code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import Any
import numpy as np
def _lowerCamelCase ( __A : np.ndarray ) -> bool:
return np.array_equal(__A , matrix.conjugate().T )
def _lowerCamelCase ( __A : np.ndarray , __A : np.ndarray ) -> Any:
_UpperCAm... | 485 |
def _lowerCamelCase ( __A : int ) -> str:
_UpperCAmelCase : Tuple = int(__A )
if decimal in (0, 1): # Exit cases for the recursion
return str(__A )
_UpperCAmelCase , _UpperCAmelCase : int = divmod(__A , 2 )
... | 485 | 1 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sc... | 90 |
from __future__ import annotations
def lowerCAmelCase__ ( lowerCamelCase_ : list[int]):
'''simple docstring'''
return len(set(lowerCamelCase_)) == len(lowerCamelCase_)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 90 | 1 |
'''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 ..u... | 369 |
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, TensorType
__snake_case = logging.ge... | 472 | 0 |
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(__lowercase , x % y )
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return (x * y) // greatest_... | 580 | import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MO... | 580 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)... | 537 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCamelCase_ = logg... | 28 | 0 |
import math
def __lowerCamelCase (UpperCAmelCase__ : int ):
SCREAMING_SNAKE_CASE = [True] * n
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
for i in range(3 , int(n**... | 711 | import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.u... | 647 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( UpperCAmelCase_ ) ->int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in r... | 522 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def lowerCamelCase_ ( *UpperCAmelCase_ ) ->Optional[int]:
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , ... | 522 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available(... | 337 |
import os
import pytest
from attr import dataclass
a ="""us-east-1""" # defaults region
@dataclass
class A_ :
_UpperCAmelCase : str
_UpperCAmelCase : Tuple = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
_UpperCAmelCase : ... | 337 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase__ = TypeVar("T")
def _A( UpperCamelCase__ : int ) -> int:
'''simple docstring'''
return (position - 1) // 2
def _A( UpperCamelCase_... | 332 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ... | 332 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a_... | 703 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
... | 347 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 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 |
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if index == number_of_items:
return 0
lowerCamelCase : Any =0
lowerCamelCase : ... | 262 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logge... | 262 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loade... | 94 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 94 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, F... | 707 |
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 ..pipeline_params import (
TEXT_GUIDED_IMAGE_IN... | 177 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, Pa... | 48 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCondit... | 521 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase_ ( __a ):
def __init__( self , _SCREA... | 700 |
from typing import Any
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : list ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
a_ = [input_list.count(UpperCamelCase ) for value in input_list]
a_ = max(UpperCamelCase ) # Gets the maximum count in the inpu... | 403 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
i... | 272 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_... | 272 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCondit... | 706 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = "▁"
_A = {"vocab_file": "spiece.model"}
_A = {
"vocab_file": {"google/pegasus-xsum": "ht... | 279 | 0 |
'''simple docstring'''
import operator as op
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = lambda __A , __A : int(x / y ) # noqa: E731 integer division operation
_SCREAMING_SNAKE_CASE = ... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 657 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 657 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 41 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__lowerCamelCase : List[str] = """<<<<<<< This should probably be modified because it... | 501 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A__ ( self ) -> List[Any]:
'''simple docstring'''
... | 601 |
from math import sqrt
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''simple docstring'''
lowercase_ = 0
for i in range(1 , int(sqrt(__lowerCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(__lowerCamelCase ):
total += i + n // i
elif i == sqrt(_... | 601 | 1 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
Mus... | 33 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipe... | 138 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPa... | 707 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: float
__magic_name__: TreeNode | None = None
__magic_name__: TreeNode | None = None
def SCREAMING_SNAKE_CASE_... | 534 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase__ ( _A):
"""simple docstring"""
pass
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : int , __lowerCAmelCase : Any ) -> None:
_A ... | 2 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import To... | 176 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_... | 711 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelin... | 573 | 0 |
"""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
a_ = logging.get_logger(__name__)
a_ ... | 76 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a... | 76 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...imag... | 270 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch... | 270 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_lowercase : List[Any] =argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=... | 136 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""camembert-base""": """https://hu... | 451 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
