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 |
|---|---|---|---|---|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main... | 357 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a__ ( ... | 357 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__snake_case : Union[str, Any] = ... | 701 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'scipy']
def __init__( self: Tuple , *_SCREAMING_SNAKE_C... | 615 | 0 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
... | 118 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_lowercase = 2_048
_lowercase = 4_096
_lowercase = 42
_lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_lowercase = {'''null''': 0, '''short''... | 118 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/goo... | 228 |
"""simple docstring"""
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
_A = logging.get_logger(__... | 228 | 1 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
SCREAMING_SNAKE_CASE_ = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@data... | 34 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_C... | 34 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__lowerCamelCase : List[str] = TypeVar("T")
class __magic_name__ ( Generic[T] ):
def __init__( self : Union[str, Any] , UpperCamelCase_... | 457 |
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 __magic_name__ ( A__, unittest.Test... | 457 | 1 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _UpperCAmelCase ( _lowerCamelCase : Optional[int] ) ... | 384 |
'''simple docstring'''
from collections.abc import Sequence
def _UpperCAmelCase ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCamelCase ) )
def _UpperCAmelCase ( _lowerCamelCase : ... | 384 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, ra... | 298 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import * | 298 | 1 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
a__ : int = {
'facebook/maskformer-swi... | 51 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
UpperCAmelCase ... | 84 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
... | 720 |
'''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.o... | 61 | 0 |
'''simple docstring'''
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
de... | 261 | 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_tens... | 312 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 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 _UpperCAmelCase :
def __init__( self : Dict , lowercase_ : ... | 123 |
"""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 BaseTransformersCLIComman... | 673 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _a ( yaml.SafeLoader):
"""simple docstring"""
def lowercase__ ( self : List[str] , __UpperCamelCase : Any )->List[Any]:... | 95 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_UpperCAmelCase ... | 95 | 1 |
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.linear_1.weight'),... | 269 |
UpperCamelCase = 8.3_144_598
def __lowerCamelCase ( __lowerCAmelCase : float , __lowerCAmelCase : float ) -> float:
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
r... | 269 | 1 |
"""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_timesteps,
... | 396 |
"""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 _lowerCAmelCase ( nn.Module ):
__lowerCAmelCase : int
__lowerCAmelCase ... | 396 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> int:
"""simple docstring"""
snake_case__ : str = set(range(3 , __lowerCAmelCase , 2 ) )
primes.add(2 )
for p in range(3 , __lowerCAmelCase , 2 ):
if p not in primes:
... | 252 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
... | 689 | 0 |
class a__ ( __SCREAMING_SNAKE_CASE ):
pass
class a__ ( __SCREAMING_SNAKE_CASE ):
pass
class a__ :
def __init__( self : int ) -> Tuple:
"""simple docstring"""
lowerCamelCase_: Any = ... | 713 | import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a__ ( __SCREAMING_SNAKE_CASE ... | 584 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = ... | 41 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to... | 41 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : Optional[int] =logging.get_logger(__name__)
_A : Optional[int] ={
'''bert-base-uncased''': '''ht... | 719 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( _lowercase ... | 4 | 0 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a : Union[str, Any] = logging.get_logger(__name__)
d... | 637 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE ( snake_case ):
'''simple docstring'''
__UpperCamelCase = "M-CLIP"
def __init__( self , SCREAMING_SNAKE_CASE__=... | 329 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a_ = 50_00_00
a_ , a_ = os.path.split(__file__)
a_ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace("""... | 708 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : Dict , __a : Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : Any = ''
for i in table:
res += inp[i - 1]
return res
def UpperCAmelCase_ ( __a : List[Any]... | 349 | 0 |
'''simple docstring'''
def __UpperCamelCase ( a : list ) ->list:
if len(a ) < 2:
return collection
def circle_sort_util(a : list , a : int , a : int ) -> bool:
snake_case = False
if low == high:
return swapped... | 342 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowercase ( unittest.TestCase ... | 342 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase: Optional[int] = TypeVar('KEY')
lowerCAmelCase: Tuple = TypeVar('VAL')
@dataclass(frozen=lowerCamelCase__ , slots=lowerCamelCase... | 717 |
'''simple docstring'''
def lowerCamelCase__ ( _A = 6008_5147_5143 ):
try:
a : Optional[int] = int(_A )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueError('Parameter n must be greater ... | 195 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSch... | 343 |
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, Flax... | 343 | 1 |
'''simple docstring'''
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 BartForCondit... | 136 |
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A : int = get_tests_dir("""fixtures/test_sentencepiec... | 136 | 1 |
def _UpperCAmelCase ( UpperCamelCase: Dict , UpperCamelCase: Tuple ):
