SNAKE_CASE )
if not all(isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for x in _object.values() ):
UpperCamelCase = '''nested dictionary ''' if nested else ''''''
UpperCamelCase = F"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 153 |
from collections import deque
from math import floor
from random import random
from time import time
class __magic_name__ :
def __init__( self : Optional[int] ) -> str:
'''simple docstring'''
UpperCamelCase__ : str = {}
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[int]=1 ) -> Any:
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
UpperCamelCase__ : List[Any] = [[w, v]]
if not self.graph.get(lowerCamelCase__ ):
UpperCamelCase__ : Any = []
def UpperCAmelCase__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return list(self.graph )
def UpperCAmelCase__ ( self : List[str] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase__ )
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase__ : int=-2 , lowerCamelCase__ : int=-1 ) -> List[Any]:
'''simple docstring'''
if s == d:
return []
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : Dict = []
if s == -2:
UpperCamelCase__ : Optional[Any] = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Union[str, Any] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : Optional[int] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : int = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return visited
def UpperCAmelCase__ ( self : str , lowerCamelCase__ : Optional[int]=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
UpperCamelCase__ : int = floor(random() * 10000 ) + 10
for i in range(lowerCamelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase__ : Optional[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase__ , lowerCamelCase__ , 1 )
def UpperCAmelCase__ ( self : List[str] , lowerCamelCase__ : Tuple=-2 ) -> str:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = deque()
UpperCamelCase__ : Optional[Any] = []
if s == -2:
UpperCamelCase__ : Optional[int] = list(self.graph )[0]
d.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
while d:
UpperCamelCase__ : List[str] = 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 UpperCAmelCase__ ( self : int , lowerCamelCase__ : List[Any] ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : List[str] ) -> int:
'''simple docstring'''
return len(self.graph[u] )
def UpperCAmelCase__ ( self : Dict , lowerCamelCase__ : List[str]=-2 ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : int = []
UpperCamelCase__ : Optional[int] = []
if s == -2:
UpperCamelCase__ : Dict = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = s
UpperCamelCase__ : Dict = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Optional[int] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : List[Any] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : Union[str, Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return sorted_nodes
def UpperCAmelCase__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = []
UpperCamelCase__ : int = []
UpperCamelCase__ : List[Any] = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : Dict = -2
UpperCamelCase__ : int = []
UpperCamelCase__ : Tuple = s
UpperCamelCase__ : str = False
UpperCamelCase__ : Optional[int] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Dict = 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
):
UpperCamelCase__ : Union[str, Any] = len(lowerCamelCase__ ) - 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] )
UpperCamelCase__ : Tuple = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : Optional[int] = True
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : List[Any] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : Optional[Any] = False
indirect_parents.append(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : Optional[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return list(lowerCamelCase__ )
def UpperCAmelCase__ ( self : Tuple ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : List[Any] = []
UpperCamelCase__ : Any = []
UpperCamelCase__ : Tuple = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : int = -2
UpperCamelCase__ : Optional[int] = []
UpperCamelCase__ : Tuple = s
UpperCamelCase__ : List[str] = False
UpperCamelCase__ : Tuple = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Any = 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
):
UpperCamelCase__ : List[str] = len(lowerCamelCase__ ) - 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] )
UpperCamelCase__ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : List[str] = True
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : Optional[Any] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : List[str] = False
indirect_parents.append(lowerCamelCase__ )
UpperCamelCase__ : Tuple = s
UpperCamelCase__ : List[Any] = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return False
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase__ : Union[str, Any]=-2 , lowerCamelCase__ : Union[str, Any]=-1 ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = time()
self.dfs(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : int = time()
return end - begin
def UpperCAmelCase__ ( self : Tuple , lowerCamelCase__ : int=-2 ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : List[str] = time()
self.bfs(lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = time()
return end - begin
class __magic_name__ :
def __init__( self : Optional[Any] ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Dict = {}
def UpperCAmelCase__ ( self : int , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Tuple=1 ) -> Dict:
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
# 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
UpperCamelCase__ : Union[str, Any] = [[w, v]]
# add the other way
if self.graph.get(lowerCamelCase__ ):
# 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
UpperCamelCase__ : int = [[w, u]]
def UpperCAmelCase__ ( self : Any , lowerCamelCase__ : Any , lowerCamelCase__ : List[Any] ) -> Tuple:
'''simple docstring'''
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCamelCase__ )
# the other way round
if self.graph.get(lowerCamelCase__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCamelCase__ )
def UpperCAmelCase__ ( self : int , lowerCamelCase__ : Tuple=-2 , lowerCamelCase__ : Tuple=-1 ) -> str:
'''simple docstring'''
if s == d:
return []
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : Tuple = []
if s == -2:
UpperCamelCase__ : str = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : int = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : int = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCamelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
UpperCamelCase__ : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : Optional[Any] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : List[str] = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return visited
def UpperCAmelCase__ ( self : Dict , lowerCamelCase__ : Optional[int]=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
UpperCamelCase__ : List[Any] = floor(random() * 10000 ) + 10
for i in range(lowerCamelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
UpperCamelCase__ : str = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCamelCase__ , lowerCamelCase__ , 1 )
def UpperCAmelCase__ ( self : Optional[int] , lowerCamelCase__ : int=-2 ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : List[Any] = deque()
UpperCamelCase__ : int = []
if s == -2:
UpperCamelCase__ : Dict = list(self.graph )[0]
d.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
while d:
UpperCamelCase__ : List[str] = 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 UpperCAmelCase__ ( self : int , lowerCamelCase__ : str ) -> List[Any]:
'''simple docstring'''
return len(self.graph[u] )
def UpperCAmelCase__ ( self : Dict ) -> int:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = []
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : str = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : Dict = -2
UpperCamelCase__ : Optional[Any] = []
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : int = False
UpperCamelCase__ : str = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Tuple = 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
):
UpperCamelCase__ : Optional[int] = len(lowerCamelCase__ ) - 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] )
UpperCamelCase__ : str = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : Optional[Any] = True
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : List[str] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : Optional[Any] = False
indirect_parents.append(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : Dict = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return list(lowerCamelCase__ )
def UpperCAmelCase__ ( self : Any ) -> str:
'''simple docstring'''
UpperCamelCase__ : int = []
UpperCamelCase__ : str = []
UpperCamelCase__ : Optional[int] = list(self.graph )[0]
stack.append(lowerCamelCase__ )
visited.append(lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = -2
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Optional[int] = s
UpperCamelCase__ : str = False
UpperCamelCase__ : Any = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
UpperCamelCase__ : Optional[int] = 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
):
UpperCamelCase__ : Optional[Any] = len(lowerCamelCase__ ) - 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] )
UpperCamelCase__ : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
UpperCamelCase__ : Optional[Any] = True
if len(lowerCamelCase__ ) != 0:
UpperCamelCase__ : Optional[int] = stack[len(lowerCamelCase__ ) - 1]
else:
UpperCamelCase__ : Tuple = False
indirect_parents.append(lowerCamelCase__ )
UpperCamelCase__ : Union[str, Any] = s
UpperCamelCase__ : Dict = ss
# check if se have reached the starting point
if len(lowerCamelCase__ ) == 0:
return False
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
return list(self.graph )
def UpperCAmelCase__ ( self : List[str] , lowerCamelCase__ : Any=-2 , lowerCamelCase__ : str=-1 ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : List[str] = time()
self.dfs(lowerCamelCase__ , lowerCamelCase__ )
UpperCamelCase__ : Dict = time()
return end - begin
def UpperCAmelCase__ ( self : List[Any] , lowerCamelCase__ : str=-2 ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : List[str] = time()
self.bfs(lowerCamelCase__ )
UpperCamelCase__ : Any = time()
return end - begin
| 146 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
return "".join(chr(ord(_lowerCamelCase ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod() | 357 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_lowerCAmelCase : List[Any] = get_logger(__name__)
class A_ :
lowerCAmelCase__ = 'dummy_data'
lowerCAmelCase__ = 'datasets'
lowerCAmelCase__ = False
def __init__( self: List[str] ,__lowerCAmelCase: str ,__lowerCAmelCase: str ,__lowerCAmelCase: Union[Version, str] ,__lowerCAmelCase: Optional[str] = None ,__lowerCAmelCase: bool = False ,__lowerCAmelCase: bool = True ,__lowerCAmelCase: Optional[List[Callable]] = None ,):
'''simple docstring'''
_lowerCamelCase : str = 0
_lowerCamelCase : List[str] = dataset_name
_lowerCamelCase : Optional[int] = cache_dir
_lowerCamelCase : Optional[int] = use_local_dummy_data
_lowerCamelCase : int = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : Tuple = str(__lowerCAmelCase )
# to be downloaded
_lowerCamelCase : Optional[Any] = None
_lowerCamelCase : Dict = None
@property
def _lowercase ( self: str ):
'''simple docstring'''
if self._dummy_file is None:
_lowerCamelCase : List[str] = self.download_dummy_data()
return self._dummy_file
@property
def _lowercase ( self: str ):
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" ,self.config.name ,self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" ,self.version_name )
@property
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
return os.path.join(self.dummy_data_folder ,"dummy_data.zip" )
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Dict = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : Optional[int] = cached_path(
__lowerCAmelCase ,cache_dir=self.cache_dir ,extract_compressed_file=__lowerCAmelCase ,force_extract=__lowerCAmelCase )
return os.path.join(__lowerCAmelCase ,self.dummy_file_name )
@property
def _lowercase ( self: Tuple ):
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir ,self.dataset_name ,self.dummy_zip_file )
@property
def _lowercase ( self: List[str] ):
'''simple docstring'''
if self._bucket_url is None:
_lowerCamelCase : List[str] = hf_github_url(self.dataset_name ,self.dummy_zip_file.replace(os.sep ,"/" ) )
return self._bucket_url
@property
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep ,"/" ).split("/" )[:-1] )
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: str ,*__lowerCAmelCase: List[Any] ):
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Tuple = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Optional[Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
return self.create_dummy_data_dict(__lowerCAmelCase ,__lowerCAmelCase )
elif isinstance(__lowerCAmelCase ,(list, tuple) ):
return self.create_dummy_data_list(__lowerCAmelCase ,__lowerCAmelCase )
else:
return self.create_dummy_data_single(__lowerCAmelCase ,__lowerCAmelCase )
def _lowercase ( self: str ,__lowerCAmelCase: Optional[int] ,*__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
return self.download_and_extract(__lowerCAmelCase )
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ):
'''simple docstring'''
return self.download_and_extract(__lowerCAmelCase )
def _lowercase ( self: Optional[int] ,__lowerCAmelCase: Optional[int] ,*__lowerCAmelCase: List[str] ,**__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
return path
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
return {}
def _lowercase ( self: Optional[Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: str ):
'''simple docstring'''
_lowerCamelCase : str = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
for single_url in single_urls:
download_callback(__lowerCAmelCase )
else:
_lowerCamelCase : Union[str, Any] = single_urls
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Dict = [os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(Path(__lowerCAmelCase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : Union[str, Any] = single_urls
_lowerCamelCase : List[str] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(Path(__lowerCAmelCase ).name ) )
_lowerCamelCase : List[Any] = value
# make sure that values are unique
if all(isinstance(__lowerCAmelCase ,__lowerCAmelCase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def _lowercase ( self: int ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: Tuple ):
'''simple docstring'''
_lowerCamelCase : Dict = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : List[str] = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" ,__lowerCAmelCase ) ) for url in data_url )
_lowerCamelCase : Optional[Any] = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : Tuple = [data_url[0]] * len(__lowerCAmelCase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : List[Any] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(__lowerCAmelCase )
return dummy_data_list
def _lowercase ( self: Union[str, Any] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: List[Any] ):
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(__lowerCAmelCase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Optional[int] = os.path.join(__lowerCAmelCase ,urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(__lowerCAmelCase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
pass
def _lowercase ( self: Optional[int] ):
'''simple docstring'''
pass
def _lowercase ( self: List[Any] ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
def _iter_archive_members(__lowerCAmelCase: Any ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : Tuple = Path(self.dummy_file ).parent
_lowerCamelCase : str = path.relative_to(__lowerCAmelCase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : Optional[int] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(__lowerCAmelCase )
_lowerCamelCase : Optional[Any] = Path(__lowerCAmelCase )
_lowerCamelCase : int = _iter_archive_members(__lowerCAmelCase ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(__lowerCAmelCase ).as_posix(), file_path.open("rb" )
def _lowercase ( self: str ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : List[Any] = [paths]
for path in paths:
if os.path.isfile(__lowerCAmelCase ):
if os.path.basename(__lowerCAmelCase ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(__lowerCAmelCase ):
if os.path.basename(__lowerCAmelCase ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(__lowerCAmelCase ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(__lowerCAmelCase ,__lowerCAmelCase ) | 340 | 0 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase ( _lowerCAmelCase : Union[str, Any] ) -> List[str]:
for param in module.parameters():
_UpperCAmelCase : Tuple = False
def UpperCamelCase ( ) -> Any:
_UpperCAmelCase : Dict = """cuda""" if torch.cuda.is_available() else """cpu"""
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase : str = """mps"""
if device == "mps":
print(
"""WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
""" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
""" with generations.""" )
return device
def UpperCamelCase ( _lowerCAmelCase : Union[str, Any] ) -> int:
_UpperCAmelCase : Union[str, Any] = plt.imshow(_lowerCAmelCase )
fig.axes.get_xaxis().set_visible(_lowerCAmelCase )
fig.axes.get_yaxis().set_visible(_lowerCAmelCase )
plt.show()
def UpperCamelCase ( ) -> int:
_UpperCAmelCase : List[str] = datetime.now()
_UpperCAmelCase : Optional[Any] = current_time.strftime("""%H:%M:%S""" )
return timestamp
| 246 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
lowerCamelCase__ : List[str] = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''
lowerCamelCase__ : str = '''
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results[\'matthews_correlation\'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results[\'matthews_correlation\'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric("matthews_correlation")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results[\'matthews_correlation\'], 2))
-0.25
'''
lowerCamelCase__ : Union[str, Any] = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _UpperCAmelCase ( datasets.Metric):
def __snake_case ( self ) -> Optional[int]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def __snake_case ( self , _A , _A , _A=None ) -> str:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(_A , _A , sample_weight=_A ) ),
}
| 246 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : Union[str, Any] = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a__ : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 243 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = None
# Automatically constructed
__SCREAMING_SNAKE_CASE = "dict"
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = field(default='''Translation''' , init=UpperCAmelCase_ , repr=UpperCAmelCase_)
def __call__( self ) -> Optional[Any]:
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def __lowerCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class UpperCAmelCase__ :
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = None
# Automatically constructed
__SCREAMING_SNAKE_CASE = "dict"
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = field(default='''TranslationVariableLanguages''' , init=UpperCAmelCase_ , repr=UpperCAmelCase_)
def __lowerCamelCase ( self ) -> Dict:
__UpperCamelCase = sorted(set(self.languages ) ) if self.languages else None
__UpperCamelCase = len(self.languages ) if self.languages else None
def __call__( self ) -> Any:
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def __lowerCamelCase ( self , lowercase ) -> Any:
__UpperCamelCase = set(self.languages )
if self.languages and set(lowercase ) - lang_set:
raise ValueError(
f"Some languages in example ({', '.join(sorted(set(lowercase ) - lang_set ) )}) are not in valid set ({', '.join(lowercase )})." )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__UpperCamelCase = []
for lang, text in translation_dict.items():
if isinstance(lowercase , lowercase ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__UpperCamelCase , __UpperCamelCase = zip(*sorted(lowercase ) )
return {"language": languages, "translation": translations}
def __lowerCamelCase ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 243 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
lowercase = '''1'''
lowercase = '''0'''
lowercase = '''1'''
lowercase = ort.SessionOptions()
lowercase = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowercase = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
lowercase = ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowercase = ort.RunOptions()
lowercase = 128
lowercase = 1
lowercase = np.ones((batch, sequence), dtype=np.intaa)
lowercase = np.ones((batch, sequence), dtype=np.intaa)
lowercase = np.ones((batch, sequence), dtype=np.intaa)
print("Warm up phase...")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Start inference...")
