code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
lowerCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class A ( UpperCamelCase_ ):
def __in... | 464 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = '▁'
lowerCamelCase = {'vocab_file': 'pr... | 464 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : Optional[int] , lowercase : Optional[Any] ) ->Optional[Any]:
"""simple docstring"""
if not (isinstance(lowercase , lowercase ) and isinstance(lowercase , lowercase )):... | 717 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __A ( a ):
"""simple docstring"""
A_ = ''
A_ ... | 318 | 0 |
"""simple docstring"""
def _A( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
A__ : Union[str, Any] = len(lowerCAmelCase )
A__ : Tuple = [[0] * n for i in range(lowerCAmelCase )]
for i in range(lowerCAmelCase ):
... | 363 | """simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __UpperCAmelCase (unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self ):
... | 363 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Union[str, Any] = {
'''configuratio... | 711 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {}
class lowerCamelCase ( _lowerCamelCase ):
'''simple docstring'''
UpperCamelCase__ ... | 501 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ = 1_000_000 ):
lowerCamelCase_ = set(range(3 ,lowerCAmelCase__ ,2 ) )
primes.add(2 )
for p in range(3 ,lowerCAmelCase__ ,2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p... | 29 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf ... | 366 | 0 |
UpperCAmelCase_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : dict , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMIN... | 664 |
# 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 appli... | 664 | 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , lowerCAmelCase ... | 169 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowerCAmelCase: Optional[int] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowerCAmelCase: O... | 526 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = log... | 476 | from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=_a ):
_SCREAMING_SNAKE_CASE : Optional[int] =["""transformers""", """torch""", """note_seq"""]
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ )... | 476 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCas... | 561 |
'''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_QUESTION_ANSWERING_MAPPING
f... | 561 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines... | 575 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 575 | 1 |
"""simple docstring"""
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [int(snake_case__ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(snake_case__ ) == 4 and all(0 <= int(snake_case__ ) <= 2_5... | 196 |
"""simple docstring"""
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A_ : Union[str, Any] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
... | 196 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 705 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
fro... | 305 | 0 |
'''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', quiet=True)
def SCREAMING_SNAKE_CA... | 418 | # limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class a__ ( __snake_case ):
def __init__( self , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
super().__ini... | 559 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils ... | 713 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple reposi... | 444 | 0 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: list ):
"""simple docstring"""
_lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
_lowerCAmelCase = ... | 580 |
"""simple docstring"""
import math
def __snake_case ( SCREAMING_SNAKE_CASE: float , SCREAMING_SNAKE_CASE: float ):
"""simple docstring"""
if (
not isinstance(SCREAMING_SNAKE_CASE , (int, float) )
or power_facto... | 580 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =(DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ =(("""eta""", 0.0), ("""num_inference... | 214 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case : Optional[int] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE... | 214 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwri... | 44 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : List[str] ="""Speech2TextFeatureExtractor"""
__Up... | 636 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""... | 233 |
'''simple docstring'''
import argparse
import struct
import unittest
class __lowerCAmelCase:
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : bytes ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ :str = data
# ... | 233 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowercase__ ( ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCa... | 373 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
SCREAMING_SNAKE_CASE_ = datasets.load_iris()
SCREAMING_SNAKE_CASE_ = np.array(data['''data'''])
SCREAMING_SNAKE_CASE_ ... | 373 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import P... | 706 |
def _lowerCAmelCase ( __magic_name__ :int = 1_0_0 ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 407 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Sessio... | 104 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class _lowerCAmelCase ( __snake_case ):
'''si... | 585 | 0 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _SCREAMING_SNAKE_CASE (unittest.TestCase ):
def lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
s... | 718 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def __UpperCAmelCase ( UpperCamelCase__ :Iterable[str] , UpperCamelCase__ :int ) -> Generator[tuple[str, ...], None, None]:
snake_case__ : Union[str, Any] ... | 574 | 0 |
import qiskit