SCREAMING_SNAKE_CASE__ = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
... | 52 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNA... | 52 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinis... | 124 |
def lowerCAmelCase_ ( _snake_case : str , _snake_case : str ) -> float:
'''simple docstring'''
def get_matched_characters(_snake_case : str , _snake_case : str ) -> str:
__magic_name__ : str = []
__magic_name__ : Optional[Any] = min(len... | 124 | 1 |
'''simple docstring'''
def lowercase_ ( lowercase__ ) ->list[int]:
_snake_case: int = len(lowercase__ )
for i in range(lowercase__ ):
for j in range(i + 1 , lowercase__ ):
if numbers[j] < numbers[i]:
... | 273 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A : str = TypeVar('T')
class lowerCamelCase ( Generic[T] ):
_SCREAMING_SNAKE_CASE = 42 # Cache store of keys
_SC... | 273 | 1 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase = logging.get_logger(__name__)
Upp... | 66 |
from __future__ import annotations
import math
def lowercase ( a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :List[Any] = u
for i in range(1 , a ):
SCREAMING_SNAKE_CASE_ :Union[str, Any] = temp * (u - i)
return temp
def lowercase ( ... | 631 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 710 |
'''simple docstring'''
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( UpperCamelCase , unittest.TestCase ):
... | 575 | 0 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCS... | 546 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str = logging.get_logger(__name__)
_A : Tuple = {
"""goog... | 361 | 0 |
from manim import *
class a ( UpperCAmelCase__ ):
def lowerCamelCase__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =Rectangle(height=0.5 , width=0.5 )
SCREAMING_... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
A... | 675 |
import string
import numpy
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
return b if a == 0 else greatest_common_divisor(b % a,snake_case_ )
class lowercase :
_a = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
... | 307 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowerCamelCase_):
def merge(lowerCamelCase_ , lowerCamelCase_) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
yield... | 200 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
... | 200 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLIm... | 356 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 356 | 1 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_A = {
... | 700 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A = """src/transformers"""
# This is to ma... | 507 | 0 |
def __snake_case ( lowerCAmelCase_ = 1_0_0_0_0_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = set(range(3 , lowerCAmelCase_ , 2 ) )
primes.add(2 )
for p in range(3 , lowerCAmelCase_ , 2 ):
if p not in primes:
continue
prim... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.toke... | 453 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__a : List[Any] = logging.get_logger(__name... | 522 | import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from .... | 522 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate im... | 465 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_ = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deform... | 465 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
a__ : List[str] = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapm... | 720 |
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 lowercase ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def _UpperC... | 235 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__lowerCAmelCase = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU:... | 358 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 266 | 0 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _A :
def __init__( self , __lowerCAmelCase... | 197 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] , lowerCAmelCase__ :list[float] ) -> float:
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , l... | 197 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import... | 12 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring'''
def __init__( self :Dic... | 557 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCamelCase =[
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linea... | 702 |
'''simple docstring'''
import random
def snake_case ( a_ : int , a_ : float , a_ : bool = False ) -> dict:
"""simple docstring"""
UpperCamelCase_ : dict = {i: [] for i in range(a_ )}
# if probability is greater or... | 543 | 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, prepare_image_inputs
if is_to... | 34 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 428 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.uti... | 27 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : dict , lowerCamelCase : str):
A_ , A_ : List[Any] = set(lowerCamelCase), [start]
while stack:
A_ : Optional[Any] =... | 27 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 274 | '''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case_... | 274 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_... | 110 |
from math import factorial
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Any ,_SCREAMING_SNAKE_CASE : List[Any] ,_SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
'''simple docstring'''
A = real
if isinstance(... | 110 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 169 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 169 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : Tuple = logging.get_logger(__name__)
_snake_case : Optional[int] ... | 214 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _A ( __snake_case :int ) -> Optional[int]:
"""simple docstring"""
if (
(cp >= 0x4E_00 and cp <= 0x9F_FF)
or (cp >= 0x34_0... | 214 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
... | 146 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( snake_case__: np.ndarray ) -> tuple[np.ndarray, np.ndarray]:
UpperCAmelCase__ , UpperCAmelCase__ = np.shape(snake_case__ )
if rows != columns:
UpperCAmelCase__ = (
... | 146 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A__ ( A__ ):
"""simple docstring"""
_lowercase = 'MCTCTFeatureExtractor'
