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 requests
from bsa import BeautifulSoup
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : dict ) ->List[Any]:
'''simple docstring'''
a : List[Any] = BeautifulSoup(... | 633 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ... | 101 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
'''simple docstring'''
def snake_case ( a_ : str ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ : Dict = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
Upper... | 208 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 0 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
... | 464 | 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 ...tokenizat... | 666 | 0 |
"""simple docstring"""
from __future__ import annotations
class __magic_name__ :
def __init__( self : Union[str, Any] , snake_case_ : int = 0 ):
__snake_case = key
def lowerCAmelCase ( self : Optional[int] , snake_ca... | 163 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : str = len(lowerCAmelCase_)
A_ : Optional[int] = len(lowerCAmelCase_)
A_ : Any = [[False for _ in range(m + 1)] for _ in range(n + 1)]
... | 665 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
# ===== initialization ====... | 407 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
from __future__ import annotations
import bisect
def __magic_name__ ( __lowerCAmelCase : list[int] , __lowerCAmelCase : int , __lowerCAmelCase : int = 0 , __lowerCAmelCase : int = -1 ) -> Optional[int]:
if hi < 0:
__lowerCamelCase ... | 298 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCamelCase : Optional[int] = logging.getLo... | 149 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 17 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVec... | 633 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Optional[Any] =logging.get_logger(__name__)
lowerCA... | 101 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase ={
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"]... | 208 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num... | 464 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __magic_name__ ( _UpperCamelCase ):
def __init__( self : Tuple , *sn... | 163 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 0 |
"""simple docstring"""
def snake_case ( _a: int , _a: Tuple )-> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 0 |
'''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 ... | 665 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def ... | 407 | 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_roberta import RobertaTokenizer... | 666 | 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 impor... | 298 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 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_roberta import RobertaTokenizer
... | 149 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 0 |
class lowerCamelCase_ :
def __init__( self : Dict , __A : str , __A : Tuple , __A : Union[str, Any] ):
__A : Union[str, Any] = None
__A : Tuple = None
__A : Optional[int] = graph
self._normalize... | 17 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _SCREAMING_SNAKE_CASE ( _lowercase : Dict , _lowercase : Dict ) ->Union[str, Any]:
'''simple docstring'''
a : ... | 633 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAmelCase__ : Tuple =logging.get_logg... | 101 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 208 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 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
lowerCam... | 464 | 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 ...tokenizat... | 666 | 0 |
"""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
if is_torch_available():
import ... | 163 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compos... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowerCamelCase ( *lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Union[Dict, Any]] = None , lowerCamelCase : str=True , lowerCam... | 665 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image... | 407 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
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... | 298 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCAmelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def lowerCAmelCase ... | 149 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_si... | 17 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, T... | 633 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 0 |
import torch
from transformers import AutoModel
class __lowercase (torch.nn.Module ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 101 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase ={
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
... | 208 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class A ( _Uppe... | 464 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
__snake_case = u
for i in range(1 , lowerCAmelCase_ ):
... | 163 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 0 |
"""simple docstring"""
def snake_case ( _a: int )-> str:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError('Input value must be an \'int\' type' )
lowerCamelCase__ = 0
while number:
... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_ba... | 665 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A =logging.get_logger(__name__)... | 407 | 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_roberta import RobertaTokenizer... | 666 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline... | 298 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils... | 149 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> Optional[int]:
__A : Any = []
__A : List[str] = 2
__A : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
__A : str = [True] * (end + 1)
__A : Any ... | 17 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 0 |
"""simple docstring"""
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
fro... | 633 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files', [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json... | 101 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
'''simple docstring'''
UpperCamelCase ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/... | 208 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 0 |
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, nested_simplify, require_tf, require_torch, slow
fro... | 464 | 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 ...tokenizat... | 666 | 0 |
"""simple docstring"""
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __magic_name__ ( _UpperCamelCase ... | 163 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot": [
... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:... | 665 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 407 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
# 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 applicab... | 298 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowerCamelCase : Tuple = '''\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Ale... | 149 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
import warnings
warnings.warn(
'''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
'''`from accelerate import find_executable_batch_size` to avoid this warning.''',
FutureWarning,
)
| 17 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 0 |
"""simple docstring"""
import mpmath # for roots of unity
import numpy as np
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None ) -> str:
a : Optional[Any] = list(poly_a or [0] )[:]
... | 633 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] =argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=s... | 101 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase =logging.get_logger("transformers.models.speecht5")
def snake_case ( a_ : Tuple , a_ ... | 208 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
from __future__ import annotations
