code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers... | 88 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common imp... | 88 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow,... | 227 |
def snake_case( __magic_name__ , __magic_name__ ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(1_00, 0.2_5) = }''')
print(f'''{price_plus_tax(1_2_5.5_0, 0.0_5) = }''') | 308 | 0 |
from __future__ import annotations
import time
_SCREAMING_SNAKE_CASE : List[str] = list[tuple[int, int]]
_SCREAMING_SNAKE_CASE : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, ... | 218 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
s... | 218 | 1 |
'''simple docstring'''
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class A__ ( lowerCamelCase... | 162 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> list:
if n_term == "":
return []
_a = []
for temp in range(int(lowercase ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main_... | 63 | 0 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __magic_name__ ( self : Optional[int] , lowerCAmelCase... | 362 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__ : Any = '.'
# Internal TensorFlow ops tha... | 301 | 0 |
"""simple docstring"""
# 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 ... | 347 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_token... | 347 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : flo... | 27 | 0 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests... | 89 |
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase , __lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
if isinstance(__lowercase , __lowercase ) and isinstance(__lowercase , __lowercase ):
_A =... | 79 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig'... | 103 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines... | 103 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_... | 274 |
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class A :
'''simple docstring'''
def __init__( self : List[Any] ) -> str:
"""simp... | 274 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__... | 358 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
'vo... | 301 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class lowercase :
def __init__( self ,A__):
lowercase = str(id_)
lowercase = None
lowercase = None
lowercase = []
lower... | 101 |
import os
import pytest
from attr import dataclass
UpperCAmelCase__ : Optional[int] = """us-east-1""" # defaults region
@dataclass
class a__ :
"""simple docstring"""
UpperCAmelCase__ : str
UpperCAmelCase__ : Union[str, ... | 245 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=a_ ):
"""simple docstring"""
A : Union[str, Any] = ["torch"]
def __init__( self : str , *UpperCAmelCase_ : Union[str, Any] , *... | 345 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : list[int | float] , snake_case : int , snake_case : int ) -> int | float:
"""simple docstring"""
if len(snake_case ) =... | 345 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__lowercase = datasets.utils.logging.get_logger(__name__)
class _A ( folder_based_build... | 40 |
from __future__ import annotations
import math
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> None:
lowerCAmelCase_ : str = size
# approximate the overall size of segment tree with given value
... | 262 | 0 |
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
raise ValueError("""String lengths must match!""" )
_a : Optional[Any] = ... | 368 |
"""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.confi... | 324 | 0 |
__snake_case : List[str] = {
"""joule""": 1.0,
"""kilojoule""": 10_00,
"""megajoule""": 1_00_00_00,
"""gigajoule""": 10_00_00_00_00,
"""wattsecond""": 1.0,
"""watthour""": 36_00,
"""kilowatthour""": 3_60_00_00,
"""newtonmeter""": 1.0,
"""calorie_nutr""": 41_86... | 248 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE( metaclass=lowercase__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = ['note_seq']
def __init__( self ,*SCREAMING_SNAKE_CASE__ ,**SCREAMI... | 358 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 239 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowercase = (720, 1280) # Height, Width
__lowercase = (0.4, 0.6) # if height or width lower than this scale, dr... | 40 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""google/efficientnet-b7""": ... | 317 | 0 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Optional[Any] ) -> Any:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__lowerCAmelCase : Any = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCa... | 369 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers... | 232 | 0 |
def A__ ( SCREAMING_SNAKE_CASE__) -> list:
if len(SCREAMING_SNAKE_CASE__) <= 1:
return lst
__snake_case: Any = 1
while i < len(SCREAMING_SNAKE_CASE__):
if lst[i - 1] <= lst[i]:
i += 1
else:
__snake_case , __snake_case: Union[str, Any] = l... | 111 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class __snake_case ( unittest.TestCase , __lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Union[str, Any] ):
__sna... | 111 | 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.pipelines.stable_diffusi... | 6 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ ={
'''repo_name''': ['''t... | 6 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__snake_case = logging.get_logger(__name__)
__snake_case = OrderedDic... | 203 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
def merge(_snake_case : list , _snake_case : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
y... | 60 | 0 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_uti... | 245 |
# 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... | 245 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ : str = logging.get_logger(__name__)
snake_case__ : Dict = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/dec... | 60 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 60 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( lowercase ... | 133 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase__ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase__ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt... | 133 | 1 |