_lowercase = 'AutoTokenizer'
def __init__( self : Union[str, Any] , ... | 151 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def UpperCamelCase_ ( __a , __a , __a , __a ) -> List[str]:
a__ : str = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не... | 151 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL_PRETRAINED_CON... | 59 |
import operator as op
def lowerCAmelCase_ ( __a ) -> Tuple:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =[]
lowerCamelCase__: Tuple =lambda __a , __a : int(x / y ) # noqa: E731 integer division operation
lowerCamelCase__: T... | 59 | 1 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeli... | 709 | from typing import Any
import numpy as np
def _snake_case ( lowerCAmelCase : np.ndarray ):
"""simple docstring"""
return np.array_equal(lowerCAmelCase , matrix.conjugate().T )
def _snake_case ( lowerCAmelCase : np.ndarray , lowerCAmelCase ... | 316 | 0 |
from __future__ import annotations
import requests
lowerCAmelCase_ = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories crea... | 217 |
from __future__ import annotations
def snake_case( __magic_name__ , __magic_name__ ) -> list[list[int]]:
'''simple docstring'''
lowercase : list[list[int]] = []
lowercase : list[int] = []
lowercase ... | 217 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSa... | 705 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any ... | 233 | 0 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acc... | 682 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivi... | 682 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipeli... | 9 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoPro... | 9 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowercase :
"""simple docstring"""
... | 101 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffuser... | 153 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureE... | 717 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import... | 562 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a__ ( A__, A__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = F'''{sampling_rate}'''
SCREAMING_SNAKE_CASE_ : str = '1'
SCREAMIN... | 101 | '''simple docstring'''
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def snake_case_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any]=False) -> Optional[int]:
lowerCAmelCase_ = ... | 274 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
while a != 0:
_snake_case , _snake_case = b % a, a
return b
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
if gcd(lowerCAmelCase_ ... | 404 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
'''huggingface/time-series-transformer-tourism-mon... | 404 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__SCREAMING_SNAKE_CASE ="\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja... | 425 | """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,
... | 425 | 1 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A = 2_9_9_7_9_2_4_5_8
# Symbols
_A , _A , _A , _A = symbols('ct x y z')
def UpperCAmelCase ( a_ ):
'''simple docstring'''
... | 133 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
... | 133 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase = logging.get_logger(__name__)
class _UpperCAmelCase ( UpperCamelCase_ , UpperCamelCase_ )... | 632 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here t... | 297 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase_ = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLM... | 707 |
import csv
import tweepy
# Twitter API credentials
lowerCAmelCase_ = """"""
lowerCAmelCase_ = """"""
lowerCAmelCase_ = """"""
lowerCAmelCase_ = """"""
def lowerCamelCase_ ( lowerCAmelCase: str )-> None:
# authorize twitter, initialize tweepy
_snake_... | 669 | 0 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class A :
'''simple docstring'''
A__ = None
A__ = False
A__ = False
A__ = False
A__ = ... | 15 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : int, _UpperCAmelCase : str):
UpperCamelCase = ''''''
for i in table:
res += inp[i - 1]
return res
def __snake_case ( _UpperCAmelCase : Dict):
return data[1:] + data[0]... | 212 | 0 |
"""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_configuration_common import... | 703 |
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__a = logging.get_logger(__name__)
class lowerCamelCase :
'''simple docstring'''
_A : Union[str, Any] = None... | 310 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 43 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE = 100 ):
'''simple docstring'''
A_ = 0
A_ = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ ... | 203 | 0 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instea... | 363 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_UpperCamelCase = logging.get_logger(__name__... | 363 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :str ):
__UpperCAmelCase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__UpperCAmelCase = hex_num[0] == '''-'''
if is_negative:
__UpperCAmelCase ... | 49 |
"""simple docstring"""
def lowercase__ ( snake_case_ :dict ):
__UpperCAmelCase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__UpperCAmelCase = set()
return any(
node not in visited and depth_first_search(snake_cas... | 49 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackb... | 169 |
def A (__A : int ) -> bool:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__A )
if number < 0:
return... | 169 | 1 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils i... | 555 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def _UpperCamelCase ( _A ) -> Callable:
"""simple docstring"""
@wraps(_A )
def _inner_fn(*_A , **_A ):
warnings.warn(
(F"""'{fn.__name__}' is experiment... | 555 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokeniz... | 522 | import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = [
'''encoder.version''',
'''decoder.version''',
'... | 522 | 1 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/li... | 356 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCamelCase__ ( a ):
# vision encoder
if "img_encoder.pos_embed" in name:
__snake_case = name.r... | 356 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( ... | 456 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
lowercase_ = TypeVar('''T''')
lowercase_ = Union[List[T], Tuple[T, ...]]