def a_ ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] =get_failure_array(lowerCAmelCase_ )
# 2) Step through text searching for pattern... | 464 | import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( _Upp... | 666 | 0 |
"""simple docstring"""
from math import isqrt
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
__snake_case = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 163 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorTy... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : list):
_enforce_args(lowerCAmelCase_ , lowerCAmelCase_)
if n == 0:
return 0
A_ : List[str] = float("""-inf""")
for i in range(1 , n + 1):
... | 665 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
... | 407 | 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_roberta import RobertaTokenizer... | 666 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 298 | from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRo... | 666 | 0 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowerCamelCase : Dict = TypeVar('''T''')
class lowerCAmelCase ( Generic[T] ):
'''simpl... | 149 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 17 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->str:
'''simple docstring'''
assert isinstance(lowerCAmelCase_ , lowerCAmelCase_ ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
re... | 633 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a__ ( A__ ): # picklable for multiprocessing
return i + 1... | 101 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nest... | 208 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class A ( _UpperCamelCase , u... | 464 | 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 ...tokenizat... | 666 | 0 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings... | 163 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 0 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def snake_case ( _a: dict )->... | 510 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__magic_name__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuild... | 665 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class _snake_case ( _UpperCamelCa... | 407 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer,... | 298 | from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 149 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : list[int] ) -> Dict: # This function is recursive
__A : int = len(lowerCAmelCase_ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <... | 17 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _UpperCamelCase ):
lowerCamelCase : Optional[int] =(KDPMaDiscreteS... | 633 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
class __lowercase (_UpperCamelCase ):
"""simple docstring"""
def __init__( ... | 101 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase ={
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalD... | 208 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class _lowerCAmelCase ( tf.keras.optimizers.sch... | 667 |
'''simple docstring'''
import math
from collections.abc import Callable
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : float = xa
A_ : float = xa
while True:
if x_n == x_na or function(lowerCamel... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCamelCase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''')... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase :List[str] = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAIN... | 667 |
'''simple docstring'''
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase , lowercase ):
A_ : List[str] = name
A_ : Dict = value
A_ : Optional[int] = weight
def __repr__(self ):
return F'{self.__class__.__name__}({self.na... | 667 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : List[str] = ['torch']
def __init__(self , *lowercase , **lowercase ):
requires_backends(self , ["""t... | 667 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowerCamelCase :int = logging.getLogger(__name__)
lowerCa... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :List[str] = {
'''configuration_spee... | 667 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 667 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 667 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueE... | 667 | 1 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
lowerCamelCase :Optional[int] = logging.getLogger(__name__)
lowerCamelCase :List[str] = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-... | 667 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 667 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 667 |
'''simple docstring'''
print((lambda quine: quine % quine)('''print((lambda quine: quine %% quine)(%r))''')) | 667 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vi... | 667 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as... | 667 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availabl... | 667 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
lowerCamelCase :Dict = get_logger(__name__)
class _lowerCAmelCase :
def __init__(self , lowercase , lowercase=None ):
A_ : Optional[int] = attrs or []
if m... | 667 | 1 |
'''simple docstring'''
from collections import defaultdict
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Any = first_str.lower().strip()
A_ : Union[str, Any] = second_str.lower().strip()
# Remove whitespace
A_ :... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase :int = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONF... | 667 | 1 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import l... | 667 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cla... | 667 | 1 |
'''simple docstring'''
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[str] = logging.get_logger(__name__)
lowerCa... | 667 |
'''simple docstring'''
import math
lowerCamelCase :int = 1_0
lowerCamelCase :List[Any] = 7
lowerCamelCase :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def a ( lowerCamelCase__ = 20 ):
'''simple docstring'''
A_ : ... | 667 | 1 |
'''simple docstring'''
from math import loga
def a ( lowerCamelCase__ ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise TypeError("""Input ... | 667 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[Any] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''google/pix2struct-tex... | 667 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCamelCase :Optional[Any] = (3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase :str = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class ... | 667 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
lowerCamelCase :Union[str, Any] = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFO... | 667 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase :Dict = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5... | 667 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCa... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(lowerCamelCase__ ):
for j in range(lowerCamelCase__ ):
if dist... | 667 |
'''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():
... | 667 | 1 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :Optional[int] = logging.get_logger(__name__)
lowerCamelCase :Union[str, Any] = {
'''facebook/data2vec-base-960h''': '''https://huggingfa... | 667 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Union[str, Any] = int(np.ceil((x_end - xa) / step_s... | 667 | 1 |
'''simple docstring'''
import math
import os
import sys
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : int = """"""
try:
with open(lowerCamelCase__ , """rb""" ) as binary_file:
A_ : List[str] = binary_file.read()
for... | 667 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_... | 667 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
_validate_point(lowerCamelCase__ )
_validate_point(lowerCamelCase__ )
if len(lowerCamelCase__ ) != len(lowerCamelCase__ ):
raise ValueError("""Both poi... | 667 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowerCamel... | 667 | 1 |
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