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'''
SCREAMING_SNAKE_CASE_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(SCREAMING_SNAKE_CASE__ ... | 597 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils i... | 597 | 1 |
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenizat... | 213 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr... | 213 | 1 |
'''simple docstring'''
UpperCamelCase__ : Dict = 65_521
def __UpperCamelCase( _A : str ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = 1
UpperCAmelCase__ : Tuple = 0
for plain_chr in plain_text:
UpperCAmelCase__ : str = ... | 614 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging... | 614 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( UpperCamelCase):
a_ = ["image_processor", "tokenizer"]
a_ = "CLIPImageProcessor"
a_ = ... | 713 |
'''simple docstring'''
def __UpperCAmelCase ( A : List[str] , A : Tuple , A : Union[str, Any]=False ) -> Tuple:
if isinstance(A , A ) and isinstance(A , A ):
UpperCAmelCase_ : Any = len(set_a.intersection(A ) )
if alternative... | 216 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
... | 196 |
'''simple docstring'''
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'''):
lowercase_ : Any = {
'''linear''': PIL.Image.Resampling.BILINEAR,
... | 588 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 98 |
'''simple docstring'''
def _snake_case ( A ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = 0
lowerCAmelCase__ = number
while duplicate > 0:
... | 98 | 1 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE__ ( ) -> None:
lowercase__: Union[str, Any] = input('''Enter message: ''' )
lowercase__: List[Any] = int(input(F"""Enter key [2-{len(__A ) - 1}]: """ ) )
lowercase__: int = input('''Encryption/Decryption [e... | 586 |
'''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,
is_torch_av... | 94 | 0 |
from __future__ import annotations
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 718 |
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
UpperCAmelCase_ = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAM... | 23 | 0 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase_ : int ) -> None:
__lowerCAmelCase = size
__lowerCAmelCase = [0] * size
__lowerCAmelCase = [0] * size
... | 53 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : List[Any], lowerCAmelCase_ : str ... | 53 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase_ (a__ , unittest.TestCase ):
"""simpl... | 545 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase_ ( _snake_case ,_snake_case=7 ):
SCREAMING_SNAKE_CASE__ : Dict = None
if token is not None:
SC... | 545 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyaz... | 690 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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... | 690 | 1 |
"""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 __UpperCAmelCase( SCREAMING_SNAK... | 703 |
"""simple docstring"""
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,
DISTI... | 85 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
SCREAMING_SNAKE_CASE ... | 94 |
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : Union[str, Any] = []
lowerCamelCase_ : Tuple = []
lowerCamelCase_ : Dict = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Pr... | 364 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, r... | 465 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A_ = datasets.logging.get_logger(__name__)
A_ = "\\n@InProceedings{moosavi... | 465 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( _UpperCAmelCase : list[int] , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if len(_UpperCAmelCase ) < k or k < 0:
raise ValueError("Invali... | 244 | '''simple docstring'''
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
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ (sna... | 244 | 1 |
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : str = "cpu" , _lowerCAmelCase : Union[str, None] = None ) -> None:
UpperCAm... | 528 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sq... | 528 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 100 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 100 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 129 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = "▁"
__... | 129 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbon... | 227 |
'''simple docstring'''
import random
from typing import Any
def __A ( a_ : list ):
for _ in range(len(a_ ) ):
lowerCAmelCase : List[Any] = random.randint(0 ,len(a_ ) - 1 )
lowerCAmelCase : Tuple = random.randint(0 ,len... | 525 | 0 |
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,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 721 |
import pytest
import datasets
# Import fixture modules as plugins
_lowerCamelCase = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# Mark tests as "unit" by default if not marked as "integration" ... | 613 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE (a__ ):
pass
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase):
'''simple docstring'''
... | 8 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = {
'xlm-mlm-en-204... | 213 | 0 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> float:
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueE... | 69 |
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_sentencepiece_available():
from .tokenization_remb... | 69 | 1 |
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 A_ ( enum.Enum ):
'''simple docstring'''
_UpperCamelCase : Tuple = """all_checks"""
_UpperCamelC... | 84 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDi... | 489 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWi... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_V... | 229 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : s... | 229 | 1 |
'''simple docstring'''