"""simple docstring"""
__lowerCAmelCase = [1]
for i in range(2 , __UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__lowerCAmelCase = ... | 611 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> tuple[int, int]:
"""simple docstring"""
try:
lowerCAmelCase_ : Tuple = float(__UpperCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
lowerCAmelCa... | 610 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCAmelCase = '''
import os
'''
UpperCAmelCase = '''
def foo():
import os
return False
'''
UpperCAmelCase = '''
def foo():
def bar():
if True:
import os
return Fals... | 565 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
if "model" in orig_key:
lowercase = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
lowercase = orig_key.repla... | 565 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""",... | 13 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase : Dict = logging.getLogger(__name__)
... | 529 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
lowercase__ :Dict = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-c... | 633 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[... | 633 | 1 |
'''simple docstring'''
from math import sqrt
def _lowerCAmelCase ( lowerCamelCase_ : Dict ):
assert isinstance(__lowercase , __lowercase ) and (
number >= 0
), "'number' must been an int and positive"
__lowercase = True
# 0 a... | 502 |
"""simple docstring"""
def _A ( __lowercase = 200_0000 ):
"""simple docstring"""
lowerCamelCase__ = [0 for i in range(n + 1 )]
lowerCamelCase__ = 1
lowerCamelCase__ = 1
for i in range(2 , int(n**0.5 ) ... | 129 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def A_ ( A__ , A__ ) ... | 392 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torc... | 392 | 1 |
def lowerCAmelCase__ ( a__ ) ->str:
'''simple docstring'''
_UpperCamelCase = int(a__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(a__ )
_UpperCamelCase = divmod(a__ , 2 )
return binary_recursive(a__ ) + str(a__ )
def lowerC... | 547 | def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
__lowercase ... | 167 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
A_ = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR... | 704 |
'''simple docstring'''
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 imp... | 465 | 0 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
... | 385 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import ... | 158 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configura... | 712 | 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_ = {
"camembert-base": "https://huggingface.co/camembert-base/r... | 390 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowercase = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__lowercase ... | 203 |
'''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-... | 292 | 0 |
from __future__ import annotations
def lowerCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ = None )-> list[list[str]]:
lowerCAmelCase_ : Optional[Any] = word_bank or []
# create a table
lowerCAmelCase_ : int = len(a_ ) + 1
lowerCAmelCase_ : list[list[list[s... | 713 |
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 : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :float = 1 / sqrt(2 ) ) -> IIRFilter:
__lowe... | 504 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case_ ( __lowercase... | 504 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _snake_case ( lowerCamelCase__ : Optional[in... | 244 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase__ ... | 244 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def A_ ( _lowerCAmelCase : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : Any ):
"""simple docstring"""
_low... | 44 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobert... | 36 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureEx... | 137 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
... | 137 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERende... | 76 | """simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__magic_name__ = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"s... | 232 | 0 |
"""simple docstring"""
from functools import reduce
__lowerCAmelCase : Optional[Any] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''... | 21 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int , __UpperCamelCase : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
snake_case_ ... | 21 | 1 |
"""simple docstring"""
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ):
'''simple docstring'''
__magic_name__ = 0
__magic_name__ = 0
__mag... | 545 |
"""simple docstring"""
def A ( __snake_case: int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
__magic_name__ = limit + 1
__magic_name__ = [0] * limit
for first_term in range(1 , __snake_case ):
... | 545 | 1 |
"""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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils ... | 48 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] ):
"""simple docstring"""
... | 48 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = """"""
def _A ( _lowerCAmelCase ):
"""simple docstring"""
... | 474 |
'''simple docstring'''
from collections.abc import Sequence
def _A ( _lowerCAmelCase = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
__lowercase =nums[0]
for i ... | 474 | 1 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM ... | 157 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_lowerCamelCase : List[Any] = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu... | 157 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_snake_case : Union[str, Any] = re.compile(R'\b(a|an|the)\b', re.UNICODE)
_snake_case : Dict = None
def a_ ( ):
__lowerCAmelCase = ... | 53 |
from copy import deepcopy
class snake_case__ :
"""simple docstring"""
def __init__( self : Union[str, Any], _snake_case : list[int] | None = None, _snake_case : int | None = None ) ->None:
if arr is None and size is not None:
s... | 478 | 0 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def _lowerCamelCase( *UpperCamelCase__ : Dict ) -> List[Any]:
with open(UpperCamelCase__ , '''r''' ) as fh:
