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 |
|---|---|---|---|---|
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__lowercase : Any =... | 54 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_proces... | 466 | 0 |
import numpy as np
lowerCAmelCase__ = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
class... | 81 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
fr... | 81 | 1 |
from torch import nn
def lowercase_ (A : Union[str, Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Un... | 478 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 478 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
... | 721 |
"""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... | 401 | 0 |
"""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 ..pipeline_pa... | 530 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowercase ):
UpperCAmelCase__ = (UnCLIPScheduler,)
def _lowercase (self , **SCREAMING_SNAKE_CASE_ ):
""... | 626 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCAmelCase_ ( ):
'''simple docstring'''
_a : List[str] = ArgumentParser(
... | 424 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def UpperCAmelCase_ ( A , A , A ):
'''simple docstring'''
return round(float((moles * 0.08_21 * temperature) / (volu... | 424 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, requir... | 20 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : int = 50_000_000 ):
'''simple docstring'''
lowercase__ = set()
lowercase__ = int((limit - 24) ** (1 / 2) )
lowercase__ = set(range(3 ,prime_square_... | 267 | 0 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class _UpperCamelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase=1 , __l... | 713 |
"""simple docstring"""
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_accele... | 422 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple ... | 414 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timest... | 414 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.versi... | 706 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def UpperCA... | 553 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, 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, rand... | 193 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_... | 193 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 720 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 0 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __snake_case ( SCREAMING_SNAKE_CASE: Dict ):
"""simple docstring"""
return choice(SCREAMING_SNAKE_CASE )
def __snake_case ( SCRE... | 580 |
"""simple docstring"""
import sys
_snake_case = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 580 | 1 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 473 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 10**-10 ) -> float:
'''simple docstring'''
__s... | 473 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowerCamelCase :
"""simple docstring"""
snake_case = 42
snake_case = None
snake_case = None
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CAS... | 590 |
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, Fla... | 590 | 1 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from trans... | 710 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_u... | 472 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransform... | 480 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowercase :
"""simple docstring"""
_A : float
_A : TreeNode | None = None
_A : TreeNode | None = None
def SCREA... | 480 | 1 |
def _snake_case ( __snake_case , __snake_case , __snake_case ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __snake_case ) * a) % mod
else:
_UpperCamelCase = binary_exponentiation(__s... | 706 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_to... | 71 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
UpperCamelCase__ = list[tuple[int, int]]
UpperCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0... | 75 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :int = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def UpperCAmelCase ( a_ = "mumbai" ) -> Generator[tuple[str, str], N... | 55 | 0 |
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Con... | 711 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'bert-base-uncased': 'https://hugging... | 80 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
fr... | 85 |
from __future__ import annotations
import requests
UpperCamelCase_ = 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 c... | 625 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ : Union[str, Any] = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVis... | 709 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ : List[str] = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['M... | 107 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def snake_case (__lowercase ) -> Union[str, Any]:
'''simple docstring'''
_snake_case : int = test_file.spli... | 670 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 332 | 0 |
"""simple docstring"""
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 PreTrainedTokenizer
from ...utils import logging
a : Optional[Any] = logging.get_logger(_... | 19 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _UpperCamelCase ( _A , _A , _A , _A , _A ) -> np.array:
"""simple docstring"""
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_U... | 19 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""distilbert-base-uncased""": """https://huggingface.co... | 413 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCamelCase = ""
__lowerCamelCase = ""
__lowerCamelCase = ""
__lowerCamelCase = 1 # (0 is vertical, 1 is horizontal)
def lowercase ( ) -> N... | 490 | 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,
CTR... | 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, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.ka... | 493 | 0 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...te... | 243 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowercase__ : str = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Sa... | 98 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( UpperCAmelCase_ , UpperCAmelCase_ ) ->int:
"""simple docstring"""
while a != 0:
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = b % a, a
return b
def... | 374 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 374 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : List[Any] = namedtuple("""result""" , """name value""" )
if (... | 414 |
'''simple docstring'''
from __future__ import annotations
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : List[Any] = len(lowerCAmelCase_ )