lowercase = time.time()
lowercase = 2000
lowercase = {}
for iter in range(max_iters):
lowercase = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))
| 178 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
A : str = logging.get_logger(__name__)
A : Union[str, Any] = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = '''layoutlmv3'''
def __init__( self : Tuple , __lowerCAmelCase : Optional[int]=5_02_65 , __lowerCAmelCase : Tuple=7_68 , __lowerCAmelCase : Union[str, Any]=12 , __lowerCAmelCase : Any=12 , __lowerCAmelCase : List[Any]=30_72 , __lowerCAmelCase : Optional[int]="gelu" , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : List[Any]=0.1 , __lowerCAmelCase : Dict=5_12 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : Dict=0.0_2 , __lowerCAmelCase : List[str]=1e-5 , __lowerCAmelCase : List[str]=1 , __lowerCAmelCase : int=0 , __lowerCAmelCase : Dict=2 , __lowerCAmelCase : Tuple=10_24 , __lowerCAmelCase : List[str]=1_28 , __lowerCAmelCase : Optional[int]=1_28 , __lowerCAmelCase : Any=True , __lowerCAmelCase : Optional[Any]=32 , __lowerCAmelCase : Any=1_28 , __lowerCAmelCase : str=64 , __lowerCAmelCase : Optional[int]=2_56 , __lowerCAmelCase : int=True , __lowerCAmelCase : int=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : int=2_24 , __lowerCAmelCase : Dict=3 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : Dict=None , **__lowerCAmelCase : Optional[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(
vocab_size=__lowerCAmelCase , hidden_size=__lowerCAmelCase , num_hidden_layers=__lowerCAmelCase , num_attention_heads=__lowerCAmelCase , intermediate_size=__lowerCAmelCase , hidden_act=__lowerCAmelCase , hidden_dropout_prob=__lowerCAmelCase , attention_probs_dropout_prob=__lowerCAmelCase , max_position_embeddings=__lowerCAmelCase , type_vocab_size=__lowerCAmelCase , initializer_range=__lowerCAmelCase , layer_norm_eps=__lowerCAmelCase , pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase , )
A__ = max_ad_position_embeddings
A__ = coordinate_size
A__ = shape_size
A__ = has_relative_attention_bias
A__ = rel_pos_bins
A__ = max_rel_pos
A__ = has_spatial_attention_bias
A__ = rel_ad_pos_bins
A__ = max_rel_ad_pos
A__ = text_embed
A__ = visual_embed
A__ = input_size
A__ = num_channels
A__ = patch_size
A__ = classifier_dropout
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : List[str] = version.parse('''1.12''' )
@property
def a_ ( self : int ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
else:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""bbox""", {0: """batch""", 1: """sequence"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
] )
@property
def a_ ( self : Optional[int] ) -> float:
"""simple docstring"""
return 1e-5
@property
def a_ ( self : Tuple ) -> int:
"""simple docstring"""
return 12
def a_ ( self : str , __lowerCAmelCase : "ProcessorMixin" , __lowerCAmelCase : int = -1 , __lowerCAmelCase : int = -1 , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional["TensorType"] = None , __lowerCAmelCase : int = 3 , __lowerCAmelCase : int = 40 , __lowerCAmelCase : int = 40 , ) -> Mapping[str, Any]:
"""simple docstring"""
setattr(processor.image_processor , """apply_ocr""" , __lowerCAmelCase )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
A__ = compute_effective_axis_dimension(
__lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
A__ = processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase )
A__ = compute_effective_axis_dimension(
__lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__lowerCAmelCase )
# Generate dummy inputs according to compute batch and sequence
A__ = [[""" """.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
A__ = [[[48, 84, 73, 1_28]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
A__ = self._generate_dummy_images(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
A__ = dict(
processor(
__lowerCAmelCase , text=__lowerCAmelCase , boxes=__lowerCAmelCase , return_tensors=__lowerCAmelCase , ) )
return inputs
| 274 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
A = jnp.ones((batch_size, length) ) / length
return scores
def UpperCamelCase__ ( self ) -> Optional[int]:
A = None
A = 2_0
A = self._get_uniform_logits(batch_size=2 ,length=lowerCamelCase_ )
# tweak scores to not be uniform anymore
A = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
A = scores.at[1, 1_0].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
A = jax.nn.softmax(lowerCamelCase_ ,axis=-1 )
A = FlaxTemperatureLogitsWarper(temperature=0.5 )
A = FlaxTemperatureLogitsWarper(temperature=1.3 )
A = jax.nn.softmax(temp_dist_warper_sharper(lowerCamelCase_ ,scores.copy() ,cur_len=lowerCamelCase_ ) ,axis=-1 )
A = jax.nn.softmax(temp_dist_warper_smoother(lowerCamelCase_ ,scores.copy() ,cur_len=lowerCamelCase_ ) ,axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1E-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1E-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min() )
def UpperCamelCase__ ( self ) -> Any:
A = None
A = 1_0
A = 2
# create ramp distribution
A = np.broadcast_to(np.arange(lowerCamelCase_ )[None, :] ,(batch_size, vocab_size) ).copy()
A = ramp_logits[1:, : vocab_size // 2] + vocab_size
A = FlaxTopKLogitsWarper(3 )
A = top_k_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() ,7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() ,2 * [True] + 3 * [False] + 5 * [True] )
# check special case
A = 5
A = FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3 )
A = np.broadcast_to(np.arange(lowerCamelCase_ )[None, :] ,(batch_size, length) ).copy()
A = top_k_warp_safety_check(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() ,[2, 2] )
def UpperCamelCase__ ( self ) -> Tuple:
A = None
A = 1_0
A = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
A = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
A = FlaxTopPLogitsWarper(0.8 )
A = np.exp(top_p_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
A = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3 ) )
# check edge cases with negative and extreme logits
A = np.broadcast_to(np.arange(lowerCamelCase_ )[None, :] ,(batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
A = ramp_logits[1] * 1_0_0.0
# make sure at least 2 tokens are kept
A = FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0 )
A = top_p_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() ,[3, 2] )
def UpperCamelCase__ ( self ) -> Dict:
A = 2_0
A = 4
A = 0
A = FlaxMinLengthLogitsProcessor(min_length=1_0 ,eos_token_id=lowerCamelCase_ )
# check that min length is applied at length 5
A = ids_tensor((batch_size, 2_0) ,vocab_size=2_0 )
A = 5
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = min_dist_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("""inf""" )] )
# check that min length is not applied anymore at length 15
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = 1_5
A = min_dist_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertFalse(jnp.isinf(lowerCamelCase_ ).any() )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
A = 2_0
A = 4
A = 0
A = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_ )
# check that all scores are -inf except the bos_token_id score
A = ids_tensor((batch_size, 1) ,vocab_size=2_0 )
A = 1
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = logits_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
A = 3
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = logits_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertFalse(jnp.isinf(lowerCamelCase_ ).any() )
def UpperCamelCase__ ( self ) -> Dict:
A = 2_0
A = 4
A = 0
A = 5
A = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ )
# check that all scores are -inf except the eos_token_id when max_length is reached
A = ids_tensor((batch_size, 4) ,vocab_size=2_0 )
A = 4
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = logits_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
A = 3
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = logits_processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
self.assertFalse(jnp.isinf(lowerCamelCase_ ).any() )
def UpperCamelCase__ ( self ) -> List[Any]:
A = 4
A = 1_0
A = 1_5
A = 2
A = 1
A = 1_5
# dummy input_ids and scores
A = ids_tensor((batch_size, sequence_length) ,lowerCamelCase_ )
A = input_ids.copy()
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = scores.copy()
# instantiate all dist processors
A = FlaxTemperatureLogitsWarper(temperature=0.5 )
A = FlaxTopKLogitsWarper(3 )
A = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
A = FlaxMinLengthLogitsProcessor(min_length=1_0 ,eos_token_id=lowerCamelCase_ )
A = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_ )
A = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ )
A = 1_0
# no processor list
A = temp_dist_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = top_k_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = top_p_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = min_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = bos_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = eos_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
# with processor list
A = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
A = processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
def UpperCamelCase__ ( self ) -> Tuple:
A = 4
A = 1_0
A = 1_5
A = 2
A = 1
A = 1_5
# dummy input_ids and scores
A = ids_tensor((batch_size, sequence_length) ,lowerCamelCase_ )
A = input_ids.copy()
A = self._get_uniform_logits(lowerCamelCase_ ,lowerCamelCase_ )
A = scores.copy()
# instantiate all dist processors
A = FlaxTemperatureLogitsWarper(temperature=0.5 )
A = FlaxTopKLogitsWarper(3 )
A = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
A = FlaxMinLengthLogitsProcessor(min_length=1_0 ,eos_token_id=lowerCamelCase_ )
A = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase_ )
A = FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ )
A = 1_0
# no processor list
def run_no_processor_list(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ):
A = temp_dist_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = top_k_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = top_p_warp(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = min_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = bos_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
A = eos_dist_proc(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
return scores
# with processor list
def run_processor_list(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ):
A = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
A = processor(lowerCamelCase_ ,lowerCamelCase_ ,cur_len=lowerCamelCase_ )
return scores
A = jax.jit(lowerCamelCase_ )
A = jax.jit(lowerCamelCase_ )
A = jitted_run_no_processor_list(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
A = jitted_run_processor_list(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ )
# scores should be equal
self.assertTrue(jnp.allclose(lowerCamelCase_ ,lowerCamelCase_ ,atol=1E-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist() )
| 366 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = '''data2vec-vision'''
def __init__( self ,lowerCamelCase_=7_6_8 ,lowerCamelCase_=1_2 ,lowerCamelCase_=1_2 ,lowerCamelCase_=3_0_7_2 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.02 ,lowerCamelCase_=1E-12 ,lowerCamelCase_=2_2_4 ,lowerCamelCase_=1_6 ,lowerCamelCase_=3 ,lowerCamelCase_=False ,lowerCamelCase_=False ,lowerCamelCase_=False ,lowerCamelCase_=False ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=True ,lowerCamelCase_=[3, 5, 7, 1_1] ,lowerCamelCase_=[1, 2, 3, 6] ,lowerCamelCase_=True ,lowerCamelCase_=0.4 ,lowerCamelCase_=2_5_6 ,lowerCamelCase_=1 ,lowerCamelCase_=False ,lowerCamelCase_=2_5_5 ,**lowerCamelCase_ ,) -> Optional[Any]:
super().__init__(**lowerCamelCase_ )
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 = initializer_range
A = layer_norm_eps
A = image_size
A = patch_size
A = num_channels
A = use_mask_token
A = use_absolute_position_embeddings
A = use_relative_position_bias
A = use_shared_relative_position_bias
A = layer_scale_init_value
A = drop_path_rate
A = use_mean_pooling
# decode head attributes (semantic segmentation)
A = out_indices
A = pool_scales
# auxiliary head attributes (semantic segmentation)
A = use_auxiliary_head
A = auxiliary_loss_weight
A = auxiliary_channels
A = auxiliary_num_convs
A = auxiliary_concat_input
A = semantic_loss_ignore_index
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = version.parse('''1.11''' )
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCamelCase__ ( self ) -> float:
return 1E-4
| 77 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple ="naver-clova-ix/donut-base-finetuned-docvqa"
SCREAMING_SNAKE_CASE_ : Dict =(
"This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
"should be the document containing the information, as well as a `question` that is the question about the "
"document. It returns a text that contains the answer to the question."
)
SCREAMING_SNAKE_CASE_ : List[str] ="document_qa"
SCREAMING_SNAKE_CASE_ : Union[str, Any] =AutoProcessor
SCREAMING_SNAKE_CASE_ : Union[str, Any] =VisionEncoderDecoderModel
SCREAMING_SNAKE_CASE_ : List[Any] =["image", "text"]
SCREAMING_SNAKE_CASE_ : Any =["text"]
def __init__( self : Optional[int] , *__A : List[str] , **__A : List[Any] ):
if not is_vision_available():
raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.' )
super().__init__(*__A , **__A )
def _lowerCamelCase ( self : Any , __A : "Image" , __A : str ):
__UpperCamelCase = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
__UpperCamelCase = task_prompt.replace('{user_input}' , __A )
__UpperCamelCase = self.pre_processor.tokenizer(
__A , add_special_tokens=__A , return_tensors='pt' ).input_ids
__UpperCamelCase = self.pre_processor(__A , return_tensors='pt' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def _lowerCamelCase ( self : Union[str, Any] , __A : Optional[Any] ):
return self.model.generate(
inputs['pixel_values'].to(self.device ) , decoder_input_ids=inputs['decoder_input_ids'].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__A , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__A , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__A , ).sequences
def _lowerCamelCase ( self : Tuple , __A : List[Any] ):
__UpperCamelCase = self.pre_processor.batch_decode(__A )[0]
__UpperCamelCase = sequence.replace(self.pre_processor.tokenizer.eos_token , '' )
__UpperCamelCase = sequence.replace(self.pre_processor.tokenizer.pad_token , '' )
__UpperCamelCase = re.sub(R'<.*?>' , '' , __A , count=1 ).strip() # remove first task start token
__UpperCamelCase = self.pre_processor.tokenajson(__A )
return sequence["answer"]
| 53 |
'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import platform
import sys
a__ : Tuple ='''3'''
print('''Python version:''', sys.version)
print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
print('''Torch version:''', None)
try:
import transformers
print('''transformers version:''', transformers.__version__)
except ImportError:
print('''transformers version:''', None)
| 53 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> List[Any]:
"""simple docstring"""
snake_case_ = torch.exp(SCREAMING_SNAKE_CASE )
snake_case_ = torch.sum(SCREAMING_SNAKE_CASE , dim=1 ) # sum of exp(x_i)
snake_case_ = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(SCREAMING_SNAKE_CASE ) - B / A
class lowerCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
super().__init__()
snake_case_ = config.output_attentions
snake_case_ = config.output_hidden_states
snake_case_ = nn.ModuleList([BertLayer(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
snake_case_ = nn.ModuleList([BertHighway(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
snake_case_ = [-1 for _ in range(config.num_hidden_layers )]
def UpperCamelCase__ ( self , _UpperCAmelCase ):
if (type(_UpperCAmelCase ) is float) or (type(_UpperCAmelCase ) is int):
for i in range(len(self.early_exit_entropy ) ):
snake_case_ = x
else:
snake_case_ = x
def UpperCamelCase__ ( self , _UpperCAmelCase ):
snake_case_ = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def UpperCamelCase__ ( self , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
snake_case_ = ()
snake_case_ = ()
snake_case_ = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
snake_case_ = all_hidden_states + (hidden_states,)
snake_case_ = layer_module(
_UpperCAmelCase , _UpperCAmelCase , head_mask[i] , _UpperCAmelCase , _UpperCAmelCase )
snake_case_ = layer_outputs[0]
if self.output_attentions:
snake_case_ = all_attentions + (layer_outputs[1],)
snake_case_ = (hidden_states,)
if self.output_hidden_states:
snake_case_ = current_outputs + (all_hidden_states,)
if self.output_attentions:
snake_case_ = current_outputs + (all_attentions,)
snake_case_ = self.highway[i](_UpperCAmelCase )
# logits, pooled_output
if not self.training:
snake_case_ = highway_exit[0]
snake_case_ = entropy(_UpperCAmelCase )
snake_case_ = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
snake_case_ = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
snake_case_ = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(_UpperCAmelCase , i + 1 )
else:
snake_case_ = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
snake_case_ = all_hidden_states + (hidden_states,)
snake_case_ = (hidden_states,)
if self.output_hidden_states:
snake_case_ = outputs + (all_hidden_states,)
if self.output_attentions:
snake_case_ = outputs + (all_attentions,)
snake_case_ = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"The Bert Model transformer with early exiting (DeeBERT). " , lowerCamelCase__ , )
class lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase )
snake_case_ = config
snake_case_ = BertEmbeddings(_UpperCAmelCase )
snake_case_ = DeeBertEncoder(_UpperCAmelCase )
snake_case_ = BertPooler(_UpperCAmelCase )
self.init_weights()
def UpperCamelCase__ ( self ):
self.encoder.init_highway_pooler(self.pooler )
def UpperCamelCase__ ( self ):
return self.embeddings.word_embeddings
def UpperCamelCase__ ( self , _UpperCAmelCase ):
snake_case_ = value
def UpperCamelCase__ ( self , _UpperCAmelCase ):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(_UpperCAmelCase )
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
def UpperCamelCase__ ( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time''' )
elif input_ids is not None:
snake_case_ = input_ids.size()
elif inputs_embeds is not None:
snake_case_ = inputs_embeds.size()[:-1]
else:
raise ValueError('''You have to specify either input_ids or inputs_embeds''' )
snake_case_ = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
snake_case_ = torch.ones(_UpperCAmelCase , device=_UpperCAmelCase )
if encoder_attention_mask is None:
snake_case_ = torch.ones(_UpperCAmelCase , device=_UpperCAmelCase )
if token_type_ids is None:
snake_case_ = torch.zeros(_UpperCAmelCase , dtype=torch.long , device=_UpperCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
snake_case_ = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
snake_case_ = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
snake_case_ = encoder_attention_mask[:, None, None, :]
snake_case_ = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
snake_case_ = (1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
snake_case_ = self.get_head_mask(_UpperCAmelCase , self.config.num_hidden_layers )
snake_case_ = self.embeddings(
input_ids=_UpperCAmelCase , position_ids=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , inputs_embeds=_UpperCAmelCase )
snake_case_ = self.encoder(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
snake_case_ = encoder_outputs[0]
snake_case_ = self.pooler(_UpperCAmelCase )
snake_case_ = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase ):
snake_case_ = message
snake_case_ = exit_layer # start from 1!
class lowerCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
super().__init__()
snake_case_ = BertPooler(_UpperCAmelCase )
snake_case_ = nn.Dropout(config.hidden_dropout_prob )
snake_case_ = nn.Linear(config.hidden_size , config.num_labels )
def UpperCamelCase__ ( self , _UpperCAmelCase ):
# Pooler
snake_case_ = encoder_outputs[0]
snake_case_ = self.pooler(_UpperCAmelCase )
# "return" pooler_output
# BertModel
snake_case_ = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
snake_case_ = bmodel_output[1]
snake_case_ = self.dropout(_UpperCAmelCase )
snake_case_ = self.classifier(_UpperCAmelCase )
return logits, pooled_output
@add_start_docstrings(
"Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. " , lowerCamelCase__ , )
class lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase )
snake_case_ = config.num_labels
snake_case_ = config.num_hidden_layers
snake_case_ = DeeBertModel(_UpperCAmelCase )
snake_case_ = nn.Dropout(config.hidden_dropout_prob )
snake_case_ = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
def UpperCamelCase__ ( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=-1 , _UpperCAmelCase=False , ):
snake_case_ = self.num_layers
try:
snake_case_ = self.bert(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , position_ids=_UpperCAmelCase , head_mask=_UpperCAmelCase , inputs_embeds=_UpperCAmelCase , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
snake_case_ = outputs[1]
snake_case_ = self.dropout(_UpperCAmelCase )
snake_case_ = self.classifier(_UpperCAmelCase )
snake_case_ = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
snake_case_ = e.message
snake_case_ = e.exit_layer
snake_case_ = outputs[0]
if not self.training:
snake_case_ = entropy(_UpperCAmelCase )
snake_case_ = []
snake_case_ = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
snake_case_ = MSELoss()
snake_case_ = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case_ = CrossEntropyLoss()
snake_case_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
snake_case_ = []
for highway_exit in outputs[-1]:
snake_case_ = highway_exit[0]
if not self.training:
highway_logits_all.append(_UpperCAmelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
snake_case_ = MSELoss()
snake_case_ = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
snake_case_ = CrossEntropyLoss()
snake_case_ = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_UpperCAmelCase )
if train_highway:
snake_case_ = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
snake_case_ = (loss,) + outputs
if not self.training:
snake_case_ = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
snake_case_ = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits) | 267 |
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 = False
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase__ ( self ):
return 12
@property
def UpperCamelCase__ ( self ):
return 12
@property
def UpperCamelCase__ ( self ):
return 32
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = VQModel(
block_out_channels=[32, 64] , 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 UpperCamelCase__ ( self ):
snake_case_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(_UpperCAmelCase )
@property
def UpperCamelCase__ ( self ):
torch.manual_seed(0 )
snake_case_ = 12
snake_case_ = 12
snake_case_ = {
'''attention_bias''': True,
'''cross_attention_dim''': 32,
'''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''': 32,
'''sample_size''': width,
'''activation_fn''': '''geglu-approximate''',
}
snake_case_ = TransformeraDModel(**_UpperCAmelCase )
return model
def UpperCamelCase__ ( self ):
snake_case_ = '''cpu'''
snake_case_ = self.dummy_vqvae
snake_case_ = self.dummy_text_encoder
snake_case_ = self.dummy_tokenizer
snake_case_ = self.dummy_transformer
snake_case_ = VQDiffusionScheduler(self.num_embed )
snake_case_ = LearnedClassifierFreeSamplingEmbeddings(learnable=_UpperCAmelCase )
snake_case_ = VQDiffusionPipeline(
vqvae=_UpperCAmelCase , text_encoder=_UpperCAmelCase , tokenizer=_UpperCAmelCase , transformer=_UpperCAmelCase , scheduler=_UpperCAmelCase , learned_classifier_free_sampling_embeddings=_UpperCAmelCase , )
snake_case_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = '''teddy bear playing in the pool'''
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
snake_case_ = pipe([prompt] , generator=_UpperCAmelCase , num_inference_steps=2 , output_type='''np''' )
snake_case_ = output.images
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
snake_case_ = pipe(
[prompt] , generator=_UpperCAmelCase , output_type='''np''' , return_dict=_UpperCAmelCase , num_inference_steps=2 )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
snake_case_ = np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992] )
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 UpperCamelCase__ ( self ):
snake_case_ = '''cpu'''
snake_case_ = self.dummy_vqvae
snake_case_ = self.dummy_text_encoder
snake_case_ = self.dummy_tokenizer
snake_case_ = self.dummy_transformer
snake_case_ = VQDiffusionScheduler(self.num_embed )
snake_case_ = LearnedClassifierFreeSamplingEmbeddings(
learnable=_UpperCAmelCase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
snake_case_ = VQDiffusionPipeline(
vqvae=_UpperCAmelCase , text_encoder=_UpperCAmelCase , tokenizer=_UpperCAmelCase , transformer=_UpperCAmelCase , scheduler=_UpperCAmelCase , learned_classifier_free_sampling_embeddings=_UpperCAmelCase , )
snake_case_ = pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
snake_case_ = '''teddy bear playing in the pool'''
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
snake_case_ = pipe([prompt] , generator=_UpperCAmelCase , num_inference_steps=2 , output_type='''np''' )
snake_case_ = output.images
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
snake_case_ = pipe(
[prompt] , generator=_UpperCAmelCase , output_type='''np''' , return_dict=_UpperCAmelCase , num_inference_steps=2 )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
snake_case_ = np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988] )
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 ):
'''simple docstring'''
def UpperCamelCase__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ):
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )
snake_case_ = VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
snake_case_ = pipeline.to(_UpperCAmelCase )
pipeline.set_progress_bar_config(disable=_UpperCAmelCase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
snake_case_ = torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
snake_case_ = pipeline(
'''teddy bear playing in the pool''' , num_images_per_prompt=1 , generator=_UpperCAmelCase , output_type='''np''' , )
snake_case_ = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert np.abs(expected_image - image ).max() < 2.0 | 267 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def a ( A__ : int ) -> str:
"""simple docstring"""
random.seed(A__ )
np.random.seed(A__ )
torch.manual_seed(A__ )
torch.cuda.manual_seed_all(A__ )
# ^^ safe to call this function even if cuda is not available
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase , lowerCAmelCase = 0.9999 , lowerCAmelCase = 0.0 , lowerCAmelCase = 0 , lowerCAmelCase = False , lowerCAmelCase = 1.0 , lowerCAmelCase = 2 / 3 , lowerCAmelCase = None , lowerCAmelCase = None , **lowerCAmelCase , ) -> str:
'''simple docstring'''
if isinstance(lowerCAmelCase , torch.nn.Module ):
_lowercase =(
'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
'Please pass the parameters of the module instead.'
)
deprecate(
'passing a `torch.nn.Module` to `ExponentialMovingAverage`' , '1.0.0' , lowerCAmelCase , standard_warn=lowerCAmelCase , )
_lowercase =parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
_lowercase =True
if kwargs.get('max_value' , lowerCAmelCase ) is not None:
_lowercase ='The `max_value` argument is deprecated. Please use `decay` instead.'
deprecate('max_value' , '1.0.0' , lowerCAmelCase , standard_warn=lowerCAmelCase )
_lowercase =kwargs['max_value']
if kwargs.get('min_value' , lowerCAmelCase ) is not None:
_lowercase ='The `min_value` argument is deprecated. Please use `min_decay` instead.'
deprecate('min_value' , '1.0.0' , lowerCAmelCase , standard_warn=lowerCAmelCase )
_lowercase =kwargs['min_value']
_lowercase =list(lowerCAmelCase )
_lowercase =[p.clone().detach() for p in parameters]
if kwargs.get('device' , lowerCAmelCase ) is not None:
_lowercase ='The `device` argument is deprecated. Please use `to` instead.'
deprecate('device' , '1.0.0' , lowerCAmelCase , standard_warn=lowerCAmelCase )
self.to(device=kwargs['device'] )
_lowercase =None
_lowercase =decay
_lowercase =min_decay
_lowercase =update_after_step
_lowercase =use_ema_warmup
_lowercase =inv_gamma
_lowercase =power
_lowercase =0
_lowercase =None # set in `step()`
_lowercase =model_cls
_lowercase =model_config
@classmethod
def A__ ( cls , lowerCAmelCase , lowerCAmelCase ) -> "EMAModel":
'''simple docstring'''
_lowercase , _lowercase =model_cls.load_config(lowerCAmelCase , return_unused_kwargs=lowerCAmelCase )
_lowercase =model_cls.from_pretrained(lowerCAmelCase )
_lowercase =cls(model.parameters() , model_cls=lowerCAmelCase , model_config=model.config )
ema_model.load_state_dict(lowerCAmelCase )
return ema_model
def A__ ( self , lowerCAmelCase ) -> int:
'''simple docstring'''
if self.model_cls is None:
raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )
if self.model_config is None:
raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )
_lowercase =self.model_cls.from_config(self.model_config )
_lowercase =self.state_dict()
state_dict.pop('shadow_params' , lowerCAmelCase )
model.register_to_config(**lowerCAmelCase )
self.copy_to(model.parameters() )
model.save_pretrained(lowerCAmelCase )
def A__ ( self , lowerCAmelCase ) -> float:
'''simple docstring'''
_lowercase =max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
_lowercase =1 - (1 + step / self.inv_gamma) ** -self.power
else:
_lowercase =(1 + step) / (10 + step)
_lowercase =min(lowerCAmelCase , self.decay )
# make sure decay is not smaller than min_decay
_lowercase =max(lowerCAmelCase , self.min_decay )
return cur_decay_value
@torch.no_grad()
def A__ ( self , lowerCAmelCase ) -> Tuple:
'''simple docstring'''
if isinstance(lowerCAmelCase , torch.nn.Module ):
_lowercase =(
'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
'Please pass the parameters of the module instead.'
)
deprecate(
'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`' , '1.0.0' , lowerCAmelCase , standard_warn=lowerCAmelCase , )
_lowercase =parameters.parameters()
_lowercase =list(lowerCAmelCase )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
_lowercase =self.get_decay(self.optimization_step )
_lowercase =decay
_lowercase =1 - decay
_lowercase =contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , lowerCAmelCase ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
_lowercase =deepspeed.zero.GatheredParameters(lowerCAmelCase , modifier_rank=lowerCAmelCase )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(lowerCAmelCase )
def A__ ( self , lowerCAmelCase ) -> None:
'''simple docstring'''
_lowercase =list(lowerCAmelCase )
for s_param, param in zip(self.shadow_params , lowerCAmelCase ):
param.data.copy_(s_param.to(param.device ).data )
def A__ ( self , lowerCAmelCase=None , lowerCAmelCase=None ) -> None:
'''simple docstring'''
_lowercase =[
p.to(device=lowerCAmelCase , dtype=lowerCAmelCase ) if p.is_floating_point() else p.to(device=lowerCAmelCase )
for p in self.shadow_params
]
def A__ ( self ) -> dict:
'''simple docstring'''
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def A__ ( self , lowerCAmelCase ) -> None:
'''simple docstring'''
_lowercase =[param.detach().cpu().clone() for param in parameters]
def A__ ( self , lowerCAmelCase ) -> None:
'''simple docstring'''
if self.temp_stored_params is None:
raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
for c_param, param in zip(self.temp_stored_params , lowerCAmelCase ):
param.data.copy_(c_param.data )
# Better memory-wise.
_lowercase =None
def A__ ( self , lowerCAmelCase ) -> None:
'''simple docstring'''
_lowercase =copy.deepcopy(lowerCAmelCase )
_lowercase =state_dict.get('decay' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('Decay must be between 0 and 1' )
_lowercase =state_dict.get('min_decay' , self.min_decay )
if not isinstance(self.min_decay , lowerCAmelCase ):
raise ValueError('Invalid min_decay' )
_lowercase =state_dict.get('optimization_step' , self.optimization_step )
if not isinstance(self.optimization_step , lowerCAmelCase ):
raise ValueError('Invalid optimization_step' )
_lowercase =state_dict.get('update_after_step' , self.update_after_step )
if not isinstance(self.update_after_step , lowerCAmelCase ):
raise ValueError('Invalid update_after_step' )
_lowercase =state_dict.get('use_ema_warmup' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , lowerCAmelCase ):
raise ValueError('Invalid use_ema_warmup' )
_lowercase =state_dict.get('inv_gamma' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('Invalid inv_gamma' )
_lowercase =state_dict.get('power' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('Invalid power' )
_lowercase =state_dict.get('shadow_params' , lowerCAmelCase )
if shadow_params is not None:
_lowercase =shadow_params
if not isinstance(self.shadow_params , lowerCAmelCase ):
raise ValueError('shadow_params must be a list' )
if not all(isinstance(lowerCAmelCase , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('shadow_params must all be Tensors' )
| 205 |
def a ( A__ : str , A__ : bool = False ) -> str:
"""simple docstring"""
if not isinstance(A__ , A__ ):
_lowercase =F'''Expected string as input, found {type(A__ )}'''
raise ValueError(A__ )
if not isinstance(A__ , A__ ):
_lowercase =F'''Expected boolean as use_pascal parameter, found {type(A__ )}'''
raise ValueError(A__ )
_lowercase =input_str.split('_' )
_lowercase =0 if use_pascal else 1
_lowercase =words[start_index:]
_lowercase =[word[0].upper() + word[1:] for word in words_to_capitalize]
_lowercase ='' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 205 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
__UpperCamelCase = logging.getLogger(__name__)
class _A ( lowerCamelCase__ ):
lowercase__: Optional[Any] = 'token-classification'
def __init__( self : Any , __magic_name__ : List[str] ) -> Tuple:
"""simple docstring"""
if type(lowercase__ ) == dict:
__snake_case : Dict = Namespace(**lowercase__ )
__snake_case : Optional[int] = import_module("""tasks""" )
try:
__snake_case : List[str] = getattr(lowercase__ , hparams.task_type )
__snake_case : List[str] = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' )
__snake_case : Optional[Any] = self.token_classification_task.get_labels(hparams.labels )
__snake_case : Tuple = CrossEntropyLoss().ignore_index
super().__init__(lowercase__ , len(self.labels ) , self.mode )
def lowercase__ ( self : Union[str, Any] , **__magic_name__ : int ) -> Union[str, Any]:
"""simple docstring"""
return self.model(**lowercase__ )
def lowercase__ ( self : str , __magic_name__ : int , __magic_name__ : Tuple ) -> str:
"""simple docstring"""
__snake_case : Dict = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
__snake_case : Tuple = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
__snake_case : Dict = self(**lowercase__ )
__snake_case : Optional[Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def lowercase__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__snake_case : Optional[Any] = self.hparams
for mode in ["train", "dev", "test"]:
__snake_case : Any = self._feature_file(lowercase__ )
if os.path.exists(lowercase__ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , lowercase__ )
__snake_case : List[Any] = torch.load(lowercase__ )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
__snake_case : int = self.token_classification_task.read_examples_from_file(args.data_dir , lowercase__ )
__snake_case : int = self.token_classification_task.convert_examples_to_features(
lowercase__ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=lowercase__ , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , lowercase__ )
torch.save(lowercase__ , lowercase__ )
def lowercase__ ( self : str , __magic_name__ : int , __magic_name__ : int , __magic_name__ : bool = False ) -> Any:
"""simple docstring"""
__snake_case : List[Any] = self._feature_file(lowercase__ )
logger.info("""Loading features from cached file %s""" , lowercase__ )
__snake_case : List[Any] = torch.load(lowercase__ )
__snake_case : Dict = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
__snake_case : Tuple = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
__snake_case : List[Any] = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
__snake_case : Optional[int] = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
__snake_case : Tuple = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(lowercase__ , lowercase__ , lowercase__ , lowercase__ ) , batch_size=lowercase__ )
def lowercase__ ( self : List[Any] , __magic_name__ : Tuple , __magic_name__ : Any ) -> List[str]:
"""simple docstring"""
"""Compute validation""" ""
__snake_case : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
__snake_case : Tuple = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
__snake_case : Tuple = self(**lowercase__ )
__snake_case , __snake_case : int = outputs[:2]
__snake_case : Optional[int] = logits.detach().cpu().numpy()
__snake_case : List[Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def lowercase__ ( self : List[str] , __magic_name__ : str ) -> Optional[int]:
"""simple docstring"""
__snake_case : Tuple = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
__snake_case : List[str] = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
__snake_case : Any = np.argmax(lowercase__ , axis=2 )
__snake_case : Optional[int] = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
__snake_case : List[Any] = dict(enumerate(self.labels ) )
__snake_case : Dict = [[] for _ in range(out_label_ids.shape[0] )]
__snake_case : Any = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
__snake_case : str = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(lowercase__ , lowercase__ ),
"""precision""": precision_score(lowercase__ , lowercase__ ),
"""recall""": recall_score(lowercase__ , lowercase__ ),
"""f1""": fa_score(lowercase__ , lowercase__ ),
}
__snake_case : str = dict(results.items() )
__snake_case : str = results
return ret, preds_list, out_label_list
def lowercase__ ( self : List[Any] , __magic_name__ : Optional[Any] ) -> str:
"""simple docstring"""
__snake_case , __snake_case , __snake_case : Tuple = self._eval_end(lowercase__ )
__snake_case : Tuple = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def lowercase__ ( self : Dict , __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
__snake_case , __snake_case , __snake_case : int = self._eval_end(lowercase__ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
__snake_case : List[str] = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def lowercase__ ( __magic_name__ : Optional[Any] , __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
BaseTransformer.add_model_specific_args(lowercase__ , lowercase__ )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=lowercase__ , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=1_28 , type=lowercase__ , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=lowercase__ , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=lowercase__ , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
__UpperCamelCase = NERTransformer.add_model_specific_args(parser, os.getcwd())
__UpperCamelCase = parser.parse_args()
__UpperCamelCase = NERTransformer(args)
__UpperCamelCase = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
__UpperCamelCase = sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
__UpperCamelCase = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 357 |
'''simple docstring'''
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 13 | 0 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class _UpperCAmelCase ( unittest.TestCase):
__a : Tuple = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def __snake_case ( self , _A , _A , _A ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : str = hf_hub_download(
repo_id="""nateraw/video-demo""" , filename="""archery.mp4""" , repo_type="""dataset""" )
_UpperCAmelCase : Tuple = VideoClassificationPipeline(model=_A , image_processor=_A , top_k=2 )