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.pi... | 707 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.g... | 666 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : str ) -> List[str]:
'''simple docstring'''
if not nums:
return 0
lowerCAmelCase = nums[0]
lowerCAmelCase = 0
for num in nums[1:]:
lowerCAm... | 169 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 | 0 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_... | 721 |
class lowerCAmelCase :
def __init__( self : Union[str, Any] , UpperCAmelCase : list ) -> None:
lowerCamelCase__ : int = set_counts
lowerCamelCase__ : List[str] = max(UpperCAmelCase )
lowerCamelCase__ : Dict = len(... | 188 | 0 |
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 snake_case__(unittest.TestCase ):
"""simple d... | 496 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def __snake_case ( _UpperCamelCase ) -> Optional[Any]:
_a = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
... | 487 | 0 |
"""simple docstring"""
import qiskit
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> qiskit.result.counts.Counts:
lowerCAmelCase__ : List[str] = qiskit.Aer.get_backend("""aer_simulator""" )
lowerCAmelCase__ : List[str] = qiskit.Qu... | 507 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 507 | 1 |
"""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_sentencepiece_available():
fr... | 77 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> int:
"""simple docstring"""
__UpperCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__UpperCAmelC... | 77 | 1 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_lowerCAmelCase ... | 348 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipeline... | 348 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase = 10
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'... | 569 |
"""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,
WavaVecaCTCTokeniz... | 567 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase : Dict = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def A__ ( ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = os.path.dirn... | 701 |
"""simple docstring"""
def A__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
_SCREAMING_SNAKE_CASE = _modexpt(UpperCamelCase__ , ... | 168 | 0 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=5 ) -> List[str]:
# Adapted from https://github.com/pytorch/fair... | 103 |
# 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
#
# Unless required by applica... | 511 | 0 |
from __future__ import annotations
def lowercase_ (A : list , A : int | None = None , A : int | None = None ):
if start is None:
snake_case__ : str = 0
if end is None:
snake_case__ : List[str] = len(A ... | 243 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since ... | 243 | 1 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Any = tf.convert_to_tensor(lowerCamelCase_ )
_UpperCAmelCase : Any = 0.5 * (1.0 + tf.math.erf(x / tf.... | 414 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 47 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
lowerCamelCase__ : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
lowerCamelCase__ : ... | 18 |
"""simple docstring"""
import math
class lowercase__:
'''simple docstring'''
def __init__( self :Union[str, Any] , lowerCamelCase_ :List[str]=0 ) -> List[Any]: # a graph with Node 0,1,...,N-1
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = n... | 18 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _SCREAMING_SNAKE_CASE ( __a ):
a_ : str ... | 132 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( __a):
__a : Optional[Any] = """SpeechT5FeatureExtractor"""
__a : Dict = """SpeechT5Tokenizer"""
def __init__( self , _A , ... | 238 | 0 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertFor... | 461 | '''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( __lowerCamelCase : List[Any] ) -> Tuple:
'''simple docstring'''
... | 461 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_d... | 54 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowercase : List[Any] =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def a__ ( ... | 54 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase_ ( a__ ):
__UpperCAmelCase = 'Speech2TextFeatureExtractor'
__UpperCAmelCase = 'Speech2TextTokenizer'
def __init__( self... | 223 |
'''simple docstring'''
import os
def _UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
with open(os.path.dirname(__A ) + "/p022_names.txt" ) as file:
UpperCamelCase__ = str(file.readlines()[0] )
UpperCamelCase__ ... | 223 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__A = logging.get_logger(__name__)
class snake_case ( __snake_case ):
def __init__( self : str , *UpperCamelCase__ :... | 346 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__A = object()
# For specifying empty leaf dict `{}`
__A = object()
def ... | 346 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __lowerCamelCase ( ) -> None:
print("Making key files..." )
make_key_files("rsa" , 1_024 )
print("Key files generation successful.... | 594 | import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _a :
"""simple docstring"""
__SCREAMING_SNAKE_CASE = None
def __lowerCAmelCase ( self ):
_lowercase =self.feature_extraction_class(**self.feat_extract_dic... | 594 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__UpperCamelCase : Any = logging.get_logger(__name__) # pylint: disable=inva... | 4 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Tuple , snake_... | 374 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common i... | 572 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
if n_term == "":
return []
_lowercase = []
for temp in range(int(snake_case_ ) ):
series.append(F"""1/{temp + 1}""" if series else """1""" )
return series
if __name__ == "__main__":
_lowerCamelCase = in... | 572 | 1 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowercase__ :
__UpperCAmelCase = 42
__UpperCAmelCase = None
__UpperCAmelCase = None
def _snake_case ... | 88 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