from math import pi, sqrt
def A_ ( A__ ) -> float:
if num <= 0:
raise ValueError('math domain error' )
if num > 1_71.5:
raise OverflowError('math range error' )
elif num - int(A__ ) not in (0, 0.5):
raise NotImplementedError('num mus... | 99 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Dict ):
"""simple docstring"""
snake_case_ = {} # Mapping from char to TrieNode
snake_case_ = False
... | 187 | 0 |
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 PretrainedConfig
from ...onnx import Onnx... | 352 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ ( __lowercase ):
A_ = ['image_processor', 'tokenizer']
A_ = 'ChineseCLIPImageProcessor'
A_ = ('BertTokenizer'... | 232 | 0 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
UpperCAmelCase_ : List... | 32 | def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : Union[str, Any] = abs(snake_case__ )
__UpperCAmelCase : Dict = 0
while n > 0:
res += n % 10
n //= 10
return res
def _UpperCamelCas... | 157 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
lowercase = None
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
A_ = self.feature_extraction_class(**self.fea... | 360 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase... | 101 | 0 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_sim... | 90 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase : list[int] = [ord(letter)... | 42 | 0 |
class UpperCAmelCase_ : # Public class to implement a graph
'''simple docstring'''
def __init__( self , _A , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = row
__SCREAMING_SNAKE_CASE =... | 358 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 118 | 0 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class lowercase:
'''simple docstring'''
def __init__( self: Dict, a_: Dict, a_: Any, a_: int, a_: Optional[Any]=None, a_: int=None ):
... | 64 |
"""simple docstring"""
from math import factorial
A_ = {str(d): factorial(d) for d in range(10)}
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case__ ) )
def UpperCAmelCas... | 64 | 1 |
"""simple docstring"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
if len(__lowerCamelCase ) <= 1:
return [tuple(__lowerCamelCase )]
lowerCAmelCase__ : str = []
def generate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )... | 360 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
# Check data validity, following RFC3... | 307 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lower... | 96 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
stooge(lowercase__ , 0 , len(lowercase__ ) - 1 )
return arr
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
if i >= h:
return
... | 96 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
lowerCamelCase : int = "1"
lowerCamelCase : int = "0"
lowerCamelCase : Union[str, Any] = "1"
lowerCamelCase : List[Any] = ort.SessionOptions()
lowerCamelCase : Opt... | 208 |
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : int ):
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(lowercase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 208 | 1 |
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__lowerCamelCase = val
__lowerCamelCase = None
__lowerCamelCase = None
def lowerCamelCase ( self , __UpperCAmelCase ):
'''simple docstring''... | 330 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modelin... | 330 | 1 |
'''simple docstring'''
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCAmelCase_ = pd.read_csv('sample_data.csv', header=None)
UpperCAme... | 352 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.j... | 61 | 0 |
'''simple docstring'''
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 impor... | 206 |
'''simple docstring'''
import re
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[int] = split_input(str_... | 206 | 1 |
"""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_ ... | 230 |
"""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
SCREAMING_SNAKE_CASE = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE = object()
... | 230 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
im... | 232 |
from math import isqrt, loga
def UpperCAmelCase_ ( __lowerCAmelCase ) -> list[int]:
__lowercase : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __lowerCAmelCase , ... | 156 | 0 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipe... | 100 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"""_TestComma... | 100 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
@staticmethod
@abstractmethod
def UpperCamelCase__ ( __magic_name__ ) -> Optional[Any]:
... | 201 |
from __future__ import annotations
def lowerCAmelCase_ ( __UpperCAmelCase: list[int] , __UpperCAmelCase: int ) -> list[int]:
UpperCamelCase__ : Optional[int] = 0
UpperCamelCase__ : Tuple = len(__UpperCAmelCase ) - 1... | 201 | 1 |
"""simple docstring"""
import os
import sys
import unittest
__SCREAMING_SNAKE_CASE : Optional[Any] = 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 ... | 366 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # ... | 233 | 0 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : str = ""
for word_or_phrase in separated:
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += ... | 61 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_SCREAMING_SNAKE_CASE : List[str] = logging.getLogger(__name__)
if is_t... | 127 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@datac... | 340 |
"""simple docstring"""
_lowerCAmelCase : dict[tuple[int, int, int], int] = {}
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days lef... | 340 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class a ( unittest.TestCase ):
def __UpperCAmelCase ( self ) -> Dict:
_a = [10, 20, 30, 40, 50, 60]
_a = [2, 4, 6, 8, 10, 12]
_a = 1_00