_UpperCAmelCase : List[str] = [
example_video_filepath,
"""https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
]
return video_classifier, examples
def __snake_case ( self , _A , _A ) -> Optional[int]:
'''simple docstring'''
for example in examples:
_UpperCAmelCase : str = video_classifier(_A )
self.assertEqual(
_A , [
{"""score""": ANY(_A ), """label""": ANY(_A )},
{"""score""": ANY(_A ), """label""": ANY(_A )},
] , )
@require_torch
def __snake_case ( self ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
_UpperCAmelCase : Optional[Any] = VideoMAEFeatureExtractor(
size={"""shortest_edge""": 10} , crop_size={"""height""": 10, """width""": 10} )
_UpperCAmelCase : List[str] = pipeline(
"""video-classification""" , model=_A , feature_extractor=_A , frame_sampling_rate=4 )
_UpperCAmelCase : Union[str, Any] = hf_hub_download(repo_id="""nateraw/video-demo""" , filename="""archery.mp4""" , repo_type="""dataset""" )
_UpperCAmelCase : Union[str, Any] = video_classifier(_A , top_k=2 )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}] , )
_UpperCAmelCase : int = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(_A , decimals=4 ) , [
[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
[{"""score""": 0.5199, """label""": """LABEL_0"""}, {"""score""": 0.4801, """label""": """LABEL_1"""}],
] , )
@require_tf
def __snake_case ( self ) -> Any:
'''simple docstring'''
pass
| 246 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
lowerCamelCase__ : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCamelCase__ : Union[str, Any] = '''
Examples:
```py
>>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> prompt = "A red cartoon frog, 4k"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> init_image = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/frog.png"
... )
>>> image = pipe(
... image=init_image,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... strength=0.2,
... ).images
>>> image[0].save("red_frog.png")
```
'''
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : List[Any], _lowerCAmelCase : Optional[Any]=8 ) -> Union[str, Any]:
_UpperCAmelCase : Dict = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_UpperCAmelCase : Tuple = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def UpperCamelCase ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Any=512, _lowerCAmelCase : Optional[Any]=512 ) -> Optional[Any]:
_UpperCAmelCase : Optional[int] = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1 )
_UpperCAmelCase : List[Any] = np.array(pil_image.convert("""RGB""" ) )
_UpperCAmelCase : str = arr.astype(np.floataa ) / 127.5 - 1
_UpperCAmelCase : Dict = np.transpose(_lowerCAmelCase, [2, 0, 1] )
_UpperCAmelCase : Union[str, Any] = torch.from_numpy(_lowerCAmelCase ).unsqueeze(0 )
return image
class _UpperCAmelCase ( __a):
def __init__( self , _A , _A , _A , ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_A , scheduler=_A , movq=_A , )
_UpperCAmelCase : Optional[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __snake_case ( self , _A , _A , _A ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : int = min(int(num_inference_steps * strength ) , _A )
_UpperCAmelCase : Dict = max(num_inference_steps - init_timestep , 0 )
_UpperCAmelCase : Tuple = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def __snake_case ( self , _A , _A , _A , _A , _A , _A , _A=None ) -> List[Any]:
'''simple docstring'''
if not isinstance(_A , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A )}''' )
_UpperCAmelCase : Any = image.to(device=_A , dtype=_A )
_UpperCAmelCase : Optional[int] = batch_size * num_images_per_prompt
if image.shape[1] == 4:
_UpperCAmelCase : Dict = image
else:
if isinstance(_A , _A ) and len(_A ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(_A )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
elif isinstance(_A , _A ):
_UpperCAmelCase : Any = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_A )
]
_UpperCAmelCase : List[Any] = torch.cat(_A , dim=0 )
else:
_UpperCAmelCase : str = self.movq.encode(_A ).latent_dist.sample(_A )
_UpperCAmelCase : Any = self.movq.config.scaling_factor * init_latents
_UpperCAmelCase : List[Any] = torch.cat([init_latents] , dim=0 )
_UpperCAmelCase : Union[str, Any] = init_latents.shape
_UpperCAmelCase : List[Any] = randn_tensor(_A , generator=_A , device=_A , dtype=_A )
# get latents
_UpperCAmelCase : Optional[int] = self.scheduler.add_noise(_A , _A , _A )
_UpperCAmelCase : Optional[int] = init_latents
return latents
def __snake_case ( self , _A=0 ) -> Optional[Any]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_UpperCAmelCase : List[Any] = torch.device(f'''cuda:{gpu_id}''' )
_UpperCAmelCase : Tuple = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_A , _A )
def __snake_case ( self , _A=0 ) -> int:
'''simple docstring'''
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.""" )
_UpperCAmelCase : 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)
_UpperCAmelCase : Any = None
for cpu_offloaded_model in [self.unet, self.movq]:
_UpperCAmelCase , _UpperCAmelCase : Tuple = cpu_offload_with_hook(_A , _A , prev_module_hook=_A )
# We'll offload the last model manually.
_UpperCAmelCase : Optional[int] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __snake_case ( self ) -> List[str]:
'''simple docstring'''
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 , _A , _A , _A , _A = 5_12 , _A = 5_12 , _A = 1_00 , _A = 4.0 , _A = 0.3 , _A = 1 , _A = None , _A = "pil" , _A = True , ) -> Any:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = self._execution_device
_UpperCAmelCase : List[str] = guidance_scale > 1.0
if isinstance(_A , _A ):
_UpperCAmelCase : Dict = torch.cat(_A , dim=0 )
_UpperCAmelCase : Any = image_embeds.shape[0]
if isinstance(_A , _A ):
_UpperCAmelCase : Any = torch.cat(_A , dim=0 )
if do_classifier_free_guidance:
_UpperCAmelCase : str = image_embeds.repeat_interleave(_A , dim=0 )
_UpperCAmelCase : Optional[int] = negative_image_embeds.repeat_interleave(_A , dim=0 )
_UpperCAmelCase : str = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_A )
if not isinstance(_A , _A ):
_UpperCAmelCase : str = [image]
if not all(isinstance(_A , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
f'''Input is in incorrect format: {[type(_A ) for i in image]}. Currently, we only support PIL image and pytorch tensor''' )
_UpperCAmelCase : Union[str, Any] = torch.cat([prepare_image(_A , _A , _A ) for i in image] , dim=0 )
_UpperCAmelCase : List[Any] = image.to(dtype=image_embeds.dtype , device=_A )
_UpperCAmelCase : int = self.movq.encode(_A )["""latents"""]
_UpperCAmelCase : Dict = latents.repeat_interleave(_A , dim=0 )
self.scheduler.set_timesteps(_A , device=_A )
_UpperCAmelCase , _UpperCAmelCase : Any = self.get_timesteps(_A , _A , _A )
_UpperCAmelCase : Dict = timesteps[:1].repeat(batch_size * num_images_per_prompt )
_UpperCAmelCase , _UpperCAmelCase : str = downscale_height_and_width(_A , _A , self.movq_scale_factor )
_UpperCAmelCase : List[Any] = self.prepare_latents(
_A , _A , _A , _A , image_embeds.dtype , _A , _A )
for i, t in enumerate(self.progress_bar(_A ) ):
# expand the latents if we are doing classifier free guidance
_UpperCAmelCase : Union[str, Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCAmelCase : Union[str, Any] = {"""image_embeds""": image_embeds}
_UpperCAmelCase : str = self.unet(
sample=_A , timestep=_A , encoder_hidden_states=_A , added_cond_kwargs=_A , return_dict=_A , )[0]
if do_classifier_free_guidance:
_UpperCAmelCase , _UpperCAmelCase : Any = noise_pred.split(latents.shape[1] , dim=1 )
_UpperCAmelCase , _UpperCAmelCase : Any = noise_pred.chunk(2 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = variance_pred.chunk(2 )
_UpperCAmelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_UpperCAmelCase : Optional[int] = 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"]
):
_UpperCAmelCase , _UpperCAmelCase : Dict = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase : List[Any] = self.scheduler.step(
_A , _A , _A , generator=_A , )[0]
# post-processing
_UpperCAmelCase : Optional[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"]:
_UpperCAmelCase : Any = image * 0.5 + 0.5
_UpperCAmelCase : Dict = image.clamp(0 , 1 )
_UpperCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_UpperCAmelCase : List[str] = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A )
| 246 | 1 |
"""simple docstring"""
import qiskit
def _A ( lowercase , lowercase ):
"""simple docstring"""
a =qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
a =qiskit.QuantumCircuit(lowercase , lowercase )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
a =qiskit.execute(lowercase , lowercase , shots=10_00 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(lowercase )
if __name__ == "__main__":
print(F'Total count for various states are: {single_qubit_measure(1, 1)}') | 352 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
lowerCamelCase_ : Any = random.Random()
def _A ( lowercase , lowercase=1.0 , lowercase=None , lowercase=None ):
"""simple docstring"""
if rng is None:
a =global_rng
a =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __A , __A=7 , __A=400 , __A=2000 , __A=10 , __A=160 , __A=8 , __A=0.0 , __A=4000 , __A=False , __A=True , ) -> Optional[Any]:
a =parent
a =batch_size
a =min_seq_length
a =max_seq_length
a =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a =padding_value
a =sampling_rate
a =return_attention_mask
a =do_normalize
a =feature_size
a =chunk_length
a =hop_length
def SCREAMING_SNAKE_CASE ( self ) -> str:
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def SCREAMING_SNAKE_CASE ( self , __A=False , __A=False ) -> str:
def _flatten(__A ):
return list(itertools.chain(*__A ) )
if equal_length:
a =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
a =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
a =[np.asarray(__A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __A ( _SCREAMING_SNAKE_CASE, unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = WhisperFeatureExtractor if is_speech_available() else None
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =WhisperFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =feat_extract_first.save_pretrained(__A )[0]
check_json_file_has_correct_format(__A )
a =self.feature_extraction_class.from_pretrained(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> str:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =os.path.join(__A , '''feat_extract.json''' )
feat_extract_first.to_json_file(__A )
a =self.feature_extraction_class.from_json_file(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
# Tests that all call wrap to encode_plus and batch_encode_plus
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
a =[floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
# Test feature size
a =feature_extractor(__A , padding='''max_length''' , return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
a =feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
a =feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test batched
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
a =[floats_list((1, x) )[0] for x in (800, 800, 800)]
a =np.asarray(__A )
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test truncation required
a =[floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
a =[x[: feature_extractor.n_samples] for x in speech_inputs]
a =[np.asarray(__A ) for speech_input in speech_inputs_truncated]
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
import torch
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =np.random.rand(100 , 32 ).astype(np.floataa )
a =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def SCREAMING_SNAKE_CASE ( self , __A ) -> Dict:
a =load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
a =ds.sort('''id''' ).select(range(__A ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE ( self ) -> Any:
# fmt: off
a =torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
a =self._load_datasamples(1 )
a =WhisperFeatureExtractor()
a =feature_extractor(__A , return_tensors='''pt''' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __A , atol=1E-4 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =self._load_datasamples(1 )[0]
a =((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535 # Rescale to [0, 65535] to show issue
a =feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__A )[0]
self.assertTrue(np.all(np.mean(__A ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(__A ) - 1 ) < 1E-3 ) ) | 215 | 0 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def _lowerCamelCase( a , a , a = None ):
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old versions of hfh don't url-encode the file path
__a = quote(a )
return hfh.hf_hub_url(a , a , repo_type="dataset" , revision=a )
| 261 | """simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 ):
__a , __a = row, column
__a = [[default_value for c in range(lowerCamelCase )] for r in range(lowerCamelCase )]
def __str__( self ):
__a = F"Matrix consist of {self.row} rows and {self.column} columns\n"
# Make string identifier
__a = 0
for row_vector in self.array:
for obj in row_vector:
__a = max(lowerCamelCase , len(str(lowerCamelCase ) ) )
__a = F"%{max_element_length}s"
# Make string and return
def single_line(lowerCamelCase ) -> str:
nonlocal string_format_identifier
__a = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(lowerCamelCase ) for row_vector in self.array )
return s
def __repr__( self ):
return str(self )
def a__ ( self , lowerCamelCase ):
if not (isinstance(lowerCamelCase , (list, tuple) ) and len(lowerCamelCase ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self , lowerCamelCase ):
assert self.validate_indicies(lowerCamelCase )
return self.array[loc[0]][loc[1]]
def __setitem__( self , lowerCamelCase , lowerCamelCase ):
assert self.validate_indicies(lowerCamelCase )
__a = value
def __add__( self , lowerCamelCase ):
assert isinstance(lowerCamelCase , lowerCamelCase )
assert self.row == another.row and self.column == another.column
# Add
__a = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__a = self[r, c] + another[r, c]
return result
def __neg__( self ):
__a = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__a = -self[r, c]
return result
def __sub__( self , lowerCamelCase ):
return self + (-another)
def __mul__( self , lowerCamelCase ):
if isinstance(lowerCamelCase , (int, float) ): # Scalar multiplication
__a = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__a = self[r, c] * another
return result
elif isinstance(lowerCamelCase , lowerCamelCase ): # Matrix multiplication
assert self.column == another.row
__a = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
__a = F"Unsupported type given for another ({type(lowerCamelCase )})"
raise TypeError(lowerCamelCase )
def a__ ( self ):
__a = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
__a = self[r, c]
return result
def a__ ( self , lowerCamelCase , lowerCamelCase ):
assert isinstance(lowerCamelCase , lowerCamelCase ) and isinstance(lowerCamelCase , lowerCamelCase )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
__a = v.transpose()
__a = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def _lowerCamelCase( ):
# a^(-1)
__a = Matrix(3 , 3 , 0 )
for i in range(3 ):
__a = 1
print(F"a^(-1) is {ainv}" )
# u, v
__a = Matrix(3 , 1 , 0 )
__a , __a , __a = 1, 2, -3
__a = Matrix(3 , 1 , 0 )
__a , __a , __a = 4, -2, 5
print(F"u is {u}" )
print(F"v is {v}" )
print(F"uv^T is {u * v.transpose()}" )
# Sherman Morrison
print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(a , a )}" )
def _lowerCamelCase( ):
import doctest
doctest.testmod()
testa()
| 261 | 1 |
"""simple docstring"""
import os
import string
import sys
a = 1 << 8
a = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARROW_KEY_FLAG,
'''mod_int''': 91,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 50,
'''delete''': 51,
'''pg_up''': 53,
'''pg_down''': 54,
}
a = KEYMAP['''up''']
a = KEYMAP['''left''']
if sys.platform == "win32":
a = []
a = {
B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(10):
a = ord(str(i))
def _snake_case ( ) -> Any:
'''simple docstring'''
if os.name == "nt":
import msvcrt
_A = 'mbcs'
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_snake_case ) == 0:
# Read the keystroke
_A = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
_A = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
_A = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['mod_int'] ) )
WIN_CH_BUFFER.append(_snake_case )
if ord(_snake_case ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
_A = chr(KEYMAP['esc'] )
except KeyError:
_A = cha[1]
else:
_A = ch.decode(_snake_case )
else:
_A = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
_A = sys.stdin.fileno()
_A = termios.tcgetattr(_snake_case )
try:
tty.setraw(_snake_case )
_A = sys.stdin.read(1 )
finally:
termios.tcsetattr(_snake_case , termios.TCSADRAIN , _snake_case )
return ch
def _snake_case ( ) -> Tuple:
'''simple docstring'''
_A = get_raw_chars()
if ord(_snake_case ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_snake_case ) == KEYMAP["esc"]:
_A = get_raw_chars()
if ord(_snake_case ) == KEYMAP["mod_int"]:
_A = get_raw_chars()
if ord(_snake_case ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_snake_case ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_snake_case ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 271 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class lowercase_ :
'''simple docstring'''
def __init__( self : str , _UpperCAmelCase : list[int] , _UpperCAmelCase : list[list[int]] , _UpperCAmelCase : list[list[int]] , ):
_A = claim_vector
_A = allocated_resources_table
_A = maximum_claim_table
def lowerCAmelCase_ ( self : Tuple ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def lowerCAmelCase_ ( self : Tuple ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def lowerCAmelCase_ ( self : List[Any] ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_UpperCAmelCase ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def lowerCAmelCase_ ( self : List[Any] ):
return {self.__need().index(_UpperCAmelCase ): i for i in self.__need()}
def lowerCAmelCase_ ( self : List[str] , **_UpperCAmelCase : int ):
_A = self.__need()
_A = self.__allocated_resources_table
_A = self.__available_resources()
_A = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print('_' * 50 + '\n' )
while need_list:
_A = False
for each_need in need_list:
_A = True
for index, need in enumerate(_UpperCAmelCase ):
if need > available_resources[index]:
_A = False
break
if execution:
_A = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
_A = original_need_index
print(F'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_UpperCAmelCase )
# update available/freed resources stack
_A = np.array(_UpperCAmelCase ) + np.array(
alloc_resources_table[process_number] )
print(
'Updated available resource stack for processes: '
+ ' '.join([str(_UpperCAmelCase ) for x in available_resources] ) )
break
if safe:
print('The process is in a safe state.\n' )
else:
print('System in unsafe state. Aborting...\n' )
break
def lowerCAmelCase_ ( self : Union[str, Any] ):
print(' ' * 9 + 'Allocated Resource Table' )
for item in self.__allocated_resources_table:
print(
F'''P{self.__allocated_resources_table.index(_UpperCAmelCase ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(' ' * 9 + 'System Resource Table' )
for item in self.__maximum_claim_table:
print(
F'''P{self.__maximum_claim_table.index(_UpperCAmelCase ) + 1}'''
+ ' '.join(F'''{it:>8}''' for it in item )
+ '\n' )
print(
'Current Usage by Active Processes: '
+ ' '.join(str(_UpperCAmelCase ) for x in self.__claim_vector ) )
print(
'Initial Available Resources: '
+ ' '.join(str(_UpperCAmelCase ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 271 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _A ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str = "cpu" , SCREAMING_SNAKE_CASE : Union[str, None] = None ):
"""simple docstring"""
a__ : int =torch.load(SCREAMING_SNAKE_CASE , map_location=SCREAMING_SNAKE_CASE )
for k, v in tqdm(state_dict.items() ):
if not isinstance(SCREAMING_SNAKE_CASE , torch.Tensor ):
raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" )
a__ : Tuple =v.half()
if save_path is None: # overwrite src_path
a__ : Optional[int] =src_path
torch.save(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
fire.Fire(convert)