def _SCREAMING_SNAKE_CASE ( __snake_case = 2 ) -> qiskit.result.counts.Counts:
_UpperCAmelCase = qubits
# Using Aer's simulator
_UpperCAmelCase = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting ... | 108 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_col... | 249 | 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, NewType, Optio... | 527 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
a : Optional[int] = datasets.logging.get_logger(__name__)
a : Tuple = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Di... | 527 | 1 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int )-> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
__snake_case = gray_code_seq... | 24 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 298 | 0 |
from math import factorial, pi
def snake_case__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 30 ) -> float:
if not isinstance(__SCREAMING_SNAKE_CASE , (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not isinstance(__SCRE... | 23 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING... | 23 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( UpperCamelCase__):
_lowe... | 563 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
UpperCAmelCase : Tuple = """scheduler_config.json"""
... | 563 | 1 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _lowerCamelCase ( lowercase : str , lowercase : complex , lowercase : str = "x" , lowercase : float = 10**-10 , lowercase ... | 521 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> int:
return 1 if input_a == input_a else 0
def _lowerCamelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) =... | 521 | 1 |
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 : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase ... | 214 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 214 | 1 |
from ...configuration_utils import PretrainedConfig
__lowerCAmelCase : Dict = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-wtq': (
'https://huggingface.co/google/tapas... | 662 |
def __magic_name__ ( A : int, A : int, A : int ):
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
a = _modexpt(A, exponent // 2, A ) % modulo_value
return (x * x) % modulo_value
else... | 662 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__lowercase = parse(importlib.metadata.version('''torch'''))
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_... | 167 | from string import ascii_uppercase
__lowercase = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise T... | 167 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = f"""Input value of [number={number}] must be an integer"... | 13 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __UpperCAmelCase = "AAPL" ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__SCREAMI... | 13 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( a__: Any , a__: Optional[int] ) -> List[Any]:
'''simple docstring'''
print(F'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(UpperCAmelCase__ ):
prin... | 618 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = HfArgumentParser(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclas... | 605 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.... | 482 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int ) -> List[Any]:
"""simple docstring"""
A_ = {}
... | 482 | 1 |
"""simple docstring"""
import math
class lowercase :
def __init__(self : Any ,SCREAMING_SNAKE_CASE_ : str=0 ) -> Dict: # a graph with Node 0,1,...,N-1
"""simple docstring"""
lowerCAmelCase = n
lowerCAmelCase = [
[math.inf for j... | 535 |
"""simple docstring"""
UpperCAmelCase = 8.3_144_598
def __magic_name__ ( _lowerCamelCase: float, _lowerCamelCase: float ) -> float:
'''simple docstring'''
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exce... | 535 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : List[Any] = "The quick brown fox jumps over the lazy dog" , ):
UpperCAmelCase : Any = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : str = input_str.replace(""" """ , """""" )
for alpha i... | 707 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_m... | 359 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase = 400_0000 ):
lowercase__ : List[Any] = [0, 1]
lowercase__ : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowercase__ : ... | 152 | '''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
... | 152 | 1 |
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int: # noqa: E741
"""simple docstring"""
while r - l > 1:
_SCREAMING_SNAKE_CASE = (l + r) // 2
... | 704 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE ... | 0 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transforme... | 237 |
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None ) -> Any:
'''simple docstring'''
a__ : Dict =data
a__ : str =previous
... | 563 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
lowercase__ :Any = logging.getLogger(__name__)
lowercase__ :Optional[int] = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-... | 374 |
"""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
lowercase__ :int = 'sr... | 374 | 1 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase_ : Tuple = 1.6021E-19 # units = C
def __A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,) -> tuple[str, float]:
'''simple docstring'''
if (conductivity, electron_conc, mo... | 435 | import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCamelCase__ = 4
UpperCamelCase__ = 3
class A ( UpperCAmelCas... | 486 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a : Tuple = {'configuration_v... | 717 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
r... | 200 | 0 |
"""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_stagin... | 680 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weig... | 217 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https:... | 313 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available(... | 313 | 1 |