fcntl.flock(UpperCamelCase__ , ... | 707 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _lowerCamelCase( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ) -> List[Any]:
A : Any = {
... | 537 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from ... | 324 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMix... | 324 | 1 |
"""simple docstring"""
from math import ceil
def lowercase_ ( _lowercase : Optional[Any] , _lowercase : List[Any] ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = list(range(0 , snake_case_ ) )
... | 710 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configura... | 292 | 0 |
'''simple docstring'''
import unittest
from transformers import 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_pi... | 69 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 1 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
_UpperCAmelCase = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
"AOT_NVFUSER",
"... | 707 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Optional[int]:
return x + 2
class _UpperCamelCase ( unittest.TestCase ):
def lowercase (... | 371 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow... | 91 |
"""simple docstring"""
import random
class lowerCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> tuple[list[int], list[int]]:
A = [ord(lowerCamelCase_ ) for i in text]
A = []... | 617 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
# `task` is not a ClassVar since we want it to be part o... | 714 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding +... | 53 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {"""vocab_file""": """vocab.json"""}
lowerCAmelCase ... | 174 |
'''simple docstring'''
lowerCAmelCase : List[str] = 2_5_6
# Modulus to hash a string
lowerCAmelCase : Tuple = 1_0_0_0_0_0_3
def _A ( A ,A ) -> bool:
lowercase : List[Any] = len(A )
lowercase : List[Any] = len(A )
... | 372 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to includ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[int] = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/ma... | 32 | 1 |
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 ...test_pipeline_mixin im... | 148 |
import argparse
import struct
import unittest
class _lowerCAmelCase :
def __init__( self , _UpperCamelCase ) -> None:
lowerCAmelCase_ = data
# Initialize hash values
lowerCAmelCase_ = [
0x6_A_0_9_E_6_6_7,
0xB... | 290 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from tra... | 127 |
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
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]
snake_case__ : L... | 127 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCamelCase__ : Tuple, lowerCamelCase__ : Tuple, lowerCamelCase__ : Union[str, Any] ) -> Dict:
if exponent == 1:
return base
if exponent % 2 == 0:
_SCREAMING_SNAKE_CASE : Dict = _modexpt(a__, ex... | 572 |
'''simple docstring'''
from math import isqrt
def a__ ( a__ ):
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2 , isqrt(a__ ) + 1 ) )
def a__ ( a__ = 10**6 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
... | 627 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _UpperCAmelCase ( ... | 302 |
"""simple docstring"""
import requests
lowerCamelCase_ : Any = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Any = requests.get(_NEWS_API + bbc_news_api_ke... | 302 | 1 |
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` instead.'
)
| 625 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=Tru... | 186 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ : Tuple = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
lowercase__ : Optional[Any] = None
def __lowercase ( ... | 485 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 485 | 1 |
# using dfs for finding eulerian path traversal
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Dict:
UpperCamelCase_: str = (path or []) + [u]
for v in graph[u]:
if visited_e... | 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""Mv... | 472 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_... | 702 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
T... | 367 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Paddin... | 69 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def A ( snake_case__ ):
'''simple docstring'''
def decorator(snake_case__ ):
SCREAMING_SNAKE_CASE__ = getattr(snake_case__ , """handle_key""" , ... | 196 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCamelCase = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
"""simple docstring"""
snake_case__ = ... | 125 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase = logging.getLogger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple... | 125 | 1 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
fr... | 340 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
__snake_case =... | 69 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase__ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
... | 719 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_comman... | 312 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
snake_case : str = logging... | 566 |
'''simple docstring'''
from __future__ import annotations
def A__ ( A_ ) -> list[int]: # This function is recursive
_lowercase = len(A_ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
ret... | 497 | 0 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCAmelCase_ : Union[str, Any] = '2.13.1'
import platform
import pyarrow
from packaging import version
if version.parse(platform.p... | 716 |
'''simple docstring'''
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 _lowerCamelCase ( lowercase : str , lowercase : i... | 521 | 0 |
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __snake_case ( UpperCAmelCase_ : str , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if version.pa... | 675 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowercase : Any = HUGGINGFACE_HUB_CACHE
lowercase : Any = "config.json"
lowercase : Any = "diffusion_pytorch_model.bin"
lowercase : Optional[Any] = "diffusion_flax_... | 327 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCAmelCase_ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''https://h... | 519 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase_ = list[list[float | int]]