| 95 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE_:str = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Union[str, Any] = [
"""TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AdaptiveEmbedding""",
"""TransfoXLForSequenceClassification""",
"""TransfoXLLMHeadModel""",
"""TransfoXLModel""",
"""TransfoXLPreTrainedModel""",
"""load_tf_weights_in_transfo_xl""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Any = [
"""TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAdaptiveEmbedding""",
"""TFTransfoXLForSequenceClassification""",
"""TFTransfoXLLMHeadModel""",
"""TFTransfoXLMainLayer""",
"""TFTransfoXLModel""",
"""TFTransfoXLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_:Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 116 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=3_0 , snake_case_=2 , snake_case_=3 , snake_case_=True , snake_case_=True , snake_case_=3_2 , snake_case_=5 , snake_case_=4 , snake_case_=3_7 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1_0 , snake_case_=0.02 , snake_case_=3 , snake_case_=0.6 , snake_case_=None , ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = parent
UpperCAmelCase_ : Optional[Any] = batch_size
UpperCAmelCase_ : Tuple = image_size
UpperCAmelCase_ : Dict = patch_size
UpperCAmelCase_ : Union[str, Any] = num_channels
UpperCAmelCase_ : Optional[int] = is_training
UpperCAmelCase_ : Dict = use_labels
UpperCAmelCase_ : Tuple = hidden_size
UpperCAmelCase_ : Optional[Any] = num_hidden_layers
UpperCAmelCase_ : Dict = num_attention_heads
UpperCAmelCase_ : Dict = intermediate_size
UpperCAmelCase_ : Union[str, Any] = hidden_act
UpperCAmelCase_ : Any = hidden_dropout_prob
UpperCAmelCase_ : Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase_ : int = type_sequence_label_size
UpperCAmelCase_ : List[Any] = initializer_range
UpperCAmelCase_ : str = mask_ratio
UpperCAmelCase_ : str = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase_ : Optional[Any] = (image_size // patch_size) ** 2
UpperCAmelCase_ : Union[str, Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ : Optional[Any] = None
if self.use_labels:
UpperCAmelCase_ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ : Any = self.get_config()
return config, pixel_values, labels
def _UpperCamelCase ( self ):
'''simple docstring'''
return ViTMAEConfig(
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=snake_case_ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Any = ViTMAEModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : Union[str, Any] = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = ViTMAEForPreTraining(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : Tuple = model(snake_case_ )
UpperCAmelCase_ : Dict = (self.image_size // self.patch_size) ** 2
UpperCAmelCase_ : str = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : int = ViTMAEForPreTraining(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCAmelCase_ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ : Union[str, Any] = model(snake_case_ )
UpperCAmelCase_ : Union[str, Any] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Any = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = config_and_inputs
UpperCAmelCase_ : Optional[int] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ :Dict = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
lowerCamelCase_ :Tuple = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
lowerCamelCase_ :str = False
lowerCamelCase_ :Optional[int] = False
lowerCamelCase_ :str = False
lowerCamelCase_ :int = False
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : str = ViTMAEModelTester(self )
UpperCAmelCase_ : str = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=3_7 )
def _UpperCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='ViTMAE does not use inputs_embeds' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : int = model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase_ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : List[Any] = model_class(snake_case_ )
UpperCAmelCase_ : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ : Dict = [*signature.parameters.keys()]
UpperCAmelCase_ : List[str] = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case_ )
def _UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ ):
'''simple docstring'''
np.random.seed(2 )
UpperCAmelCase_ : int = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase_ : Optional[int] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase_ : Optional[int] = torch.from_numpy(snake_case_ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase_ : Optional[int] = pt_noise
super().check_pt_tf_models(snake_case_ , snake_case_ , snake_case_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ : int = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
UpperCAmelCase_ : List[str] = outputs[0].cpu().numpy()
UpperCAmelCase_ : Tuple = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
UpperCAmelCase_ : List[Any] = model_class.from_pretrained(snake_case_ )
model.to(snake_case_ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase_ : Dict = model(**self._prepare_for_class(snake_case_ , snake_case_ ) )
# Make sure we don't have nans
UpperCAmelCase_ : Optional[Any] = after_outputs[0].cpu().numpy()
UpperCAmelCase_ : List[str] = 0
UpperCAmelCase_ : Union[str, Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(snake_case_ , 1E-5 )
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(
reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results.' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ : List[Any] = ViTMAEModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _lowerCamelCase ( ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None
@slow
def _UpperCamelCase ( self ):
'''simple docstring'''
np.random.seed(2 )
UpperCAmelCase_ : Dict = ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' ).to(snake_case_ )
UpperCAmelCase_ : Any = self.default_image_processor
UpperCAmelCase_ : Any = prepare_img()
UpperCAmelCase_ : Optional[int] = image_processor(images=snake_case_ , return_tensors='pt' ).to(snake_case_ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase_ : List[str] = ViTMAEConfig()
UpperCAmelCase_ : List[Any] = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase_ : List[str] = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase_ : int = model(**snake_case_ , noise=torch.from_numpy(snake_case_ ).to(device=snake_case_ ) )
# verify the logits
UpperCAmelCase_ : Any = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape , snake_case_ )
UpperCAmelCase_ : Optional[int] = torch.tensor(
[[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(snake_case_ ) , atol=1E-4 ) )
| 274 | '''simple docstring'''
snake_case__ : str = '''Tobias Carryer'''
from time import time
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=int(time() ) ): # noqa: B008
'''simple docstring'''
UpperCAmelCase_ : str = multiplier
UpperCAmelCase_ : Dict = increment
UpperCAmelCase_ : Tuple = modulo
UpperCAmelCase_ : Dict = seed
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Union[str, Any] = (self.multiplier * self.seed + self.increment) % self.modulo
return self.seed
if __name__ == "__main__":
# Show the LCG in action.
snake_case__ : Any = LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
while True:
print(lcg.next_number())
| 274 | 1 |
"""simple docstring"""
__snake_case : List[Any] = 65_521
def _lowercase ( __snake_case ) -> int:
__lowerCAmelCase : int = 1
__lowerCAmelCase : List[Any] = 0
for plain_chr in plain_text:
__lowerCAmelCase : Dict = (a + ord(__UpperCamelCase )) % MOD_ADLER
__lowerCAmelCase : List[Any] = (b + a) % MOD_ADLER
return (b << 16) | a | 269 | import warnings
from ..trainer import Trainer
from ..utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
class __snake_case ( lowerCamelCase_ ):
def __init__( self : Tuple , _lowercase : Optional[int]=None , **_lowercase : List[Any] ):
"""simple docstring"""
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" , _lowercase , )
super().__init__(args=_lowercase , **_lowercase )
| 219 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
SCREAMING_SNAKE_CASE = _modexpt(SCREAMING_SNAKE_CASE_ , exponent // 2 , SCREAMING_SNAKE_CASE_ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(SCREAMING_SNAKE_CASE_ , exponent - 1 , SCREAMING_SNAKE_CASE_ )) % modulo_value
def lowercase (SCREAMING_SNAKE_CASE_ : int = 17_77 , SCREAMING_SNAKE_CASE_ : int = 18_55 , SCREAMING_SNAKE_CASE_ : int = 8 ) -> int:
SCREAMING_SNAKE_CASE = base
for _ in range(1 , SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = _modexpt(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 10**digits )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 38 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str ) -> Any:
# Load configuration defined in the metadata file
with open(SCREAMING_SNAKE_CASE_ ) as metadata_file:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_ , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )['module']
# Load the entity vocab file
SCREAMING_SNAKE_CASE = load_original_entity_vocab(SCREAMING_SNAKE_CASE_ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'r' ) as f:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'MLukeTokenizer'
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['@'] )[0]
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['#'] )[0]
SCREAMING_SNAKE_CASE = state_dict['embeddings.word_embeddings.weight']
SCREAMING_SNAKE_CASE = word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE = state_dict[bias_name]
SCREAMING_SNAKE_CASE = decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE = F'encoder.layer.{layer_index}.attention.self.'
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE = state_dict['entity_embeddings.entity_embeddings.weight']
SCREAMING_SNAKE_CASE = entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE = state_dict['entity_predictions.bias']
SCREAMING_SNAKE_CASE = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE = LukeForMaskedLM(config=SCREAMING_SNAKE_CASE_ ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
SCREAMING_SNAKE_CASE = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
SCREAMING_SNAKE_CASE = state_dict[key]
else:
SCREAMING_SNAKE_CASE = state_dict[key]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
if set(SCREAMING_SNAKE_CASE_ ) != {"luke.embeddings.position_ids"}:
raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}' )
if set(SCREAMING_SNAKE_CASE_ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ , task='entity_classification' )
SCREAMING_SNAKE_CASE = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
SCREAMING_SNAKE_CASE = (0, 9)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 33, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 1, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'Tokyo is the capital of <mask>.'
SCREAMING_SNAKE_CASE = (24, 30)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = encoding['input_ids'][0].tolist()
SCREAMING_SNAKE_CASE = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
SCREAMING_SNAKE_CASE = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE_ ) )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = ['[MASK]', '[PAD]', '[UNK]']
SCREAMING_SNAKE_CASE = [json.loads(SCREAMING_SNAKE_CASE_ ) for line in open(SCREAMING_SNAKE_CASE_ )]
SCREAMING_SNAKE_CASE = {}
for entry in data:
SCREAMING_SNAKE_CASE = entry['id']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE = entity_id
break
SCREAMING_SNAKE_CASE = F'{language}:{entity_name}'
SCREAMING_SNAKE_CASE = entity_id
return new_mapping
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
__UpperCamelCase = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 38 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Optional[int] = """▁"""
__lowerCamelCase : List[str] = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
__lowerCamelCase : Tuple = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
__lowerCamelCase : Optional[Any] = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
__lowerCamelCase : int = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
__lowerCamelCase : Union[str, Any] = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class A__ ( __snake_case ):
_UpperCAmelCase :List[str] = ["input_ids"]
_UpperCAmelCase :Any = VOCAB_FILES_NAMES
_UpperCAmelCase :Optional[int] = PRETRAINED_INIT_CONFIGURATION
_UpperCAmelCase :List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase :List[str] = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase :Tuple = RESOURCE_FILES_NAMES
def __init__( self , A_ , A_=None , A_=False , A_="utf8" , A_="[UNK]" , A_="[SEP]" , A_="[PAD]" , A_="[CLS]" , A_="[MASK]" , A_ = None , **A_ , ):
'''simple docstring'''
UpperCamelCase : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=A_ , unk_token=A_ , sep_token=A_ , pad_token=A_ , cls_token=A_ , mask_token=A_ , vocab_file=A_ , encoding=A_ , sp_model_kwargs=self.sp_model_kwargs , **A_ , )
UpperCamelCase : List[str] = do_lower_case
UpperCamelCase : List[Any] = sentencepiece_model_ckpt
UpperCamelCase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A_ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
UpperCamelCase : List[Any] = self.load_vocab(filepath=A_ )
else:
UpperCamelCase : Optional[Any] = {self.sp_model.id_to_piece(A_ ): id for id in range(self.sp_model.get_piece_size() )}
UpperCamelCase : Union[str, Any] = {v: k for k, v in self.vocab.items()}
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if text is None:
return None
UpperCamelCase : Optional[Any] = self.tokenize(A_ )
UpperCamelCase , UpperCamelCase : Any = "", []
for i, ch in enumerate(A_ ):
if ch in self.SP_CHAR_MAPPING:
UpperCamelCase : int = self.SP_CHAR_MAPPING.get(A_ )
else:
UpperCamelCase : int = unicodedata.normalize("NFKC" , A_ )
if self.is_whitespace(A_ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(A_ ) )
UpperCamelCase , UpperCamelCase , UpperCamelCase : str = normalized_text, [], 0
if self.do_lower_case:
UpperCamelCase : List[str] = text.lower()
for token in split_tokens:
if token[:1] == "▁":
UpperCamelCase : List[Any] = token[1:]
UpperCamelCase : Tuple = text[offset:].index(A_ ) + offset
UpperCamelCase : List[Any] = start + len(A_ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
UpperCamelCase : Dict = end
return token_mapping
@property
def __UpperCamelCase( self ):
'''simple docstring'''
return len(self.vocab )
def __UpperCamelCase( self ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ):
'''simple docstring'''
UpperCamelCase : Tuple = self.__dict__.copy()
UpperCamelCase : Dict = None
return state
def __setstate__( self , A_ ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCamelCase : Optional[int] = {}
UpperCamelCase : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(A_ , A_ ) for c in text) )
def __UpperCamelCase( self , A_ , A_=False , A_=64 , A_=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
UpperCamelCase : Any = True
if self.sp_model_kwargs.get("alpha" ) is not None:
UpperCamelCase : Union[str, Any] = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
UpperCamelCase : Dict = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
UpperCamelCase : Optional[int] = self.sp_model.EncodeAsPieces(A_ )
else:
UpperCamelCase : List[str] = self.sp_model.SampleEncodeAsPieces(A_ , A_ , A_ )
UpperCamelCase : Tuple = []
for pi, piece in enumerate(A_ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(A_ ) and pi != 0:
new_pieces.append(A_ )
continue
else:
continue
UpperCamelCase : str = 0
for i, chunk in enumerate(A_ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(A_ ) or self.is_punct(A_ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(A_ )
UpperCamelCase : List[str] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
UpperCamelCase : Any = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
UpperCamelCase : Dict = i
if len(A_ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
UpperCamelCase : Tuple = "".join(A_ ).replace(A_ , " " ).strip()
return out_string
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = self.convert_ids_to_tokens(A_ )
UpperCamelCase : Optional[Any] = "".join(A_ ).replace(A_ , " " ).strip()
return out_string
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self.vocab.get(A_ , self.vocab.get(self.unk_token ) )
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
return self.reverse_vocab.get(A_ , self.unk_token )
def __UpperCamelCase( self , A_ , A_=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase : int = [self.cls_token_id]
UpperCamelCase : Optional[int] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def __UpperCamelCase( self , A_ , A_=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def __UpperCamelCase( self , A_ , A_=None , A_=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1]
def __UpperCamelCase( self , A_ , A_ = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(A_ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(A_ ) + 1) + [1] * (len(A_ ) + 3)
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(A_ ) == 1:
UpperCamelCase : Optional[Any] = unicodedata.category(A_ )
if cat == "Zs":
return True
return False
def __UpperCamelCase( self , A_ ):
'''simple docstring'''
UpperCamelCase : str = {}
with io.open(A_ , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(A_ ):
UpperCamelCase : List[Any] = line.rstrip("\n" )
UpperCamelCase : Any = int(A_ )
return token_to_idx
def __UpperCamelCase( self , A_ , A_ = None ):
'''simple docstring'''
UpperCamelCase : Any = 0
if os.path.isdir(A_ ):
UpperCamelCase : Optional[int] = os.path.join(
A_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
UpperCamelCase : List[Any] = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(A_ , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda A_ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCamelCase : Any = token_index
writer.write(token + "\n" )
index += 1
UpperCamelCase : Any = os.path.join(A_ , "sentencepiece.bpe.model" )
with open(A_ , "wb" ) as fi:
UpperCamelCase : int = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (vocab_file,)
| 52 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase : Optional[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = XGLMTokenizer
_UpperCAmelCase : List[Any] = XGLMTokenizerFast
_UpperCAmelCase : Optional[int] = True
_UpperCAmelCase : Tuple = True
def _SCREAMING_SNAKE_CASE ( self : Tuple):
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE_: List[Any] = XGLMTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
tokenizer.save_pretrained(self.tmpdirname)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
SCREAMING_SNAKE_CASE_: Optional[Any] = "<pad>"
SCREAMING_SNAKE_CASE_: int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__) , lowerCAmelCase__)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__) , lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Optional[int] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0] , "<s>")
self.assertEqual(vocab_keys[1] , "<pad>")
self.assertEqual(len(lowerCAmelCase__) , 1008)
def _SCREAMING_SNAKE_CASE ( self : Any):
self.assertEqual(self.get_tokenizer().vocab_size , 1008)
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: Optional[int] = XGLMTokenizer(lowerCAmelCase__ , keep_accents=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = tokenizer.tokenize("This is a test")
self.assertListEqual(lowerCAmelCase__ , ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase__) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
SCREAMING_SNAKE_CASE_: List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
lowerCAmelCase__ , [
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",
".",
] , )
SCREAMING_SNAKE_CASE_: Optional[Any] = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE_: List[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase__)
self.assertListEqual(
lowerCAmelCase__ , [
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 : Any):
return XGLMTokenizer.from_pretrained("facebook/xglm-564M")
def _SCREAMING_SNAKE_CASE ( self : str):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCAmelCase__ , f.name)
SCREAMING_SNAKE_CASE_: Tuple = XGLMTokenizer(f.name , keep_accents=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = pickle.dumps(lowerCAmelCase__)
pickle.loads(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : str):
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE_: Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE_: List[str] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_: Any = "I was born in 92000, and this is falsé."