# We need to create solution object to save path.
_UpperCAmelCase : Optional[int] = [[0 for _ in... | 414 | 1 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__lowercase = '''scheduler_config.json'''
class _lowerc... | 701 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowercase = 299_792_458
# Symbols
__lowercase , __lowercase , __lowercase , __lowercase = symbols('''ct x y z''')
def low... | 296 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=SCREAMING_SNAKE_CASE__ ):
snake_case__ :int = ['onnx']
def __init__( self : Dict , *__magic_name__ : Optional[Any] , **__magic_name__ : Optional[A... | 48 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase__ : List[Any] = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
i... | 48 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def __lowercase ( _UpperCAmelCase ) -> typing.Counter[int]:
'''simple docstring'''
__lowercase = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(_UpperCAmelCase , ... | 711 | from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class snake_case ( __snake_case ):
"""simple docstring"""
... | 576 | 0 |
class UpperCAmelCase :
def __init__( self : List[Any] , lowerCAmelCase : list[int] ):
lowercase : Union[str, Any] = len(lowerCAmelCase )
lowercase : int = [0] * len_array
if len_array > 0:
... | 583 |
from math import factorial, pi
def lowerCamelCase_ ( UpperCAmelCase_ : float , UpperCAmelCase_ : int = 30 ):
if not isinstance(UpperCAmelCase_ , (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for thet... | 583 | 1 |
import os
from math import logaa
def _lowerCAmelCase ( __magic_name__ :str = "base_exp.txt" ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(__magic_name__ ) , __magic_name__ ) ) ):
... | 407 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 407 | 1 |
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 __lowercase :
"""simple docstring"""
def __init__( ... | 542 |
def snake_case__ ( lowerCamelCase_ = 1000 ):
return sum(e for e in range(3 , lowerCamelCase_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"{solution() = }")
| 542 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effecti... | 401 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''shi-labs/n... | 401 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Optional[Any] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ... | 80 | 1 |
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , __UpperCamelCase : int ):
_UpperCAmelCase = order
# a_{0} ... a_{k}
_UpperCAmelCase = [1.0] + [0.0] * order
# b_{0} ... b_{k}
_UpperC... | 719 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start... | 129 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
UpperCAmelCase = None
def lowercase ( ) -> List[Any]:
_UpperCamelCase = argpa... | 420 | """simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
UpperCAmelCase = 6_37_81_37.0
UpperCAmelCase = 6_35_67_52.31_42_45
UpperCAmelCase = 6_378_137
def lowercase ( a__ : float , a__ : float , a__ : float , ... | 420 | 1 |
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if not (isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(_UpperCAmelCase , _UpperCAmelCase )):
raise ValueError('longest_common_substring() takes tw... | 314 |
import re
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans(... | 314 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 75 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : List[Any] =get_tests_dir('fixtures/test_sent... | 647 | 0 |
"""simple docstring"""
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import lo... | 701 | """simple docstring"""
from string import ascii_uppercase
lowercase__ = {str(ord(c) - 55): c for c in ascii_uppercase}
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('int() can\'t c... | 217 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from... | 697 |
def __A ( ) -> list[list[int]]:
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__UpperCamelCase : str = generate_large_matrix()
__UpperCamelCase : Tuple = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -... | 468 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling... | 706 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 0 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeni... | 636 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils i... | 241 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_comm... | 718 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 346 | 0 |
'''simple docstring'''
import sys
from collections import defaultdict
class a__:
'''simple docstring'''
def __init__( self):
"""simple docstring"""
lowerCAmelCase = []
def a_ ( self , __lowerCAmelCase):
"""simple docstring"""
re... | 370 | '''simple docstring'''
from __future__ import annotations
import math
def snake_case__ ( _A: int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowerCAmelCase = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(_A )
low... | 370 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoMo... | 715 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
lowerCAmelCase : Tuple = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return r... | 693 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class A ( SCREAMING_SNAKE_CASE__ ):
# `task` is not a ClassVar since we want it to b... | 48 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import Au... | 318 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ : List[str] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfi... | 446 |
def __lowercase ( _A ) -> list[int]:
SCREAMING_SNAKE_CASE : int = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = 0, 0
for i in range(1... | 446 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
... | 5 |
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = [0 for i in range(len(snake_case ) )]
# initialize interval's left pointer and right pointer
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Optional[int] = 0, 0
... | 74 | 0 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
Def... | 705 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import M... | 518 | 0 |
def a__ ( A__ ):