SCREAMING_SNAKE_CASE : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
i... | 294 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase = 10 , _lowerCamelCase = 22 ) -> int:
'''simple docstring'''
_lowerCamelCase : Tuple = range(1 , _lowerCamelCase )
_lowerCamelCase : Tuple = range(1 , _lowerCamelCase )... | 386 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowerCamelCase_( ) -> Any:
'''simple docstring'''
_lowerCamelCase : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=_l... | 386 | 1 |
'''simple docstring'''
class __A :
"""simple docstring"""
def __init__( self )-> int:
lowercase__ = {}
def snake_case_( self )-> None:
print(self.vertex )
for i in self.vertex:
print... | 161 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( a_ : str = "" ):
__a = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__a = Beautiful... | 539 | 0 |
from torch import nn
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
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"""Unsupported activation func... | 716 | # Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
snake_case = TypeVar("T")
class __A ( Generic[T] ):
'''simple docstring'''
def __init__( ... | 587 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Union[str, Any] = {
"config... | 564 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformer... | 564 | 1 |
def A ( a_ ,a_ ) -> int:
__UpperCamelCase : Optional[Any] =0
__UpperCamelCase : str =len(a_ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
... | 721 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def A ( a_ = "" ) -> dict[str, float]:
__UpperCamelCase : Tuple =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__UpperCamelCase : Optional[int... | 154 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchF... | 196 |
"""simple docstring"""
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,
... | 196 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _lowerCAmelCase ( Upp... | 712 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 411 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
def UpperCAmelCase__ ( lowerCAmelCase__ :str ) -> dict:
'''simple docstring'''
lowercase = f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
... | 359 | """simple docstring"""
from math import pow, sqrt
def UpperCAmelCase__ ( *lowerCAmelCase__ :float ) -> bool:
'''simple docstring'''
lowercase = len(lowerCAmelCase__ ) > 0 and all(value > 0.0 for value in values )
return result
... | 359 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __magic_name__( __UpperCAmelCase ) -> Any:
'''simple docstring'''
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , set() )
@pytest.... | 709 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu... | 18 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
_SCREAMING_SNAKE_CASE = ... | 18 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClass... | 713 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerat... | 242 | 0 |
"""simple docstring"""
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 = False
... | 104 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ (SCREAMING_SNAKE_CASE__ ):
lowerCAmelCase__ =(KDPMaDiscreteScheduler,)
lowerCAmelCase__ =10
... | 360 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_v... | 185 |
def UpperCamelCase_( _A :str )-> int:
UpperCamelCase__ = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCamelCase__ = hex_num[0] == "-"
if is_negative:
UpperCamelCase__ = hex_num[1:]
try:
Up... | 185 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
norm... | 633 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 322 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : Any = "isbn/0140328726" ):
UpperCamelCase_ : Optional[int] = olid.strip().strip("""/""" ) # Remove leading/trailing... | 712 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import night... | 138 | 0 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int | float:
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
... | 235 |
import requests
lowercase_ = """YOUR API KEY"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = giphy_api_key ) -> list:
lowercase__ = '+'.join(query.split() )
lowercase__ = F"""https://api.giphy.co... | 235 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 157 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Union[str, Any] = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
... | 157 | 1 |
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.generatio... | 458 |
from string import ascii_uppercase
UpperCAmelCase_ = {char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_ = dict(enumerate(ascii_uppercase))
def __magic_name__ ( lowercase , lowercase ) -> str:
"""simple docstring"""
... | 458 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( __lowerCAmelCase):
... | 712 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ... | 106 | 0 |
'''simple docstring'''
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 (
... | 42 | import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase = pytest.mark.integration
@pytest.mark.parametrize('path' ,['... | 558 | 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 ...tes... | 509 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.... | 509 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 455 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
SCREAMING_SNAKE_CASE = True
exce... | 579 | 0 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
... | 384 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A_ = "src/transformers"
A_ = "docs/source/en/tasks"
... | 384 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Tuple = {
'''asapp/sew-d-tiny-100k''': '''h... | 231 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/r... | 231 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = (... | 71 | import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = (... | 71 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ : List[str] = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization... | 673 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase_ : List[str] = False