lowercase_ = Union[T, List[T], Dict[str, T]]
lowercase_ = Union[str, bytes, os.PathLike]
| 456 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import... | 603 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Con... | 603 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
snake_case = logging.get_logger(__name__)
snake_case = {"vocab... | 587 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise Optional... | 587 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings... | 376 |
def lowerCamelCase__ ( ):
'''simple docstring'''
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = 1
snake_case_ =... | 376 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 720 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class lowerCAmelCase :
"""simple docstring"""
def __init__( self , UpperCamelCase__ ) -> str:
'''simple docstring'''
lowerCamelCase_ =... | 66 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'''google/umt5... | 24 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 0 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class lowerCamelCase :
'''simple docstring'''
def __init__( self: List[Any] ) -> None:
snake_case_ :Union[str, Any] = [2, 1, 2, -1]
snake_case_ :Optio... | 715 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__a = ""
__a = ""
__a = ""
__a = 1 # (0 is vertical, 1 is horizontal)
def A_ ( ):
'''simple docstring'''
... | 310 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def A ( __UpperCamelCase ) -> Tuple:
A__ = FileLock(str(tmpdir / 'foo.lock' ) )
A__ = FileLock(str(tmpdir / 'foo.lock' ) )
A__ = 0.01
with locka.acq... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
"""simple docstring"""
import functools
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ) or not all(isinstance(__UpperCAmelCase ,__UpperCAmelCase ) for day in days ):
raise ValueErr... | 283 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
raise TypeError('Undefined for non-integers' )
elif precision ... | 283 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
... | 29 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCamelCase__ : int = logging.get_logger(__name__)
class __magic_name__ (snake_case_ ):
'''simple docstring'''
def __init__( s... | 33 | 0 |
from string import ascii_lowercase, ascii_uppercase
def _snake_case (__lowercase):
if not sentence:
return ""
UpperCamelCase_ = dict(zip(__lowercase , __lowercase))
return lower_to_upper.get(sentence[0] , sentence[0]) + sentence[1:]
i... | 618 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
snake_case__ : Optional[int] = pytest.mark.integration
@p... | 618 | 1 |
from math import factorial
UpperCamelCase = {str(digit): factorial(digit) for digit in range(10)}
def __magic_name__ ( SCREAMING_SNAKE_CASE ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('Parameter number m... | 66 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizer... | 66 | 1 |
"""simple docstring"""
def _a ( UpperCAmelCase__ ) -> Union[str, Any]:
if not head:
return True
# split the list to two parts
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = head.next, head
while fast and fast.next:
__SCREAM... | 702 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cach... | 690 | 0 |
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 snake_case ( UpperCamel... | 85 |
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_utils import require_vision
from transformers.utils import... | 327 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_A : str = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIV... | 330 | '''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve... | 330 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/reso... | 363 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( _snake_case ... | 467 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCamelCase__ = lo... | 439 |
"""simple docstring"""
def UpperCAmelCase ( snake_case : int , snake_case : int ):
return x if y == 0 else greatest_common_divisor(snake_case , x % y )
def UpperCAmelCase ( snake_case : int , snake_case : int ):
... | 439 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _Up... | 60 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __lowerCAmelCase ( _a ):
lowerCamelCase_ : Optional[Any] = '''encoder-decoder'''
lowerCamelCase_ : Optional[Any] = True
... | 60 | 1 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCST... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
""... | 425 | 0 |
'''simple docstring'''
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
lowerCamelCase : str = lo... | 460 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils... | 460 | 1 |
from math import factorial
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < ... | 720 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_:Optional[int] = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", ""... | 520 | 0 |
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