SCREAMING_SNAKE_CASE_: Union[str, Any] = tokenizer.tokenize(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = rust_tokenizer.tokenize(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[int] = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_: str = tokenizer.encode(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Dict = "Hello World!"
SCREAMING_SNAKE_CASE_: Union[str, Any] = [2, 3_1227, 4447, 35]
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__))
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
SCREAMING_SNAKE_CASE_: Union[str, Any] = (
"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"
)
# fmt: off
SCREAMING_SNAKE_CASE_: Optional[Any] = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(lowerCAmelCase__ , self.big_tokenizer.encode(lowerCAmelCase__))
@slow
def _SCREAMING_SNAKE_CASE ( self : int):
# fmt: off
SCREAMING_SNAKE_CASE_: str = {
"input_ids": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
"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, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase__ , model_name="facebook/xglm-564M" , padding=lowerCAmelCase__ , )
| 13 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_UpperCAmelCase = TypeVar("""T""")
class UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self , lowercase , lowercase ):
"""simple docstring"""
A_ : Any | T = None
A_ : int = len(lowercase )
A_ : list[T] = [any_type for _ in range(self.N )] + arr
A_ : Dict = fnc
self.build()
def lowerCAmelCase_ ( self ):
"""simple docstring"""
for p in range(self.N - 1 , 0 , -1 ):
A_ : List[Any] = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self , lowercase , lowercase ):
"""simple docstring"""
p += self.N
A_ : Union[str, Any] = v
while p > 1:
A_ : List[str] = p // 2
A_ : Optional[int] = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self , lowercase , lowercase ): # noqa: E741
"""simple docstring"""
A_ , A_ : Optional[int] = l + self.N, r + self.N
A_ : T | None = None
while l <= r:
if l % 2 == 1:
A_ : int = self.st[l] if res is None else self.fn(lowercase , self.st[l] )
if r % 2 == 0:
A_ : Any = self.st[r] if res is None else self.fn(lowercase , self.st[r] )
A_ , A_ : Union[str, Any] = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
_UpperCAmelCase = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
_UpperCAmelCase = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
_UpperCAmelCase = SegmentTree(test_array, min)
_UpperCAmelCase = SegmentTree(test_array, max)
_UpperCAmelCase = SegmentTree(test_array, lambda a, b: a + b)
def UpperCamelCase ( ):
'''simple docstring'''
for i in range(len(__lowercase ) ):
for j in range(__lowercase ,len(__lowercase ) ):
A_ : Optional[int] = reduce(__lowercase ,test_array[i : j + 1] )
A_ : Any = reduce(__lowercase ,test_array[i : j + 1] )
A_ : str = reduce(lambda __lowercase ,__lowercase : a + b ,test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__lowercase ,__lowercase )
assert max_range == max_segment_tree.query(__lowercase ,__lowercase )
assert sum_range == sum_segment_tree.query(__lowercase ,__lowercase )
test_all_segments()
for index, value in test_updates.items():
_UpperCAmelCase = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 192 | 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
_UpperCAmelCase = {
"""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 UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCAmelCase_ ( cls ):
"""simple docstring"""
A_ : Optional[int] = TOKEN
HfFolder.save_token(lowercase )
@classmethod
def lowerCAmelCase_ ( cls ):
"""simple docstring"""
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 lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Union[str, Any] = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
A_ : Dict = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase , getattr(lowercase , lowercase ) )
# 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(lowercase , repo_id='test-config' , push_to_hub=lowercase , use_auth_token=self._token )
A_ : Dict = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase , getattr(lowercase , lowercase ) )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[str] = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
A_ : List[Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase , getattr(lowercase , lowercase ) )
# 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(
lowercase , repo_id='valid_org/test-config-org' , push_to_hub=lowercase , use_auth_token=self._token )
A_ : Optional[Any] = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase , getattr(lowercase , lowercase ) )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
CustomConfig.register_for_auto_class()
A_ : Optional[int] = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
A_ : Optional[int] = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=lowercase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : int = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
A_ : Optional[int] = c.n_embd + 1 # int
A_ : List[str] = c.resid_pdrop + 1.0 # float
A_ : str = not c.scale_attn_weights # bool
A_ : Optional[int] = 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(lowercase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(lowercase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(lowercase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(lowercase , c.summary_type , 'mismatch for key: summary_type' )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Optional[int] = PretrainedConfig()
A_ : 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(
lowercase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
A_ : List[str] = [key for key, value in config_common_kwargs.items() if value == getattr(lowercase , lowercase )]
if len(lowercase ) > 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(lowercase )}.''' )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
with self.assertRaises(lowercase ):
# config is in subfolder, the following should not work without specifying the subfolder
A_ : Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
A_ : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(lowercase )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : int = mock.Mock()
A_ : int = 5_0_0
A_ : Union[str, Any] = {}
A_ : List[str] = HTTPError
A_ : List[Any] = {}
# Download this model to make sure it's in the cache.
A_ : Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=lowercase ) as mock_head:
A_ : Optional[int] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : Any = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : int = AutoConfig.from_pretrained('bert-base-cased' )
A_ : Tuple = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowercase )
A_ : Dict = 2
json.dump(configuration.to_dict() , open(os.path.join(lowercase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
A_ : Tuple = AutoConfig.from_pretrained(lowercase )
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
A_ : int = ['config.42.0.0.json']
A_ : str = 7_6_8
configuration.save_pretrained(lowercase )
shutil.move(os.path.join(lowercase , 'config.4.0.0.json' ) , os.path.join(lowercase , 'config.42.0.0.json' ) )
A_ : str = AutoConfig.from_pretrained(lowercase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : List[Any] = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
A_ : List[Any] = 'v4.0.0'
A_ , A_ : List[str] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowercase , return_unused_kwargs=lowercase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowercase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
A_ : Optional[int] = 'v3.0.0'
A_ : List[Any] = old_transformers.models.auto.AutoConfig.from_pretrained(lowercase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 192 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A__ ( _lowerCamelCase):
@staticmethod
@abstractmethod
def __lowerCamelCase ( _SCREAMING_SNAKE_CASE ):
raise NotImplementedError()
@abstractmethod
def __lowerCamelCase ( self ):
raise NotImplementedError() | 86 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class A__ ( _lowerCamelCase):
A_ : Optional[int] = 'poolformer'
def __init__( self , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4.0 , _SCREAMING_SNAKE_CASE=[2, 2, 6, 2] , _SCREAMING_SNAKE_CASE=[64, 1_28, 3_20, 5_12] , _SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , _SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , _SCREAMING_SNAKE_CASE=[2, 1, 1, 1] , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=0.02 , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : int = num_channels
__lowerCAmelCase : str = patch_size
__lowerCAmelCase : Optional[Any] = stride
__lowerCAmelCase : Optional[int] = padding
__lowerCAmelCase : List[Any] = pool_size
__lowerCAmelCase : int = hidden_sizes
__lowerCAmelCase : str = mlp_ratio
__lowerCAmelCase : Optional[int] = depths
__lowerCAmelCase : str = patch_sizes
__lowerCAmelCase : str = strides
__lowerCAmelCase : Optional[int] = num_encoder_blocks
__lowerCAmelCase : Any = drop_path_rate
__lowerCAmelCase : Any = hidden_act
__lowerCAmelCase : Dict = use_layer_scale
__lowerCAmelCase : Union[str, Any] = layer_scale_init_value
__lowerCAmelCase : Dict = initializer_range
super().__init__(**_SCREAMING_SNAKE_CASE )
class A__ ( _lowerCamelCase):
A_ : List[str] = version.parse('1.11')
@property
def __lowerCamelCase ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __lowerCamelCase ( self ):
return 2E-3 | 86 | 1 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__A : Dict = '''pt'''
elif is_tf_available():
__A : Union[str, Any] = '''tf'''
else:
__A : Tuple = '''jax'''
class __A ( lowerCAmelCase , unittest.TestCase ):
lowerCAmelCase_ : Union[str, Any] = ByTaTokenizer
lowerCAmelCase_ : Dict = False
def lowercase__ ( self : Any ):
super().setUp()
lowerCAmelCase : List[Any] = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowercase__ ( self : int ):
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def lowercase__ ( self : str , **UpperCAmelCase_ : Tuple ):
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowercase__ ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : str=False , UpperCAmelCase_ : int=20 , UpperCAmelCase_ : Optional[Any]=5 ):
# XXX The default common tokenizer tests assume that every ID is decodable on its own.
# This assumption is invalid for ByT5 because single bytes might not be
# valid utf-8 (byte 128 for instance).
# Here we're overriding the smallest possible method to provide
# a clean sequence without making the same assumption.
lowerCAmelCase : Any = []
for i in range(len(UpperCAmelCase_ ) ):
try:
lowerCAmelCase : Optional[int] = tokenizer.decode([i] , clean_up_tokenization_spaces=UpperCAmelCase_ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
lowerCAmelCase : Union[str, Any] = list(filter(lambda UpperCAmelCase_ : re.match(R'^[ a-zA-Z]+$' , t[1] ) , UpperCAmelCase_ ) )
lowerCAmelCase : Tuple = list(filter(lambda UpperCAmelCase_ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=UpperCAmelCase_ ) , UpperCAmelCase_ ) )
if max_length is not None and len(UpperCAmelCase_ ) > max_length:
lowerCAmelCase : Optional[int] = toks[:max_length]
if min_length is not None and len(UpperCAmelCase_ ) < min_length and len(UpperCAmelCase_ ) > 0:
while len(UpperCAmelCase_ ) < min_length:
lowerCAmelCase : Union[str, Any] = toks + toks
# toks_str = [t[1] for t in toks]
lowerCAmelCase : List[str] = [t[0] for t in toks]
# Ensure consistency
lowerCAmelCase : Tuple = tokenizer.decode(UpperCAmelCase_ , clean_up_tokenization_spaces=UpperCAmelCase_ )
if " " not in output_txt and len(UpperCAmelCase_ ) > 1:
lowerCAmelCase : Tuple = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=UpperCAmelCase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=UpperCAmelCase_ )
)
if with_prefix_space:
lowerCAmelCase : Union[str, Any] = ' ' + output_txt
lowerCAmelCase : Optional[int] = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
return output_txt, output_ids
def lowercase__ ( self : List[str] ):
lowerCAmelCase : int = self.ta_base_tokenizer
lowerCAmelCase : Dict = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
lowerCAmelCase : Tuple = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def lowercase__ ( self : str ):
lowerCAmelCase : str = self.ta_base_tokenizer
lowerCAmelCase : str = 'Unicode €.'