'''simple docstring'''
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from t... | 3 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
f... | 255 | 0 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
a_ :int = logging.get_logger(__name__)
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
def __init__( self : Any, *_snake_case :... | 243 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ :str = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBlipQFormerConfig",
"Ins... | 243 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self: int ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = [10, 20, 3... | 221 | import os
def lowerCAmelCase_ ( __A = "matrix.txt" ) -> int:
'''simple docstring'''
with open(os.path.join(os.path.dirname(__A ), __A ) ) as in_file:
UpperCAmelCase__ = in_file.read()
UpperCAmelCase__ = [[int(_... | 486 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _lowerCAmelCase ( *UpperCamelCase__: Optional[int] , UpperCamelCase__: Optional[Union[Dict, Any]] = None , UpperCamelCase__: int=True , UpperCamelCase_... | 712 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : Any = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 546 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ... | 42 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
... | 42 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : int ) ->int:
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
... | 318 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : int ) ->int:
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
... | 318 | 1 |
import operator
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase = False , lowerCamelCase = None ):
__magic_name__ : Any =operator.lt if reverse else operator.gt
__magic_name__ : Union[str, Any] =solution or []
if not arr:... | 21 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_a : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase_ ( __UpperCamelCase ):
"""simple docstring"""
def __init__( self , *UpperCAmelCase... | 479 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCAmelCase_ : 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: E402
... | 443 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import Ge... | 443 | 1 |
from __future__ import annotations
def snake_case__ ( lowerCamelCase_ ):
if not nums:
return 0
A : Any = nums[0]
A : Any = 0
for num in nums[1:]:
A , A : Optional[int] = ... | 542 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 542 | 1 |
'''simple docstring'''
import heapq
def _lowerCAmelCase ( __snake_case : dict ) -> set[int]:
__A : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be... | 338 |
'''simple docstring'''
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
fro... | 338 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCamelCase ) )
def lowerCAmelCase_ ( __lowerCamelCas... | 81 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if "cls_toke... | 207 | 0 |
'''simple docstring'''
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class A ( a ):
def __lt__( self , snake_case_ ) -> Optional[int]:
return self[-1] < other[-1]
... | 707 |
'''simple docstring'''
class A :
def __init__( self ) -> List[str]:
_a = 0
_a = 0
_a = {}
def __lowerCAmelCase ( self , snake_case_ ) -> int:
if vertex not in self.adjacency:
... | 691 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():... | 492 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowercase ( ):
__lowerCAmelCase : Optional[int] = 9
__lowerCAmelCase : Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
... | 492 | 1 |
"""simple docstring"""
import string
import numpy
def lowercase (snake_case__ : int , snake_case__ : int ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class SCREAMING_SNAKE_C... | 529 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase (snake_case__ : dict , snake_case__ : str , snake_case__ : set , snake_case__ : set , snake_case__ : dict , snake_c... | 529 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 ... | 67 |
"""simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acc... | 506 | 0 |
'''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/LICENSE-2.0
#
# ... | 287 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a= {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailab... | 287 | 1 |
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,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 57 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case : Optional[Any] = logging.get_logger(__name__)
... | 605 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 341 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase : str = {
"""configuration_bridgetower""": [
"""BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BridgeTowerConfig""... | 341 | 1 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transfor... | 54 |
from __future__ import annotations
from typing import Any
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
if not postfix_notation:
return 0
_SCREAMING_SNAKE_CASE = {'+', '-', '*', '/'}
_SCREAMING_SNAKE_CASE = []
f... | 605 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowerCAmelCase (__A , __A , __A , __A , __A , __A , __A , __A , __A , ):
"""simple docstring"""
for nxt, d in graph[v]:
... | 352 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
lowercase_ = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a... | 352 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'sentencepiece.bpe.model'}... | 417 | import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Any ... | 417 | 1 |
"""simple docstring"""
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 : Tuple = "."