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1, len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
SCREAMING_SNAKE_CASE_ : List[Any] = grid[0]... | 101 |
'''simple docstring'''
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_ver... | 51 | 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_... | 700 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase__ : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place fr... | 18 | 0 |
from __future__ import annotations
import math
def A ( _lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0,... | 500 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 500 | 1 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
if not is_accelerate_availa... | 701 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class A_ :
"""simple docstring"""
def __init__( self :Dict ) -> List[str]:
'''simple docstring'''
... | 656 | 0 |
A_ : List[str] = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ... | 57 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__low... | 655 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenizati... | 218 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=a_ )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : ... | 218 | 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 load_me... | 48 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _a ... | 85 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__ ( lowerCamelCase__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def snake_case ( __A : Any ):
"""simple docstring"""
raise NotI... | 700 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 602 | 0 |
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_EN... | 122 |
import unittest
import numpy as np
from datasets import load_dataset
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 ... | 122 | 1 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
... | 100 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase : List[Any] = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperC... | 100 | 1 |
import os
import numpy
import onnx
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = a.name
snake_case = b.name
snake_case = ''''''
snake_case ... | 550 |
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_inpu... | 550 | 1 |
'''simple docstring'''
from PIL import Image
def _snake_case ( lowercase , lowercase ) -> Image:
__a : List[Any] = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowercase ) -> int:
return int(1_2_8 + factor * (c - 1_2_8... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
from collections.abc import Sequence
def a ( A__ , A__ = False ) -> Union[str, Any]:
'''simple docstring'''
if not arr:
return 0
SCREAMING_SNAKE_CASE__ : Optional[int] = 0 if allow_empty_subarrays else float('''-inf''' )
S... | 35 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCAmelCase ) )
def A__( __lowerCAmelCase , __lowerCAmelCase , ... | 304 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _a ( ... | 705 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, req... | 220 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class UpperCAmelCase_ ( A ):
'''simple docstring'''
def __init__( self : int , snake_case__ : str , snake_case__ : List[Any] ):
'''simple docstring'''
super().__init__()... | 199 |
'''simple docstring'''
def snake_case_ ( lowercase__ ):
if len(lowercase__ ) <= 1:
return [tuple(lowercase__ )]
UpperCAmelCase__ : Union[str, Any] = []
def generate(lowercase__ , lowercase__ ):
UpperCAmelCase__ : str = [0] * n
... | 199 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = ["flax", "transformers"]
def __init__( self ,*UpperCAmelCase_ ,**UpperCAmelCase_ ):
requires... | 713 |
"""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, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import ... | 600 | 0 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _snake_case ( A ) -> np.ndarray:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 * r + 0.5... | 90 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_b... | 542 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: str = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
... | 713 |
'''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( a : int = 4 ) -> list[list[int]]:
"""simple docstring"""
lowercase_ : Tuple = abs(a ) or 4
return [[1 + x + y * row_size for x in range(a )] for y in range(a ... | 7 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowerCAmelCase_ ( unittes... | 45 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, 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,
... | 18 | 0 |
import os
def __magic_name__ ( ) -> Tuple:
with open(os.path.dirname(lowercase__ ) + '/p022_names.txt' ) as file:
_lowercase : List[Any] = str(file.readlines()[0] )
_lowercase : List[Any] =... | 702 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartToke... | 677 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
),
# See all Vivit ... | 30 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowercase_ = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5... | 552 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,11... | 713 |
'''simple docstring'''
from torch import nn
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase ... | 162 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not... | 23 |
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 snake_case__ ( UpperCamelCase_ ):
def __init__( self : int , _lowerCamel... | 170 | 0 |
'''simple docstring'''
def __a ( __lowerCamelCase : list[int] , __lowerCamelCase : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(__lowerCamelCase ) == len(__lowerCamelCase ) == 3:
raise ValueError("Please enter a valid equation." )
... | 461 | '''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 461 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):... | 441 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import to... | 441 | 1 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
fr... | 263 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .... | 263 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a ( A__ , A__=1 ) -> int:
'''simple docstring'''
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_s... | 35 |
# Function to print upper half of diamond (pyramid)
def a ( a ) ->Optional[Any]:
'''simple docstring'''
for i in range(0 , a ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 , i + 1 ): # printing ... | 201 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowercase ( __a ):
"""simple docstring"""
lowercase__ = ['''image_processor''', '''tokenizer''']
l... | 296 | """simple docstring"""
import sys
__lowercase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 296 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from t... | 79 |
'''simple docstring'''
def _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
_UpperCamelCase =[redshift, radiation_density, matter_density, dark_energy]
if any(p < 0... | 404 | 0 |
'''simple docstring'''
import os
import sys
import unittest
lowercase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_objec... | 276 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"YituTech/conv-bert-base": "https... | 276 | 1 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : Union[str, Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Transla... | 310 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCAmelCase__ : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase__ : Optional[Any] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class ... | 313 | 0 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaS... | 668 | """simple docstring"""
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class a__ ( _a ):
def __init__( self, _UpperCAmelCase, ... | 668 | 1 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : str , __lowerCamelCase : Optional[int] ) -> int:
if len(_lowercase ) != len(_lowercase ):
raise ValueError('''The length of profit and weight must be same.''' ... | 224 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase = logging.get_logger(__name__)
class _a ( lowerCAmelCase__ ):
'''simple docstring'''
... | 520 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_senten... | 705 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase (metaclass=__snake_case ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["""flax""", """transformers"""]
def __init__( self :List[str] , *__magic_name__ :int ... | 348 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCamelCase__ = Mapping[str, np.ndarray]
lowerCamelCase__ = Mapping[str, Any] # Is a nested dict.
lowerCame... | 524 | import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers imp... | 524 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import t... | 708 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ) -> None:
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print(... | 77 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( _a ):
if not nums:
return 0
snake_case_ : List[str] = nums[0]
snake_case_ : Tuple = 0
for num in nums[1:]:
snake_case_, snake_case_ : int = ... | 123 |
from collections import deque
def A__ ( __A : Optional[Any] ) ->Tuple:
__A =len(__A )
__A =deque()
__A =[False for _ in range(__A )]
__A =[-1 for _ in range(__A )]
__A =index_of[:]
def strong_connect(__A : Union[str, Any] ... | 184 | 0 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__lowercase : Optional[int] =logging.get_logger(__name__)
class A ( __lowercase ):
def __init__( self: Tuple , *_lowerCAmelCase: List[Any] ... | 550 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__lowercase : Optional[int] =TypeVar("""T""")
def a__ ( lowercase__ ):
'''simple docstring'''
return (position - 1) // 2
def a__ ... | 550 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
SCREAMING_SNAKE_CASE = logging.getLogger()
@unittest.skip("Temporarily disable ... | 485 |
import os
import sys
SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequ... | 485 | 1 |
_UpperCAmelCase : List[str] = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N"... | 108 |
def __lowerCamelCase ( UpperCamelCase__ = 50000000 ):
'''simple docstring'''
snake_case_ = set()
snake_case_ = int((limit - 24) ** (1 / 2) )
snake_case_ = set(range(3 , prime_square_limit + 1 , 2 )... | 108 | 1 |
'''simple docstring'''
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowercase ( __magic_name__ ):
@require_torch
def UpperCamelC... | 539 |
'''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, Tenso... | 539 | 1 |
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A : Li... | 702 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : str = logging.getLogger(__name__)
@dataclass
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple doc... | 356 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__snake_case = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""... | 451 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
... | 451 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __UpperCamelCase ( ... | 326 |
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_schedule,
... | 326 | 1 |
"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformers... | 308 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_a... | 104 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_A = "Usage of script: script_name <size_of_canvas:int>"
_A = [0] * 1_00 + [1] * 10
random.shuffle(choice)
def lowerCamelCase__ ( __lowerCAmelCase : ... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2ve... | 279 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class a__( lowerCamelCase__ ):
lowercase__ = (DDPMScheduler,)
def lowercase_ ( self : Optional[int] , **__snake_case : Optio... | 526 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: List[Any] = {'configuration_reformer': ['REFORMER_PRETRAINED_CONF... | 526 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'google/bigbird-roberta-base': 'https://huggingface.co/google... | 721 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCamelCase__ :
_SCREAMING_SNAKE_CASE : Optional[str] = field(
default="codeparrot/codeparrot" ,metadata={"help": "Model name or path of model to be trained."} )
_SCREAMING_SNAKE_CASE :... | 326 | 0 |
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