code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTa... | 553 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
fr... | 653 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCamelCase__ ( yaml.SafeLoader ):
"""simple docstring"""
def lowerCAmelCase_ ( self : T... | 331 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class A ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : List[str]):
self.test()
def UpperCAmelCase__ ( self : List[Any]):
_lowercas... | 226 |
"""simple docstring"""
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_con... | 103 | 0 |
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class _snake_case ( __SCREAMING_S... | 12 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
def _A (UpperCamelCase : Dict , UpperCamelCase : Tuple ) ->int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def _A () ->None:
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
a... | 157 |
"""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.or... | 103 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple = "isbn/0140328726" ):
lowerCAmelCase = olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes... | 4 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class _UpperCamelCase( __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''facebook/bart-large-mnli'''
__SCREAMING_S... | 47 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import tor... | 170 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 0 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def __lowerCamelCase ( __snake_case : Dict ) -> int:
"""simple docstring"""
if not isinstance(lowerCAmelCase_, lowerCAmelCase_ ):
A__ : Dict =f"Input value of [number={number}]... | 215 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxX... | 103 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class A( unittest.TestCase ):
"""s... | 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docs... | 553 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
import operator as op
__lowerCamelCase : Optional[int] = '''scaler.pt'''
__lowerCamelCase : Tuple = '''pytorch_model'''
__lowerCamelCase : Dict = '''random_states'''
__lowerCamelCase : Dict = '''optimiz... | 653 |
"""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.mode... | 103 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
fr... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
from typing import Any
def __lowerCAmelCase ( __magic_name__ ):
if not input_list:
return []
_lowercase: List[str] = [input_list.count(lowerCAmelCase_ ) for value in input_list]
_lowercase: Any = max(lowerCAmelCase_ ) # Gets the maximum count in the in... | 226 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase__ : Dict = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}
tr... | 12 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'''configuration_owlvit''': [
'''OWLVIT_PRETR... | 157 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( __SCREAMING_SNAKE_CASE ):
snake_case__ = (DDPMScheduler,)
def UpperCamelCase__ ( self , **_snake_case ):
"... | 4 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = ... | 47 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowercase__( A , A , A , A=5 ):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
s... | 170 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case : Any = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenize... | 215 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ :Any = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Bridg... | 355 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 553 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __UpperCAmelCase ( *__magic_name__ )-> Tuple:
"""simple docstring"""
with open(lowerCAmelCase_ ,"r" ) as fh:
fcntl.flock(lower... | 653 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
... | 331 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
def __lowerCAmelCase ( __magic_name__ = 4_0_0_0_0_0_0 ):
_lowercase: str = []
_lowercase , _lowercase: Optional[int] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowerCAmelCase_ )
_lowercase , _lowercase: List[str] = b, a + b... | 226 |
"""simple docstring"""
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_con... | 103 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester... | 12 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
_lowerc... | 157 |
"""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.or... | 103 | 0 |
"""simple docstring"""
__UpperCamelCase : str = '''
# 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... | 4 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_... | 47 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase : List[str] ... | 170 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 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_availab... | 215 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxX... | 103 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
loggin... | 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
class __UpperCamelCase : # Public class to implement a graph
def __init__( self : int , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[bool]] ) -> Any:
lowerCAmelCase :Dict = r... | 553 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_availabl... | 653 |
"""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.mode... | 103 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_ro... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
'asapp/sew-tiny-100k': 'https://huggingface.co... | 226 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # ... | 12 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
import sys
_lowercase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''668966489504452445231617318564... | 157 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 | 0 |
"""simple docstring"""
from math import sqrt
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any = 100_0000 ):
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid... | 4 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ['''torch''', '''scipy''']
def __init__( self : Any , *__lowerCamelCase : List[... | 103 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.t... | 47 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , ... | 103 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 170 |
"""simple docstring"""
import os
from pathlib import Path
def snake_case ( ) -> Tuple:
from torch.utils.cpp_extension import load
_snake_case = Path(lowerCAmelCase_ ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
_snake_case = ... | 103 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__snake_case = ['''torch''', '''scipy''']