lowerCAmelCase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
lowerCAmelCase : int = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
self.assertEqual(encoded['input_ids'] , UpperCAmelCase_ )
# decoding
lowerCAmelCase : int = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , 'Unicode €.</s>' )
lowerCAmelCase : Optional[Any] = tokenizer('e è é ê ë' )
lowerCAmelCase : List[Any] = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
self.assertEqual(encoded['input_ids'] , UpperCAmelCase_ )
# decoding
lowerCAmelCase : List[Any] = tokenizer.decode(UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def lowercase__ ( self : Tuple ):
lowerCAmelCase : Dict = self.ta_base_tokenizer
lowerCAmelCase : List[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
lowerCAmelCase : Optional[Any] = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
# fmt: on
lowerCAmelCase : Dict = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
if FRAMEWORK != "jax":
lowerCAmelCase : Union[str, Any] = list(batch.input_ids.numpy()[0] )
else:
lowerCAmelCase : Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : int = self.ta_base_tokenizer
lowerCAmelCase : Tuple = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCAmelCase : Optional[Any] = tokenizer(UpperCAmelCase_ , padding=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , UpperCAmelCase_ )
self.assertIn('attention_mask' , UpperCAmelCase_ )
self.assertNotIn('decoder_input_ids' , UpperCAmelCase_ )
self.assertNotIn('decoder_attention_mask' , UpperCAmelCase_ )
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : Dict = self.ta_base_tokenizer
lowerCAmelCase : List[Any] = [
'Summary of the text.',
'Another summary.',
]
lowerCAmelCase : Dict = tokenizer(
text_target=UpperCAmelCase_ , max_length=32 , padding='max_length' , truncation=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : Any = self.ta_base_tokenizer
lowerCAmelCase : int = ['A long paragraph for summarization. </s>']
lowerCAmelCase : Optional[Any] = ['Summary of the text. </s>']
# fmt: off
lowerCAmelCase : Optional[Any] = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
lowerCAmelCase : List[str] = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
# fmt: on
lowerCAmelCase : Optional[Any] = tokenizer(UpperCAmelCase_ , text_target=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , batch['input_ids'][0] )
self.assertEqual(UpperCAmelCase_ , batch['labels'][0] )
def lowercase__ ( self : List[Any] ):
# safety check on max_len default value so we are sure the test works
lowerCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
lowerCAmelCase : Dict = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase : Dict = tempfile.mkdtemp()
lowerCAmelCase : Tuple = ' He is very happy, UNwant\u00E9d,running'
lowerCAmelCase : Tuple = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
tokenizer.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Tuple = tokenizer.__class__.from_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Dict = after_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
shutil.rmtree(UpperCAmelCase_ )
lowerCAmelCase : Dict = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
lowerCAmelCase : List[Any] = tempfile.mkdtemp()
lowerCAmelCase : Union[str, Any] = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
lowerCAmelCase : Tuple = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
lowerCAmelCase : List[Any] = tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
tokenizer.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = tokenizer.__class__.from_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Tuple = after_tokenizer.encode(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
lowerCAmelCase : Optional[int] = tokenizer.__class__.from_pretrained(UpperCAmelCase_ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(UpperCAmelCase_ )
def lowercase__ ( self : Dict ):
lowerCAmelCase : List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
lowerCAmelCase : int = json.load(UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
lowerCAmelCase : Dict = json.load(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = [f"<extra_id_{i}>" for i in range(125 )]
lowerCAmelCase : Tuple = added_tokens_extra_ids + [
'an_additional_special_token'
]
lowerCAmelCase : Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(UpperCAmelCase_ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
with open(os.path.join(UpperCAmelCase_ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
lowerCAmelCase : int = tokenizer_class.from_pretrained(
UpperCAmelCase_ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
lowerCAmelCase : Any = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=UpperCAmelCase_ )]
lowerCAmelCase : List[str] = tokenizer_class.from_pretrained(
UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def lowercase__ ( self : int ):
lowerCAmelCase : Any = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = tokenizer_class.from_pretrained(UpperCAmelCase_ )
self.assertTrue(tokenizer.decode([255] ) == '' )
def lowercase__ ( self : List[Any] ):
pass
def lowercase__ ( self : Any ):
pass
def lowercase__ ( self : List[str] ):
pass
def lowercase__ ( self : Optional[int] ):
pass
def lowercase__ ( self : List[Any] ):
# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
# and special added tokens as tokens
lowerCAmelCase : Tuple = self.get_tokenizers(fast=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ )
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase : Tuple = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
lowerCAmelCase : List[str] = tokenizer.convert_tokens_to_string(UpperCAmelCase_ )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}" ):
lowerCAmelCase : Tuple = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
lowerCAmelCase : Optional[int] = 0
lowerCAmelCase : List[str] = tokenizer.convert_ids_to_tokens(
UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
for attr in attributes_list:
setattr(UpperCAmelCase_ , attr + '_id' , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , attr + '_id' ) , UpperCAmelCase_ )
setattr(UpperCAmelCase_ , attr + '_id' , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
self.assertEqual(getattr(UpperCAmelCase_ , attr + '_id' ) , UpperCAmelCase_ )
setattr(UpperCAmelCase_ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(UpperCAmelCase_ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(UpperCAmelCase_ , 'additional_special_tokens_ids' ) , [] )
setattr(UpperCAmelCase_ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(UpperCAmelCase_ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(UpperCAmelCase_ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] )
| 323 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('String lengths must match!' )
lowerCAmelCase : Tuple = 0
for chara, chara in zip(_UpperCAmelCase, _UpperCAmelCase ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 323 | 1 |
'''simple docstring'''
import os
a_ : Optional[int] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def a_ ( __snake_case : str ) -> int:
"""simple docstring"""
lowerCamelCase_ =0
lowerCamelCase_ =0
while index < len(__snake_case ) - 1:
lowerCamelCase_ =SYMBOLS[numerals[index]]
lowerCamelCase_ =SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a_ ( __snake_case : int ) -> str:
"""simple docstring"""
lowerCamelCase_ =''''''
lowerCamelCase_ =num // 1000
numerals += m_count * "M"
num %= 1000
lowerCamelCase_ =num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
lowerCamelCase_ =num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a_ ( __snake_case : str = "/p089_roman.txt" ) -> int:
"""simple docstring"""
lowerCamelCase_ =0
with open(os.path.dirname(__snake_case ) + roman_numerals_filename ) as filea:
lowerCamelCase_ =filea.readlines()
for line in lines:
lowerCamelCase_ =line.strip()
lowerCamelCase_ =parse_roman_numerals(__snake_case )
lowerCamelCase_ =generate_roman_numerals(__snake_case )
savings += len(__snake_case ) - len(__snake_case )
return savings
if __name__ == "__main__":
print(F"""{solution() = }""")
| 75 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 199 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
UpperCAmelCase = logging.get_logger(__name__)
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : str = ["input_features"]
def __init__( self , A_=80 , A_=1_6000 , A_=160 , A_=30 , A_=400 , A_=0.0 , A_=False , **A_ , ) -> Optional[int]:
super().__init__(
feature_size=A_ , sampling_rate=A_ , padding_value=A_ , return_attention_mask=A_ , **A_ , )
lowerCAmelCase = n_fft
lowerCAmelCase = hop_length
lowerCAmelCase = chunk_length
lowerCAmelCase = chunk_length * sampling_rate
lowerCAmelCase = self.n_samples // hop_length
lowerCAmelCase = sampling_rate
lowerCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=A_ , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=A_ , norm="""slaney""" , mel_scale="""slaney""" , )
def __snake_case ( self , A_ ) -> np.ndarray:
lowerCAmelCase = spectrogram(
A_ , window_function(self.n_fft , """hann""" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="""log10""" , )
lowerCAmelCase = log_spec[:, :-1]
lowerCAmelCase = np.maximum(A_ , log_spec.max() - 8.0 )
lowerCAmelCase = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __snake_case ( A_ , A_ , A_ = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
lowerCAmelCase = np.array(A_ , np.intaa )
lowerCAmelCase = []
for vector, length in zip(A_ , attention_mask.sum(-1 ) ):
lowerCAmelCase = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
lowerCAmelCase = padding_value
normed_input_values.append(A_ )
else:
lowerCAmelCase = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self , A_ , A_ = True , A_ = None , A_ = None , A_ = None , A_ = "max_length" , A_ = None , A_ = None , A_ = None , **A_ , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
lowerCAmelCase = isinstance(A_ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
lowerCAmelCase = is_batched_numpy or (
isinstance(A_ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(A_ , np.ndarray ):
lowerCAmelCase = np.asarray(A_ , dtype=np.floataa )
elif isinstance(A_ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase = [np.asarray([raw_speech] ).T]
lowerCAmelCase = BatchFeature({"""input_features""": raw_speech} )
# convert into correct format for padding
lowerCAmelCase = self.pad(
A_ , padding=A_ , max_length=max_length if max_length else self.n_samples , truncation=A_ , pad_to_multiple_of=A_ , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
lowerCAmelCase = self.zero_mean_unit_var_norm(
padded_inputs["""input_features"""] , attention_mask=padded_inputs["""attention_mask"""] , padding_value=self.padding_value , )
lowerCAmelCase = np.stack(padded_inputs["""input_features"""] , axis=0 )
# make sure list is in array format
lowerCAmelCase = padded_inputs.get("""input_features""" ).transpose(2 , 0 , 1 )
lowerCAmelCase = [self._np_extract_fbank_features(A_ ) for waveform in input_features[0]]
if isinstance(input_features[0] , A_ ):
lowerCAmelCase = [np.asarray(A_ , dtype=np.floataa ) for feature in input_features]
else:
lowerCAmelCase = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
lowerCAmelCase = padded_inputs["""attention_mask"""][:, :: self.hop_length]
if return_tensors is not None:
lowerCAmelCase = padded_inputs.convert_to_tensors(A_ )
return padded_inputs
def __snake_case ( self ) -> Dict[str, Any]:
lowerCAmelCase = copy.deepcopy(self.__dict__ )
lowerCAmelCase = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output | 358 |
'''simple docstring'''
from torch import nn
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'Unsupported activation function: {act_fn}' ) | 187 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "wav2vec2"
def __init__( self, lowerCAmelCase__=32, lowerCAmelCase__=768, lowerCAmelCase__=12, lowerCAmelCase__=12, lowerCAmelCase__=3072, lowerCAmelCase__="gelu", lowerCAmelCase__=0.1, lowerCAmelCase__=0.1, lowerCAmelCase__=0.1, lowerCAmelCase__=0.0, lowerCAmelCase__=0.0, lowerCAmelCase__=0.1, lowerCAmelCase__=0.1, lowerCAmelCase__=0.02, lowerCAmelCase__=1e-5, lowerCAmelCase__="group", lowerCAmelCase__="gelu", lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512), lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2), lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2), lowerCAmelCase__=False, lowerCAmelCase__=128, lowerCAmelCase__=16, lowerCAmelCase__=False, lowerCAmelCase__=True, lowerCAmelCase__=0.05, lowerCAmelCase__=10, lowerCAmelCase__=2, lowerCAmelCase__=0.0, lowerCAmelCase__=10, lowerCAmelCase__=0, lowerCAmelCase__=320, lowerCAmelCase__=2, lowerCAmelCase__=0.1, lowerCAmelCase__=100, lowerCAmelCase__=256, lowerCAmelCase__=256, lowerCAmelCase__=0.1, lowerCAmelCase__="sum", lowerCAmelCase__=False, lowerCAmelCase__=False, lowerCAmelCase__=256, lowerCAmelCase__=(512, 512, 512, 512, 1500), lowerCAmelCase__=(5, 3, 3, 1, 1), lowerCAmelCase__=(1, 2, 3, 1, 1), lowerCAmelCase__=512, lowerCAmelCase__=0, lowerCAmelCase__=1, lowerCAmelCase__=2, lowerCAmelCase__=False, lowerCAmelCase__=3, lowerCAmelCase__=2, lowerCAmelCase__=3, lowerCAmelCase__=None, lowerCAmelCase__=None, **lowerCAmelCase__, ) -> List[Any]:
super().__init__(**lowerCAmelCase__, pad_token_id=lowerCAmelCase__, bos_token_id=lowerCAmelCase__, eos_token_id=lowerCAmelCase__)
snake_case_ = hidden_size
snake_case_ = feat_extract_norm
snake_case_ = feat_extract_activation
snake_case_ = list(lowerCAmelCase__)
snake_case_ = list(lowerCAmelCase__)
snake_case_ = list(lowerCAmelCase__)
snake_case_ = conv_bias
snake_case_ = num_conv_pos_embeddings
snake_case_ = num_conv_pos_embedding_groups
snake_case_ = len(self.conv_dim)
snake_case_ = num_hidden_layers
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_attention_heads
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = feat_proj_dropout
snake_case_ = final_dropout
snake_case_ = layerdrop
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = vocab_size
snake_case_ = do_stable_layer_norm
snake_case_ = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel)}`.')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case_ = apply_spec_augment
snake_case_ = mask_time_prob
snake_case_ = mask_time_length
snake_case_ = mask_time_min_masks
snake_case_ = mask_feature_prob
snake_case_ = mask_feature_length
snake_case_ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
snake_case_ = num_codevectors_per_group
snake_case_ = num_codevector_groups
snake_case_ = contrastive_logits_temperature
snake_case_ = feat_quantizer_dropout
snake_case_ = num_negatives
snake_case_ = codevector_dim
snake_case_ = proj_codevector_dim
snake_case_ = diversity_loss_weight
# ctc loss
snake_case_ = ctc_loss_reduction
snake_case_ = ctc_zero_infinity
# adapter
snake_case_ = add_adapter
snake_case_ = adapter_kernel_size
snake_case_ = adapter_stride
snake_case_ = num_adapter_layers
snake_case_ = output_hidden_size or hidden_size
snake_case_ = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case_ = list(lowerCAmelCase__)
snake_case_ = list(lowerCAmelCase__)
snake_case_ = list(lowerCAmelCase__)
snake_case_ = xvector_output_dim
@property
def a_ ( self) -> List[Any]:
return functools.reduce(operator.mul, self.conv_stride, 1)
| 69 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
lowerCamelCase : Optional[Any] =subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowerCamelCase : str =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()
lowerCamelCase : List[Any] ='''|'''.join(sys.argv[1:])
lowerCamelCase : str =re.compile(RF"""^({joined_dirs}).*?\.py$""")
lowerCamelCase : Optional[int] =[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''') | 189 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = ['model.decoder.embed_positions.weights']
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Any ) -> Optional[int]:
if "emb" in name:
__lowerCAmelCase : Tuple = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase : Any = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase : Tuple = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase : int = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase : Optional[int] = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase : Optional[int] = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase : int = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase : int = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase : str = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase : Any = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase : Any = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :OrderedDict , SCREAMING_SNAKE_CASE :int ) -> Tuple[Dict, Dict]:
__lowerCAmelCase : int = list(state_dict.keys() )
__lowerCAmelCase : Dict = {}
for key in keys:
__lowerCAmelCase : str = state_dict.pop(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = rename_keys(SCREAMING_SNAKE_CASE )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase : Optional[Any] = val[:hidden_size, :]
__lowerCAmelCase : List[str] = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase : str = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase : Any = val
else:
__lowerCAmelCase : int = val
return state_dict, enc_dec_proj_state_dict
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :str ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase : Optional[Any] = 1_024
__lowerCAmelCase : Dict = 24
__lowerCAmelCase : Union[str, Any] = 16
elif checkpoint == "medium":
__lowerCAmelCase : Tuple = 1_536
__lowerCAmelCase : Tuple = 48
__lowerCAmelCase : List[str] = 24
elif checkpoint == "large":
__lowerCAmelCase : List[Any] = 2_048
__lowerCAmelCase : Optional[int] = 48
__lowerCAmelCase : List[str] = 32
else:
raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
__lowerCAmelCase : List[Any] = MusicgenDecoderConfig(
hidden_size=SCREAMING_SNAKE_CASE , ffn_dim=hidden_size * 4 , num_hidden_layers=SCREAMING_SNAKE_CASE , num_attention_heads=SCREAMING_SNAKE_CASE , )
return config
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Optional[int] , SCREAMING_SNAKE_CASE :str=None , SCREAMING_SNAKE_CASE :List[Any]=None , SCREAMING_SNAKE_CASE :List[Any]="cpu" ) -> Optional[int]:
__lowerCAmelCase : Tuple = MusicGen.get_pretrained(SCREAMING_SNAKE_CASE , device=SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = fairseq_model.lm.state_dict()
__lowerCAmelCase : Optional[int] = rename_state_dict(
SCREAMING_SNAKE_CASE , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase : Optional[Any] = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase : List[str] = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase : Optional[int] = MusicgenForCausalLM(SCREAMING_SNAKE_CASE ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase : List[Any] = decoder.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}''' )
if len(SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}''' )
# init the composite model
__lowerCAmelCase : Optional[int] = MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE , audio_encoder=SCREAMING_SNAKE_CASE , decoder=SCREAMING_SNAKE_CASE )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE )
# check we can do a forward pass
__lowerCAmelCase : List[Any] = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase : Union[str, Any] = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase : List[Any] = model(input_ids=SCREAMING_SNAKE_CASE , decoder_input_ids=SCREAMING_SNAKE_CASE ).logits
if logits.shape != (8, 1, 2_048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase : Union[str, Any] = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase : Tuple = MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
# set the appropriate bos/pad token ids
__lowerCAmelCase : int = 2_048
__lowerCAmelCase : str = 2_048
# set other default generation config params
__lowerCAmelCase : List[str] = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase : List[str] = True
__lowerCAmelCase : Dict = 3.0
if pytorch_dump_folder is not None:
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
if repo_id:
logger.info(F'''Pushing model {checkpoint} to {repo_id}''' )
model.push_to_hub(SCREAMING_SNAKE_CASE )
processor.push_to_hub(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint',
default='small',
type=str,
help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
)
parser.add_argument(
'--pytorch_dump_folder',
required=True,
default=None,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
parser.add_argument(
'--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
)
_UpperCAmelCase = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub) | 351 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}
_UpperCAmelCase = {
'vocab_file': {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
},
'emoji_file': {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
},
}
_UpperCAmelCase = {
'abeja/gpt-neox-japanese-2.7b': 2048,
}
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :Optional[int] ) -> Optional[Any]:
with open(SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" ) as f:
__lowerCAmelCase : int = json.loads(f.read() )
__lowerCAmelCase : Dict = collections.OrderedDict()
__lowerCAmelCase : str = collections.OrderedDict()
__lowerCAmelCase : Union[str, Any] = collections.OrderedDict()
with open(SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" ) as f:
__lowerCAmelCase : Tuple = f.readlines()
__lowerCAmelCase : Tuple = [[t.rstrip("""\n""" )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n""" ).split(""",""" ) for t in token]
for idx, b in enumerate(SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Dict = b
__lowerCAmelCase : Dict = idx
for wd in b:
__lowerCAmelCase : List[str] = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class snake_case_ ( __lowercase ):
A_ = VOCAB_FILES_NAMES