self.as... | 362 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 104 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
# Initialise PyTorc... | 303 |
from heapq import heappop, heappush
import numpy as np
def a__ ( snake_case , snake_case , snake_case , snake_case , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : int = grid.shape
__SCREAMING_SNAKE_CASE : Tuple = ... | 303 | 1 |
'''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,
WavaVe... | 3 |
'''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,
)
fr... | 3 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 34 |
'''simple docstring'''
import os
from distutils.util import strtobool
def snake_case_ (_a : Union[str, Any] , _a : List[Any] ):
for e in env_keys:
UpperCAmelCase = int(os.environ.get(_a , -1 ) )
if val >= 0:
return ... | 34 | 1 |
'''simple docstring'''
__snake_case : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def _UpperCAmelCase ( _UpperCamelCase : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCamelCase ) )
def _... | 18 | '''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : int = logging.get_logger(__name__)
__snake_case : str = {
'microsoft/xprophetnet-large-wiki100-cased': (
... | 18 | 1 |
UpperCamelCase_ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available... | 345 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 345 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 23 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ : str ... | 23 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging... | 102 |
'''simple docstring'''
from __future__ import annotations
__lowerCAmelCase = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def UpperCAmelCase_ (__a : list[float] ):
... | 271 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 366 |
'''simple docstring'''
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.
_lowercase = 10
def A (__lowerCamelCase :int , __lowerCamelCase :int , __lowerCamelCase... | 229 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : int = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "... | 236 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 236 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowerCamelCase (a_ :list[list[float]]) -> list[list[float]]:
lowercase :Tuple = Decimal
# Check if the provided matrix has 2 rows and 2 columns
... | 172 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase = '''\
@misc{chen2021evaluating,
title={Evalu... | 172 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase : str = logging.get_logger(__name__)
_UpperCAmelCase : List[Any] ... | 285 |
import os
import numpy
import onnx
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = a.name
snake_case_ = b.name
snake_case_ = ''
snake_case_ = ''
snake... | 285 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_l... | 364 | def snake_case (__lowercase , __lowercase ) -> str:
'''simple docstring'''
_snake_case : Tuple = ""
for word_or_phrase in separated:
if not isinstance(__lowercase , __lowercase ):
raise Exception("join() accepts only strings to ... | 284 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : int ) -> Dict:
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
lowerCAmelCase_ :List[Any] = len(lowercase__ )
lowerCAmelCase_ ... | 84 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = ... | 84 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__snake_case : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def... | 136 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The con... | 136 | 1 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 238 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import r... | 270 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
lowerCAmelCase__ :str = str(bin(_SCRE... | 254 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 254 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( __snake_case : Dict , __snake_case : int , __snake_case : Optional[int] , __snake_case : List[str] , ):
... | 220 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
... | 318 | 0 |
"""simple docstring"""
import os
import sys
import unittest
lowercase__ : Any = 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, creat... | 368 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> list[int]:
a = 2
a = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__UpperCamelCase)
if n... | 180 | 0 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCA... | 176 |
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or not number >=... | 176 | 1 |
__UpperCAmelCase : Optional[Any] = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 293 |
import math
def A__ ( SCREAMING_SNAKE_CASE__) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
__snake_case: Optional[int] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE__)
if num... | 293 | 1 |
'''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
__SCREAMING_SNAKE_CASE :Dict = '''src/transfo... | 22 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 22 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
snake_case : Optional[int] = sorted(string.lower() )
return len(__lowerCamelCase ) =... | 368 |
from math import pow
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
... | 176 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeat... | 187 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
... | 106 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[str] = logging.get_logger(__name__)
snake_case : Any = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class _snake... | 281 |
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('''Input value must be an \'int\' type''' )
a :Optional[int] = 0
while number:
... | 281 | 1 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a( A : Optional[int] ) -> List[Any]: # picklable for multi... | 227 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowercase : List[Any] = 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_copies # noqa... | 294 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