def __init__( self : Any , *lowerCAmelCase_ : List[Any] ... | 215 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixi... | 103 | 0 |
"""simple docstring"""
def A_ ( snake_case__ = 10 , snake_case__ = 22 ) -> int:
_UpperCamelCase :Optional[Any] = range(1 , lowerCAmelCase_ )
_UpperCamelCase :int = range(1 , lowerCAmelCase_ )
return sum(
1 for power in po... | 355 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def __UpperCAmelCase ( __lowerCamelCase : ArgumentParser ):
"... | 103 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avai... | 553 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 0 |
'''simple docstring'''
class A_ :
"""simple docstring"""
def __init__( self :Union[str, Any] , lowerCAmelCase__ :int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Any = size
... | 653 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
snake_case = {'''tokenization_herbert''': ['''HerbertTokenizer''']}
try:
if not is_tokenizers_available():
raise Op... | 103 | 0 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCamelCase_ : str = """\
@misc{chen20... | 331 |
"""simple docstring"""
from itertools import product
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> list[int]:
_snake_case = sides_number
_snake_case = max_face_number * dice_number
_snake_case = [0] * (max_total + 1)
_snake... | 103 | 0 |
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
while a != 0:
_lowercase , _lowercase: List[Any] = b % a, a
return b
def __lowerCAmelCase ( __magic_name__ , __magic_name__ ):
if gcd(lowerCAmelCase_ , lowerCAmelCase_ ) != 1:
_low... | 226 |
"""simple docstring"""
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_con... | 103 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepie... | 12 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase ( yaml.SafeLoader ):
def __UpperCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ... | 103 | 0 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_lowercase = logging.get... | 157 |
"""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.or... | 103 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class... | 4 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is trul... | 103 | 0 |
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _UpperCamelCase( __SCREAMING_SNAKE_CASE ):
__SCREAMING_SNAKE_CASE : str = '''openai/whisper-base'''
__SCREAMING_SNAKE_CASE : List[Any]... | 47 |
"""simple docstring"""
from math import sqrt
def snake_case ( lowerCAmelCase_ = 1000000 ) -> int:
_snake_case = 0
_snake_case = 0
_snake_case = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2... | 103 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowerCamelCase : List[str] = logging.get_logger(__name__)
class snak... | 170 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ... | 103 | 0 |
'''simple docstring'''
__snake_case : Optional[Any] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import ... | 215 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxX... | 103 | 0 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase__ :Union[str, Any] = HfApi()
UpperCamelCase__ :str = {}
# fmt: off
UpperCamelCase__ :Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, ... | 355 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torcha... | 103 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __UpperCamelCase :
def __init__( self : Union[str, Any] , UpperCAmelCase : Collection[float] | None = None ) -... | 553 |
"""simple docstring"""
import operator
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None ) -> list:
_snake_case = operator.lt if reverse else operator.gt
_snake_case = solution or []
if not arr:
retu... | 103 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = None ,__magic_name__ = False ,)-> tuple[int, float, str]:
"""simple docstring"""
snake_case_ : Any = ... | 653 |
"""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.mode... | 103 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
f... | 331 |
"""simple docstring"""
from copy import deepcopy
class UpperCAmelCase :
def __init__( self : Optional[Any] , __lowerCamelCase : list[int] | None = None , __lowerCamelCase : int | None = None ):
"""simple docstring"""
... | 103 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available(... | 226 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 103 | 0 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils impor... | 12 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 103 | 0 |
"""simple docstring"""
from functools import lru_cache
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> set:
"""simple docstring"""
A__ = 2
A__ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //... | 104 |
"""simple docstring"""
from statistics import mean
import numpy as np
def _lowerCamelCase ( UpperCAmelCase_ : list, UpperCAmelCase_ : list, UpperCAmelCase_ : list, UpperCAmelCase_ : int ) -> list:
"""simple docstring"""
A_... | 104 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : Tuple = "SpeechT5FeatureExtractor"
A__ : List[Any] = "SpeechT5Tokenizer"
... | 104 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-... | 104 | 1 |
"""simple docstring"""
import random
def _lowerCamelCase ( UpperCAmelCase_ : int, UpperCAmelCase_ : float, UpperCAmelCase_ : bool = False ) -> dict:
"""simple docstring"""
A__ = {i: [] for i in range(UpperCAmelCase_ ... | 104 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : bytes ) -> str:
"""simple docstring"""
return "".join([hex(UpperCAmelCase_ )[2:].zfill(2 ).upper() for byte in list(UpperCAmelCase_ )] )
def _lowerCamelCase ... | 104 |
"""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... | 104 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
... | 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_a... | 104 | 1 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransf... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : bytes ) -> str:
"""simple docstring"""
return "".join([hex(UpperCAmelCase_ )[2:].zfill(2 ).upper() for byte in list(UpperCAmelCase_ )] )
def _lowerCamelCase ... | 104 | 1 |
"""simple docstring"""
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 im... | 104 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_uti... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : str, UpperCAmelCase_ : str ) -> bool:
"""simple docstring"""
A__ = len(UpperCAmelCase_ )
A__ = len(UpperCAmelCase_ )
A__ = ... | 104 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMix... | 104 | 1 |
"""simple docstring"""
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ = "" , SCREAMING_SNAKE_CASE__ = False ) -> None:
# Mapping from the first character of the prefix of the node
A__ = ... | 104 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( UpperCAmelCase_ : float, UpperCAmelCase_ : float, UpperCAmelCase_ : float, ) -> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc)... | 104 | 1 |
"""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__)... | 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"""configuration_perceiver""": ["""PE... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"""configuration_perceiver""": ["""PE... | 104 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_ef... | 104 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _lowerCamelCase ( UpperCAmelCase_ : str, UpperCAmelCase_ : complex, UpperCAmelCase_ : str = "x", UpperCAmelCase_ : float = 10**-10, UpperCAme... | 104 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 104 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torc... | 104 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCamelCase = {"""vocab_file""": """vocab.txt"... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(UpperCAmelCase_ ) )
if txt[a].isalpha()
]
if... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
assert (
isinstance(UpperCAmelCase_, UpperCAmelCase_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, yo... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int = 1000000 ) -> int:
"""simple docstring"""
A__ = set(range(3, UpperCAmelCase_, 2 ) )
primes.add(2 )
for p in range(3, UpperCAmelCase_, 2 ):
if... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise Value... | 104 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCamelCase = {"""vocab_file""": """vocab.txt"... | 104 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
f... | 104 | 1 |
"""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.... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : Optional[Any] ) -> Dict:
"""simple docstring"""
A__ = []
A__ = set({"(", "[", "{"} )
A__ = set({")", "]", "}"} )
A__ ... | 104 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtracti... | 104 |
"""simple docstring"""
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
... | 104 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, req... | 104 |
"""simple docstring"""
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ = "" , SCREAMING_SNAKE_CASE__ = False ) -> None:
# Mapping from the first character of the prefix of the node
A__ = ... | 104 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import S... | 104 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstri... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_availab... | 104 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
d... | 104 | 1 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1... | 104 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeach... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """Meg... | 104 |
"""simple docstring"""
from statistics import mean
import numpy as np
def _lowerCamelCase ( UpperCAmelCase_ : list, UpperCAmelCase_ : list, UpperCAmelCase_ : list, UpperCAmelCase_ : int ) -> list:
"""simple docstring"""
A_... | 104 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcesso... | 104 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-... | 104 | 1 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import Iterab... | 104 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__na... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int = 50 ) -> int:
"""simple docstring"""
A__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ... | 104 |
"""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... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_A... | 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_a... | 104 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, n... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : bytes ) -> str:
"""simple docstring"""
return "".join([hex(UpperCAmelCase_ )[2:].zfill(2 ).upper() for byte in list(UpperCAmelCase_ )] )
def _lowerCamelCase ... | 104 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = ... | 104 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_uti... | 104 | 1 |
"""simple docstring"""
UpperCamelCase = 8.314_4598
def _lowerCamelCase ( UpperCAmelCase_ : float, UpperCAmelCase_ : float ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less tha... | 104 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMix... | 104 | 1 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _lowerCamelCase ( UpperCAmelCase_ : np.ndarray, UpperCAmelCase_ : int, UpperCAmelCase_ : int ) -> np.ndarray:
"""simple docstring"""
A__ = np.array(... | 104 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( UpperCAmelCase_ : float, UpperCAmelCase_ : float, UpperCAmelCase_ : float, ) -> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc)... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],... | 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"""configuration_perceiver""": ["""PE... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_... | 104 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_ef... | 104 | 1 |
"""simple docstring"""
import math
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> bool:
"""simple docstring"""
return math.sqrt(UpperCAmelCase_ ) * math.sqrt(UpperCAmelCase_ ) == num
def _lowerCamelCase ( UpperCAmel... | 104 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 104 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ... | 104 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
UpperCamelCase = {"""vocab_file""": """vocab.txt"... | 104 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
i... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
assert (
isinstance(UpperCAmelCase_, UpperCAmelCase_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, yo... | 104 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
UpperCamelCase , UpperCamelCase = shutil.get_terminal_size()
UpperCamelCase = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class UpperCamelCase_... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise Value... | 104 | 1 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : Union[str, Any] ) -> Optional[Any]: # noqa: E741
"""simple docstring"""
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ ... | 104 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
f... | 104 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : Optional[Any] ) -> Dict:
"""simple docstring"""
A__ = []
A__ = set({"(", "[", "{"} )
A__ = set({")", "]", "}"} )
A__ ... | 104 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
... | 104 |
"""simple docstring"""
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
... | 104 | 1 |
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