A_ = PRETRAINED_VOCAB_FILES_MAP
A_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A_ = ['input_ids', 'attention_mask']
def __init__( self : str , _snake_case : Union[str, Any] , _snake_case : Optional[Any] , _snake_case : Any="<|endoftext|>" , _snake_case : str="<|endoftext|>" , _snake_case : str="<|startoftext|>" , _snake_case : List[Any]="<|endoftext|>" , _snake_case : str=False , **_snake_case : List[Any] , )->Union[str, Any]:
'''simple docstring'''
super().__init__(
unk_token=_snake_case , pad_token=_snake_case , bos_token=_snake_case , eos_token=_snake_case , do_clean_text=_snake_case , **_snake_case , )
if not os.path.isfile(_snake_case ):
raise ValueError(
F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
""" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
if not os.path.isfile(_snake_case ):
raise ValueError(
F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
""" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" )
__lowerCAmelCase : Any = do_clean_text
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = load_vocab_and_emoji(_snake_case , _snake_case )
__lowerCAmelCase : int = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def UpperCAmelCase__ ( self : int )->str:
'''simple docstring'''
return len(self.raw_vocab )
def UpperCAmelCase__ ( self : Tuple )->Any:
'''simple docstring'''
return dict(self.raw_vocab , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self : Any , _snake_case : str )->Optional[int]:
'''simple docstring'''
return self.subword_tokenizer.tokenize(_snake_case , clean=self.do_clean_text )
def UpperCAmelCase__ ( self : Optional[Any] , _snake_case : Optional[Any] )->Any:
'''simple docstring'''
return self.vocab.get(_snake_case , self.vocab.get(self.unk_token ) )
def UpperCAmelCase__ ( self : int , _snake_case : Any )->int:
'''simple docstring'''
return self.subword_tokenizer.convert_id_to_token(_snake_case )
def UpperCAmelCase__ ( self : Optional[int] , _snake_case : int )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : str = """""".join(_snake_case ).strip()
return out_string
def UpperCAmelCase__ ( self : List[str] , _snake_case : "Conversation" )->List[int]:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_snake_case , add_special_tokens=_snake_case ) + [self.eos_token_id] )
if len(_snake_case ) > self.model_max_length:
__lowerCAmelCase : List[str] = input_ids[-self.model_max_length :]
return input_ids
def UpperCAmelCase__ ( self : Optional[Any] , _snake_case : str , _snake_case : Optional[str] = None )->Tuple[str]:
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = 0
if os.path.isdir(_snake_case ):
__lowerCAmelCase : Dict = os.path.join(
_snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCAmelCase : List[Any] = os.path.join(
_snake_case , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""] )
else:
__lowerCAmelCase : Union[str, Any] = (
(filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
)
__lowerCAmelCase : Dict = (
(filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
)
with open(_snake_case , """w""" , encoding="""utf-8""" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
""" Please check that the vocabulary is not corrupted!""" )
__lowerCAmelCase : List[str] = token_index
writer.write(""",""".join(_snake_case ) + """\n""" )
index += 1
with open(_snake_case , """w""" , encoding="""utf-8""" ) as writer:
json.dump(self.emoji , _snake_case )
return vocab_file, emoji_file
class snake_case_ ( __lowercase ):
def __init__( self : Optional[Any] , _snake_case : str , _snake_case : Union[str, Any] , _snake_case : Optional[int] )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : Optional[Any] = vocab # same as swe
__lowerCAmelCase : str = ids_to_tokens # same as bpe
__lowerCAmelCase : Dict = emoji
__lowerCAmelCase : int = np.max([len(_snake_case ) for w in self.vocab.keys()] )
__lowerCAmelCase : str = re.compile(R"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)""" )
__lowerCAmelCase : Optional[Any] = re.compile(R"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*""" )
__lowerCAmelCase : Tuple = re.compile(R"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}""" )
__lowerCAmelCase : Optional[Any] = re.compile(
R"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
__lowerCAmelCase : Union[str, Any] = re.compile(
R"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" )
__lowerCAmelCase : str = re.compile(
R"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*""" )
__lowerCAmelCase : List[Any] = """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
__lowerCAmelCase : Union[str, Any] = """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
__lowerCAmelCase : str = str.maketrans({k: """<BLOCK>""" for k in keisen + blocks} )
def __len__( self : int )->int:
'''simple docstring'''
return len(self.ids_to_tokens )
def UpperCAmelCase__ ( self : List[str] , _snake_case : Any )->str:
'''simple docstring'''
__lowerCAmelCase : List[str] = self.content_repattera.sub("""<URL>""" , _snake_case )
__lowerCAmelCase : Tuple = self.content_repattera.sub("""<EMAIL>""" , _snake_case )
__lowerCAmelCase : Optional[Any] = self.content_repattera.sub("""<TEL>""" , _snake_case )
__lowerCAmelCase : str = self.content_repattera.sub("""<DATE>""" , _snake_case )
__lowerCAmelCase : Tuple = self.content_repattera.sub("""<DATE>""" , _snake_case )
__lowerCAmelCase : Tuple = self.content_repattera.sub("""<PRICE>""" , _snake_case )
__lowerCAmelCase : List[Any] = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
__lowerCAmelCase : str = content.replace("""<BLOCK><BLOCK>""" , """<BLOCK>""" )
return content
def UpperCAmelCase__ ( self : str , _snake_case : List[Any] , _snake_case : Optional[int]=False )->int:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = text.replace(""" """ , """<SP>""" )
__lowerCAmelCase : Optional[int] = text.replace(""" """ , """<SP>""" )
__lowerCAmelCase : Union[str, Any] = text.replace("""\r\n""" , """<BR>""" )
__lowerCAmelCase : Tuple = text.replace("""\n""" , """<BR>""" )
__lowerCAmelCase : List[str] = text.replace("""\r""" , """<BR>""" )
__lowerCAmelCase : Dict = text.replace("""\t""" , """<TAB>""" )
__lowerCAmelCase : Dict = text.replace("""—""" , """ー""" )
__lowerCAmelCase : Tuple = text.replace("""−""" , """ー""" )
for k, v in self.emoji["emoji"].items():
if k in text:
__lowerCAmelCase : Optional[Any] = text.replace(_snake_case , _snake_case )
if clean:
__lowerCAmelCase : List[Any] = self.clean_text(_snake_case )
def check_simbol(_snake_case : List[str] ):
__lowerCAmelCase : Optional[int] = x.encode()
if len(_snake_case ) == 1 and len(_snake_case ) == 2:
__lowerCAmelCase : Optional[Any] = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0xc2a1 and c <= 0xc2bf)
or (c >= 0xc780 and c <= 0xc783)
or (c >= 0xcab9 and c <= 0xcbbf)
or (c >= 0xcc80 and c <= 0xcda2)
):
return True
return False
def checkuae(_snake_case : Union[str, Any] ):
__lowerCAmelCase : Dict = x.encode()
if len(_snake_case ) == 1 and len(_snake_case ) == 3:
__lowerCAmelCase : List[str] = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0xe2_8080 and c <= 0xe2_b07f:
return True
return False
__lowerCAmelCase : Dict = 0
__lowerCAmelCase : Dict = []
while pos < len(_snake_case ):
__lowerCAmelCase : str = min(len(_snake_case ) , pos + self.maxlen + 1 ) if text[pos] == """<""" else pos + 3
__lowerCAmelCase : Tuple = [] # (token_id, token, pos)
for e in range(_snake_case , _snake_case , -1 ):
__lowerCAmelCase : Optional[int] = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(_snake_case ) > 2:
__lowerCAmelCase : Tuple = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(_snake_case ) > 0:
# the smallest token_id is adopted
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : int = sorted(_snake_case , key=lambda _snake_case : x[0] )[0]
result.append(_snake_case )
__lowerCAmelCase : int = e
else:
__lowerCAmelCase : Dict = pos + 1
__lowerCAmelCase : Dict = text[pos:end]
if check_simbol(_snake_case ):
result.append("""<KIGOU>""" )
elif checkuae(_snake_case ):
result.append("""<U2000U2BFF>""" )
else:
for i in wd.encode("""utf-8""" ):
result.append("""<|byte%d|>""" % i )
__lowerCAmelCase : int = end
return result
def UpperCAmelCase__ ( self : List[str] , _snake_case : Optional[int] , _snake_case : List[Any]="\n" )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : List[str] = []
__lowerCAmelCase : Union[str, Any] = []
__lowerCAmelCase : Optional[Any] = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(_snake_case ) > 0:
words.append(bytearray(_snake_case ).decode("""utf-8""" , errors="""replace""" ) )
__lowerCAmelCase : Optional[Any] = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["""emoji_inv"""][word] )
elif word == "<SP>":
words.append(""" """ )
elif word == "<BR>":
words.append(_snake_case )
elif word == "<TAB>":
words.append("""\t""" )
elif word == "<BLOCK>":
words.append("""▀""" )
elif word == "<KIGOU>":
words.append("""ǀ""" )
elif word == "<U2000U2BFF>":
words.append("""‖""" )
else:
words.append(_snake_case )
if len(_snake_case ) > 0:
words.append(bytearray(_snake_case ).decode("""utf-8""" , errors="""replace""" ) )
__lowerCAmelCase : Dict = """""".join(_snake_case )
return text | 232 | 0 |
'''simple docstring'''
from timeit import timeit
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
_a : List[str] =0
while number:
number &= number - 1
result += 1
return result
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
_a : Any =0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def SCREAMING_SNAKE_CASE_ ( ) -> None:
def do_benchmark(_UpperCAmelCase : int ) -> None:
_a : Optional[int] ="""import __main__ as z"""
print(F"Benchmark when {number = }:" )
print(F"{get_set_bits_count_using_modulo_operator(_UpperCAmelCase ) = }" )
_a : Union[str, Any] =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 ) = }" )
_a : Union[str, Any] =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()
| 276 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str
__UpperCamelCase : int
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> list[str]:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase ) )]
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> BWTTransformDict:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter s type must be str.""" )
if not s:
raise ValueError("""The parameter s must not be empty.""" )
_a : List[Any] =all_rotations(_UpperCAmelCase )
rotations.sort() # sort the list of rotations in alphabetically order
# make a string composed of the last char of each rotation
_a : BWTTransformDict ={
"bwt_string": "".join([word[-1] for word in rotations] ),
"idx_original_string": rotations.index(_UpperCAmelCase ),
}
return response
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ,_UpperCAmelCase : int ) -> str:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeError("""The parameter bwt_string type must be str.""" )
if not bwt_string:
raise ValueError("""The parameter bwt_string must not be empty.""" )
try:
_a : List[str] =int(_UpperCAmelCase )
except ValueError:
raise TypeError(
"""The parameter idx_original_string type must be int or passive"""
""" of cast to int.""" )
if idx_original_string < 0:
raise ValueError("""The parameter idx_original_string must not be lower than 0.""" )
if idx_original_string >= len(_UpperCAmelCase ):
raise ValueError(
"""The parameter idx_original_string must be lower than""" """ len(bwt_string).""" )
_a : Optional[int] =[""""""] * len(_UpperCAmelCase )
for _ in range(len(_UpperCAmelCase ) ):
for i in range(len(_UpperCAmelCase ) ):
_a : int =bwt_string[i] + ordered_rotations[i]
ordered_rotations.sort()
return ordered_rotations[idx_original_string]
if __name__ == "__main__":
A__: Any = '''Provide a string that I will generate its BWT transform: '''
A__: Union[str, Any] = input(entry_msg).strip()
A__: Optional[int] = bwt_transform(s)
print(
F"Burrows Wheeler transform for string '{s}' results "
F"in '{result['bwt_string']}'"
)
A__: Union[str, Any] = reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
print(
F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
F"we get original string '{original_string}'"
)
| 276 | 1 |
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()
__a = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__a = []
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 lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : int = state_dict.pop(_lowercase )
UpperCAmelCase_ : Optional[int] = val
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : str = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase_ : List[Any] = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
UpperCAmelCase_ : Optional[Any] = value
else:
UpperCAmelCase_ : Union[str, Any] = value
return new_state_dict
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : int = ''''''
# 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_ : Dict = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Any = 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_ : Union[str, Any] = in_proj_weight[:256, :]
UpperCAmelCase_ : Optional[int] = in_proj_bias[:256]
UpperCAmelCase_ : Tuple = in_proj_weight[256:512, :]
UpperCAmelCase_ : List[Any] = in_proj_bias[256:512]
UpperCAmelCase_ : Union[str, Any] = in_proj_weight[-256:, :]
UpperCAmelCase_ : str = in_proj_bias[-256:]
# 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_ : Any = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase_ : Optional[int] = 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[:256, :]
UpperCAmelCase_ : List[str] = in_proj_bias[:256]
UpperCAmelCase_ : Optional[int] = in_proj_weight[256:512, :]
UpperCAmelCase_ : str = in_proj_bias[256:512]
UpperCAmelCase_ : Optional[Any] = in_proj_weight[-256:, :]
UpperCAmelCase_ : Union[str, Any] = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ : List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase_ : List[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[:256, :]
UpperCAmelCase_ : str = in_proj_bias_cross_attn[:256]
UpperCAmelCase_ : int = in_proj_weight_cross_attn[256:512, :]
UpperCAmelCase_ : Tuple = in_proj_bias_cross_attn[256:512]
UpperCAmelCase_ : List[str] = in_proj_weight_cross_attn[-256:, :]
UpperCAmelCase_ : Dict = in_proj_bias_cross_attn[-256:]
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = image.size
UpperCAmelCase_ : List[Any] = max(_lowercase , _lowercase )
UpperCAmelCase_ : Dict = 800 if '''detection''' in checkpoint_url else 1000
UpperCAmelCase_ : Any = target_max_size / current_max_size
UpperCAmelCase_ : Any = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = F.to_tensor(_lowercase )
UpperCAmelCase_ : Optional[Any] = F.normalize(_lowercase , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
logger.info('''Converting model...''' )
# load original state dict
UpperCAmelCase_ : Any = torch.hub.load_state_dict_from_url(_lowercase , map_location='''cpu''' )
# rename keys
for src, dest in rename_keys:
rename_key(_lowercase , _lowercase , _lowercase )
UpperCAmelCase_ : Optional[int] = rename_backbone_keys(_lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(_lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ : int = '''model.'''
for key in state_dict.copy().keys():
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
UpperCAmelCase_ : int = state_dict.pop(_lowercase )
UpperCAmelCase_ : Dict = 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_ : Any = 15
UpperCAmelCase_ : List[str] = 2
UpperCAmelCase_ : Union[str, Any] = {0: '''table''', 1: '''table rotated'''}
UpperCAmelCase_ : str = idalabel
UpperCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
else:
UpperCAmelCase_ : Optional[Any] = 125
UpperCAmelCase_ : Optional[Any] = 6
UpperCAmelCase_ : Dict = {
0: '''table''',
1: '''table column''',
2: '''table row''',
3: '''table column header''',
4: '''table projected row header''',
5: '''table spanning cell''',
}
UpperCAmelCase_ : Optional[Any] = idalabel
UpperCAmelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
UpperCAmelCase_ : Union[str, Any] = DetrImageProcessor(
format='''coco_detection''' , max_size=800 if '''detection''' in checkpoint_url else 1000 )
UpperCAmelCase_ : Union[str, Any] = TableTransformerForObjectDetection(_lowercase )
model.load_state_dict(_lowercase )
model.eval()
# verify our conversion
UpperCAmelCase_ : str = '''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=_lowercase )
UpperCAmelCase_ : Dict = Image.open(_lowercase ).convert('''RGB''' )
UpperCAmelCase_ : Any = normalize(resize(_lowercase , _lowercase ) ).unsqueeze(0 )
UpperCAmelCase_ : Dict = model(_lowercase )
if "detection" in checkpoint_url:
UpperCAmelCase_ : Any = (1, 15, 3)
UpperCAmelCase_ : Optional[int] = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
UpperCAmelCase_ : Any = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
UpperCAmelCase_ : Any = (1, 125, 7)
UpperCAmelCase_ : Any = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
UpperCAmelCase_ : str = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , _lowercase , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , _lowercase , 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(_lowercase ).mkdir(exist_ok=_lowercase )
model.save_pretrained(_lowercase )
image_processor.save_pretrained(_lowercase )
if push_to_hub:
# Push model to HF hub
logger.info('''Pushing model to the hub...''' )
UpperCAmelCase_ : List[Any] = (
'''microsoft/table-transformer-detection'''
if '''detection''' in checkpoint_url
else '''microsoft/table-transformer-structure-recognition'''
)
model.push_to_hub(_lowercase )
image_processor.push_to_hub(_lowercase )
if __name__ == "__main__":
__a = 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.'
)
__a = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub) | 370 |
from ...processing_utils import ProcessorMixin
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''SpeechT5FeatureExtractor'''
lowerCAmelCase = '''SpeechT5Tokenizer'''
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def __call__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ : List[str] = kwargs.pop('''audio''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = kwargs.pop('''text''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = kwargs.pop('''text_target''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = kwargs.pop('''audio_target''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Tuple = kwargs.pop('''sampling_rate''' ,_SCREAMING_SNAKE_CASE )
if audio is not None and text is not None:
raise ValueError(
'''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?''' )
if audio_target is not None and text_target is not None:
raise ValueError(
'''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?''' )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
'''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.''' )
if audio is not None:
UpperCAmelCase_ : Optional[Any] = self.feature_extractor(_SCREAMING_SNAKE_CASE ,*_SCREAMING_SNAKE_CASE ,sampling_rate=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
elif text is not None:
UpperCAmelCase_ : Dict = self.tokenizer(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : List[str] = None
if audio_target is not None:
UpperCAmelCase_ : List[Any] = self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE ,*_SCREAMING_SNAKE_CASE ,sampling_rate=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : int = targets['''input_values''']
elif text_target is not None:
UpperCAmelCase_ : Optional[int] = self.tokenizer(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Optional[Any] = targets['''input_ids''']
else:
UpperCAmelCase_ : Tuple = None
if inputs is None:
return targets
if targets is not None:
UpperCAmelCase_ : Dict = labels
UpperCAmelCase_ : Optional[int] = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
UpperCAmelCase_ : List[Any] = decoder_attention_mask
return inputs
def a__ ( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> Tuple:
UpperCAmelCase_ : Dict = kwargs.pop('''input_values''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Union[str, Any] = kwargs.pop('''input_ids''' ,_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = kwargs.pop('''labels''' ,_SCREAMING_SNAKE_CASE )
if input_values is not None and input_ids is not None:
raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.''' )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
'''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.''' )
if input_values is not None:
UpperCAmelCase_ : Tuple = self.feature_extractor.pad(_SCREAMING_SNAKE_CASE ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
elif input_ids is not None:
UpperCAmelCase_ : Tuple = self.tokenizer.pad(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase_ : List[str] = None
if labels is not None:
if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) and "input_ids" in labels[0]):
UpperCAmelCase_ : Tuple = self.tokenizer.pad(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = targets['''input_ids''']
else:
UpperCAmelCase_ : int = self.feature_extractor.feature_size
UpperCAmelCase_ : List[str] = self.feature_extractor.num_mel_bins
UpperCAmelCase_ : Optional[Any] = self.feature_extractor.pad(_SCREAMING_SNAKE_CASE ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[Any] = feature_size_hack
UpperCAmelCase_ : List[Any] = targets['''input_values''']
else:
UpperCAmelCase_ : Optional[int] = None
if inputs is None:
return targets
if targets is not None:
UpperCAmelCase_ : Optional[int] = labels
UpperCAmelCase_ : Union[str, Any] = targets.get('''attention_mask''' )
if decoder_attention_mask is not None:
UpperCAmelCase_ : List[Any] = decoder_attention_mask
return inputs
def a__ ( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def a__ ( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) | 235 | 0 |
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