class UpperCamelCase_ ( ... | 673 | 1 |
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( ... | 320 | '''simple docstring'''
UpperCamelCase_ = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
UpperCamelCase_ = ['''a''', '''b''', '''c''', '''d''', '''e''']
def lowerCamelCase ( UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__... | 320 | 1 |
from itertools import count
def a__ ( A_ = 50 ):
'''simple docstring'''
__magic_name__ = [1] * min_block_length
for n in count(lowercase__ ):
fill_count_functions.append(1 )
for block_length in range(lowercase__, n + 1 ):
for block_start ... | 529 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
... | 230 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from typing import Any
import requests
__lowerCAmelCase : Union[str, Any] = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase ... | 674 | """simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 674 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test... | 546 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 52 | 0 |
'''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 = get_tests_dir("""fixtures/test_sentencepiece_with... | 716 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggi... | 617 | 0 |
'''simple docstring'''
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():
... | 28 |
import os
import sys
import unittest
__a: Tuple = 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_object, find_bac... | 108 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers... | 715 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
ca... | 33 | 0 |
"""simple docstring"""
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import t... | 46 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
snake_case_ : Optional[int] = l... | 138 | 0 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, ... | 408 |
from torch import nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
super().__init__()
snake_case__ ... | 408 | 1 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 101 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objec... | 151 | 0 |
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, ... | 718 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable(... | 693 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__lowerCamelCase : str = """
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={... | 297 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch... | 191 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
UpperCamelCase_ : int
UpperCamelCase_ : Node | None = None
UpperCamelCase_ : No... | 674 | """simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase :... | 674 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a ( __snake_case : Any, __snake_case : ... | 608 |
"""simple docstring"""
import os
import sys
import unittest
__lowerCamelCase = 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_d... | 608 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CAS... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
... | 680 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 692 |
from copy import deepcopy
class __A :
def __init__(self , __magic_name__ = None , __magic_name__ = None ):
if arr is None and size is not None:
lowerCamelCase__ : int = size
lowerCamelCase__ : Union[str, Any] = [0] * size
... | 157 | 0 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__A = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attent... | 560 |
"""simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTeste... | 560 | 1 |
'''simple docstring'''
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
Di... | 525 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase = []
def __A ( a_ : list[list[int]] ,a_ : int ,a_ : int ):
for i in range(len(a_ ) ):
if board[row][i] == 1:
return False
for i in range(len(a_ ) ):
if bo... | 525 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ = 1000000 ) -> int:
'''simple docstring'''
__UpperCAmelCase : List[Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in r... | 717 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _UpperCamelCase ( snake_case__ ) -> Dict:
__UpperCAmelCase : List[Any] = FileLock(str(tmpdir / "foo.lock" ) )
__UpperCAmelCase : List[str] = FileLoc... | 382 | import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__na... | 382 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
lo... | 711 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 1 | 0 |
'''simple docstring'''
from math import factorial
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or success... | 476 |
'''simple docstring'''
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 TF... | 476 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 403 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase_ ( unittest.TestCase ):
def __magic_name__ ( self ):
a_ = 10
def __... | 403 | 1 |
'''simple docstring'''
from __future__ import annotations
A : Dict = []
def _a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
for i in range(len(snake_case__ ) ):
if board[row][i] == 1:
return False
for i in range(len(snake_case__ ... | 349 |
"""simple docstring"""
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 A( lowerCamelCase__ ):
"""simple docstring"""
def __in... | 355 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.util... | 701 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = 0
if start < end:
... | 47 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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 ...tes... | 81 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 33 | 0 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoT... | 305 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCon... | 305 | 1 |
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