# In... | 709 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : List[str] = {
"snap-research/efficientformer-l1-300": (
"https://... | 293 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'Instruc... | 575 |
"""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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to... | 575 | 1 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
def UpperCamelCase( lowercase_ , lowercase_ ... | 161 |
def UpperCamelCase( lowercase_ = 200 ) -> int:
'''simple docstring'''
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowercase_ , pe... | 161 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class UpperCAmelCase__ ( __snake_case , unittest.TestCase ):
__snake_case : ... | 206 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require... | 206 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common... | 585 |
from __future__ import annotations
from random import choice
def _a ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return choice(__SCREAMING_SNAKE_CASE )
def _a ( __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ... | 585 | 1 |
from __future__ import annotations
def a ( A__ ) -> None:
'''simple docstring'''
create_state_space_tree(A__ , [] , 0 , [0 for i in range(len(A__ ) )] )
def a ( A__ , A__ , A__ , A__ , ) -> ... | 35 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 288 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 668 | """simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from... | 668 | 1 |
'''simple docstring'''
def lowerCamelCase ( _snake_case : str ):
'''simple docstring'''
lowercase__ = [int(_snake_case ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(_snake_case ) == 4 and all(0 <= int(_sna... | 267 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class snake_case (unittest.TestCase ):
lowerCAmelCase__ :Dict = JukeboxTokenizer
lowerCAmelCase__ :List[str] = {
... | 267 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils impo... | 54 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf... | 54 | 1 |
"""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
_lowerCamelCas... | 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[lis... | 674 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCamelCase : List[Any] = {
'google/pix2struct-textcaps-base': (
'https://huggingface.... | 715 |
'''simple docstring'''
import numpy as np
def __UpperCAmelCase ( A : np.ndarray , A : np.ndarray , A : float = 1e-12 , A : int = 1_0_0 , ) -> tuple[float, np.ndarray]:
assert np.shape(A )[0] == np.shape(A )[1]
# Ensure proper dimensionality.
assert np... | 216 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Nes... | 158 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
_A = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def A_ ( __SCREAMING_SNAKE_CASE : str = "mumbai" ) -> Generator[t... | 158 | 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_modeling_common ... | 715 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 293 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : int = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': [... | 56 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip ins... | 172 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transforme... | 721 |
'''simple docstring'''
import numpy as np
import qiskit
def _snake_case ( A = 8 , A = None ) -> str:
lowerCAmelCase__ = np.random.default_rng(seed=A )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
... | 98 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host ... | 663 |
from functools import lru_cache
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> set:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = 2
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
while i *... | 663 | 1 |
"""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 tensorflow as tf
from transforme... | 168 |
"""simple docstring"""
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... | 168 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase : str = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''X... | 36 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : Dict ={
'configura... | 206 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
C... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowerCAmel... | 347 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 454 | 0 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_commo... | 385 |
'''simple docstring'''
from __future__ import annotations
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Dict:
A_ : List[Any] = TypeError(
"""Matrices must be formed from a list of zero or m... | 385 | 1 |
from __future__ import annotations
def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> list:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = input_list[low:mid], ... | 100 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 0 |
"""simple docstring"""
snake_case = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = 0
while number:
# Increased Speed Slightly by checking every 5 digits toge... | 406 |
"""simple docstring"""
import os
import sys
import unittest
snake_case = 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... | 406 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__a = logging.get_logger(__name__)
__a = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/... | 30 |
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
__a = logging.get_logger(__name__)
__a ... | 30 | 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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_ver... | 408 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Confi... | 408 | 1 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a__ : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , *l... | 622 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _lowerCAmelCase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.ra... | 622 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( snake_case : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
"""simple docstring"""
a : Tuple = set()
# Replace all the whitespace in our sentence
a : Lis... | 610 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Tuple = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_availab... | 610 | 1 |
from __future__ import annotations
from typing import Any
class a ( __lowerCAmelCase ):
"""simple docstring"""
pass
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ ) -> None:... | 401 | from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
_SCREAMING_SNAKE_CASE = HfArgumentParser(InitializationArguments)
_SCREAMING_SNAKE_CASE = parser.parse_args()
# Load codeparrot tokenize... | 401 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/... | 471 |
'''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_configurati... | 471 | 1 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple):
_A : List[Any] = ''
_A : str = ''... | 128 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def lowerCAmelCase__ ( lowerCamelCase : Callable[[int | float], int | float] ,lowerCamelCase : int | float ,lowerCamelCase : int | float ,lowerCamelCase : int = 100 ,):
... | 128 | 1 |
import math
def _lowerCamelCase ( __A : int ) -> int:
if not isinstance(__A , __A ):
_UpperCAmelCase : List[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__A )
if number < 1:
_Upper... | 707 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.rou... | 186 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase :Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase :Dict = {
'''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/re... | 561 |
'''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
... | 561 | 1 |
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_camembert import Ca... | 721 | from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , a % b )
__lowercase = ... | 522 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_c... | 36 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
'''simple docstring'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ):
'''simple docstri... | 36 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
f... | 709 | # 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 a... | 661 | 0 |
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