... | 294 | 1 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_A = logging.get_logger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
UpperCAmelCase__ : Any = None
@experimental
d... | 62 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLCo... | 321 | 0 |
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
class lowercase ( snake_case__... | 277 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Par... | 277 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : int = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP... | 54 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
a__ : Optional[int] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER a... | 54 | 1 |
# 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 applicab... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :int = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_avai... | 124 | 0 |
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _A ( lowercase , lowercase=7 ):
"""simple docstring"""
a =None
if token is not None:
a =... | 81 |
"""simple docstring"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowercase:
'''simple docstring'''
def __init__( self: List[Any], a_: List[str] ):
'''simple docstring'''
... | 64 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _snake_case ( lowerCamelCase__ : Sequence[float] , lowerCamelCase__ : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(lowerCamelCase__ ) )
def _snake_case ( lowerCamelCase__... | 209 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__ : Tuple = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
A__ : Dict = _LazyModule(__name__, globals()['__fi... | 209 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_UpperCAmelCase : List[Any] ={
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRCo... | 262 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optio... | 262 | 1 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = 'T5Config'
de... | 221 |
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, ImageC... | 221 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TrajectoryTransformerConfig''... | 87 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenizat... | 254 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _snake_case ( _snake_case : Dict ):
lowerCAmelCase : Union[str, Any] = prime_factors(lowerCAmelCase_ )
if is_square_free(lowerCAmelCase_ ):... | 369 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Union[str, Any] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : int = re.compile(R'''is\_([a-z_]*)_available()''')
# Catc... | 314 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = 10
... | 19 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__snake... | 4 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
fr... | 264 |
'''simple docstring'''
import sys
_lowercase : Union[str, Any] = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227... | 264 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig... | 205 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( A__ : bool = True , *A__ : int , **A__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
... | 205 | 1 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly ... | 89 |
'''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, requi... | 89 | 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
... | 282 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def a_ ( __lowercase : Dict ) -> List[Any]:
_snake_case = args.pruning_method
_snake_case = args.threshold
_snake_case = args.model_name_or_... | 282 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfi... | 93 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax... | 93 | 1 |
import functools
def lowerCamelCase__ ( A__ : str , A__ : str ):
'''simple docstring'''
__lowerCamelCase = len(A__ )
__lowerCamelCase = len(A__ )
@functools.cache
def min_distance(A__ : int , A__ ... | 12 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Optional[Any] = TypeVar('''T''')
class a ( Generic[T] ):
def __init__( self : List[str] ... | 183 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_... | 362 |
'''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 __lowercase :
'''simple... | 217 | 0 |
"""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 ...tokenization_utils_base... | 25 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 25 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class __snake_c... | 203 | """simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowercase__ = logging.get_logger(__name__)... | 203 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _lowerCamelCase ( lowerCamelCase__ ):
pass
class _lowerCamelCase :
def __init__(self , __a ) -> None:
UpperCamelCase = data
... | 153 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase__ ( _A : int = 3 ):
'''simple docstring'''
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a i... | 188 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__sna... | 78 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 78 | 1 |
import csv
import tweepy
# Twitter API credentials
__lowerCamelCase : Dict = ''''''
__lowerCamelCase : Union[str, Any] = ''''''
__lowerCamelCase : Dict = ''''''
__lowerCamelCase : List[Any] = ''''''
def _snake_case ( lowerCAmelCase : str ):
"""simple d... | 18 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | 89 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
snake_case__ : Any = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight'''... | 360 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if not is_torch_available():
... | 176 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__snake_ca... | 176 | 1 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
def merge(UpperCAmelCase , UpperCAmelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() ... | 352 | '''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
... | 214 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.