def UpperCAmelCase ( A__ , A__ ) -> Matrix:
_snake_case : int = len(A__ )
_snake_case : Matrix = [[0 for _ in ra... | 519 | 1 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : Tuple ): # noqa: E741
'''simple docstring'''
_lowerCAmelCase = len(_SCREAMING_SNAKE_CASE )
_lowerCAmelCase = 0
_lowerCAmelCase = [0] * n
_lowerCAmelCase = [... | 18 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 27 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerToke... | 186 |
def _lowerCamelCase ( __A : int ) -> bool:
if not isinstance(__A , __A ):
_UpperCAmelCase : Tuple = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__A )
if number < 0:
return False
_UpperCAme... | 186 | 1 |
lowercase__ : Union[str, Any] = "Alexander Joslin"
import operator as op
from .stack import Stack
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
s... | 376 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstr... | 376 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToke... | 720 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
f... | 671 | 0 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_T... | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : int = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
a : int = _... | 556 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 706 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_snake_case : List[Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wan... | 203 | 0 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from... | 36 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def A_ ( snake_case_ : int ,snake_case_ : int = 2 ,snake_case_ : int = 1 ,snake_case_ : int = 3 ,):
'''simple docstring'''
# A value less than 2 can cause an infin... | 499 | 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 SCRE... | 717 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor,... | 666 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 617 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 617 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A ( __UpperCamelCase ) -> int:
A__ = ... | 52 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoToke... | 52 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def snake_case_ ( lowercase__ , lowercase__ , lowercase__ ):
if not arr:
return None, None, 0
if low ... | 199 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_u... | 199 | 1 |
a_ : Any = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-', 'W': '.... | 148 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForc... | 148 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 110 |
import os
def UpperCAmelCase ( ) -> Any:
"""simple docstring"""
__A = os.path.dirname(os.path.realpath(a_ ) )
__A = os.path.join(a_ , "triangle.txt" )
with open(a_ ) as f:
__A = f.readlines()
__A = []
f... | 55 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avai... | 25 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : int = {
"""post_extract_proj"... | 25 | 1 |
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, BartTokenizer
... | 398 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : Tuple = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'f... | 640 | 0 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> tuple[int | None, int | None, float]:
"""sim... | 706 |
from cva import destroyAllWindows, imread, imshow, waitKey
def __lowerCamelCase ( snake_case__ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's c... | 569 | 0 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Any = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disabl... | 70 |
"""simple docstring"""
import pytest
import datasets
# Import fixture modules as plugins
__UpperCAmelCase = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowercase__ ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str... | 642 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...t... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCamelCase ( lowerCamelCase : List[str]):
if "model" in orig_key:
A_ : List[str] = orig_key.replace("""model.""" , """""")
if "norm1" in ori... | 665 |
'''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-2.0
#
# ... | 665 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelera... | 366 | """simple docstring"""
import math
def lowercase_ ( _lowerCamelCase: int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0,... | 366 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , UpperCamelCase__ ):
"""simple docstring"""
a_ = value
a_ ... | 536 |
'''simple docstring'''
import math
import qiskit
def __UpperCamelCase ( lowercase_ : int = 1 , lowercase_ : int = 1 , lowercase_ : int = 1 ):
"""simple docstring"""
if (
isinstance(lowercase_ , lowercase_ )
... | 536 | 1 |
'''simple docstring'''
def a_ ( _A , _A ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
snake_case__ = str(bin(_A ) )
binary_number +=... | 716 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __SCREAMING_SNAKE_CASE:
def __init__( self: int , UpperCamelCase: Any ) -> List[Any]:
snake_case__ = data
snake_case__ ... | 372 | 0 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase_ : int ... | 255 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( __a ):
_A :Optional[Any] = ['''image_processor''', '''tokenizer''']
_A :List[str] = '''ViTImageProcessor'''
_A :Optional[Any] = ('''C... | 428 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolFormerOnnxConfig',
... | 701 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 684 | 0 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if index == r:
for j in range(_lowerCAmelCase ):
print(data[j] , end=" " )
... | 333 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@r... | 333 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data... | 248 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = len(UpperCamelCase_ )
for i in range(UpperCamelCase_ ):
for j in range(i + 1 , UpperCamelCase_ ):
if numbers[j] < numbers[i]:
__SCREAMING_SNAKE_CASE ... | 248 | 1 |
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