code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __snake_case ( __SCREAMING_SNAKE_CASE )... | 38 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 38 | 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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils impo... | 707 |
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__... | 57 | 0 |
"""simple docstring"""
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 ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> ... | 482 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class A__:
def __init__( self : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
... | 482 | 1 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info... | 678 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , A ) -> Tuple:
'''simple docstring'''
__magic_name__ = list_of_points
# Degree det... | 678 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''si... | 55 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase : List[Any] = logging.get_l... | 509 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : int = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicals... | 710 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 | 0 |
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():
from PIL import Image
from ..image_util... | 80 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 245 | 0 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ : Tuple = ... | 178 |
'''simple docstring'''
UpperCamelCase__ : List[str] = '''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,
)
f... | 178 | 1 |
'''simple docstring'''
# 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 hav... | 372 |
'''simple docstring'''
import baseaa
def _A ( A ) -> bytes:
return baseaa.aaaencode(string.encode("utf-8" ) )
def _A ( A ) -> str:
return baseaa.aaadecode(A ).decode("utf-8" )
if __name__ == "__main__":
import doctest
doctest.testmod(... | 372 | 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,
WavaVecaCTCTokenizer,
WavaVec... | 112 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase = 0b1011_0011_1110_1100_1001_0000_0111_1011_... | 112 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils... | 180 |
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( snake_case__ , snake_case__ ):
if len(snake_case__ ) == 0:
return False
A_ : Union[str, Any] = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a_li... | 180 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 436 |
import math
def __magic_name__ ( lowercase ) -> bool:
"""simple docstring"""
lowercase_ : Optional[Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase ... | 436 | 1 |
def _a ( lowercase__ : int , lowercase__ : int ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def _a ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert ... | 85 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase : int = logging.getLogger(__name__)
class _a (a__ ):
'''simple docstring'''
lowerCAmelCase_ : Union[str, A... | 116 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import f... | 88 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[Any]=None ):
'''simple docstring'''
... | 88 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def A ( lowercase__ : int ) -> str:
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined for non-natural numbers""" )... | 45 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ... | 45 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if index == r:
for j in range(SCREAMING_SNAKE_CASE__ ):
... | 693 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _UpperCAmelCase ( ):
'''simple docstring'''
with offline(O... | 693 | 1 |
from math import sqrt
def UpperCamelCase ( _a ) -> int:
'''simple docstring'''
lowercase_ :Any = 0
for i in range(1 , int(sqrt(_a ) + 1 ) ):
if n % i == 0 and i != sqrt(_a ):
total += i + n // i
... | 257 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
SCREAMING_SNAKE_CASE : Any = False
try:
SC... | 257 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}
try:
if not is_to... | 704 | import qiskit
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Dict = qiskit.Aer.get_backend('''aer_simulator''' )
__UpperCamelCase :Tuple = qiskit.QuantumCircuit(4 , 2 )
# encode in... | 452 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import F... | 4 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
SCREAMING_SNAKE_CASE_ = logging.get_logger(... | 237 | 0 |
import inspect
import unittest
from transformers import YolosConfig
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 ConfigTester
from ...tes... | 715 | from __future__ import annotations
lowerCamelCase_ : List[Any] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"... | 246 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_UpperCAmelCase : List[str] = 3
def snake_case__ ( UpperCamelCase ) -> int:
print('''Generating primitive root of p''' )
while True:
_Upp... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common imp... | 396 |
"""simple docstring"""
def A_ ( __UpperCamelCase : list ):
for i in range(len(__UpperCamelCase ) - 1 , 0 , -1 ):
lowercase = False
for j in range(__UpperCamelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lowe... | 396 | 1 |
_lowerCAmelCase: dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
_lowerCAmelCase: dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def _lowercase( __a ... | 20 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Optional[Any] ) -> Dict:
return getitem, k
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CAS... | 504 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : str = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
'''XLMR... | 316 | from __future__ import annotations
def _snake_case ( lowerCAmelCase : list[int] ): # This function is recursive
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = len(lowerCAmelCase )
# If the array contains only one element, we return it (it's the stop condi... | 316 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: int , lowerCAmelCase_: list[list[int]] ):
def update_area_of_max_square(lowerCAmelCase_: int , lowerCAmelCase_: int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
... | 666 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
... | 447 | 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
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCam... | 710 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVe... | 601 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_UpperCAmelCase : Optional[int] = logging.get... | 107 |
'''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 FeatureExtracti... | 207 | 0 |
from __future__ import annotations
import bisect
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ = 0, lowerCAmelCase_ = -1 ):
"""simple docstring"""
if hi < 0:
SCREAMING_SNAKE_CASE =len(lowerCAmelCase_ )
while lo < ... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase ={
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "Perce... | 252 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 100 , ):
"""simple docstring"""
__magic_na... | 436 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_dev... | 204 | 0 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 |
import math
import unittest
from transformers import BioGptConfig, 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 ModelTe... | 675 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase_ ( _lowerCamelCase : int , _lowerCamelCase : int ):
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def lowerCa... | 142 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase ( a ):
"""simple docstring"""
__lowercase :Optional[int] = ["image_processor", "tokenizer"]
__low... | 142 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase_ = '''\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Ama... | 718 |
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar('''_T''')
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ = None ):
"""simple docstring"""
... | 519 | 0 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __A :
'''simple docstring'''
def a__ (self , A ) -> Optional[int]:
"""simple docstring"""
raise... | 11 |
import os
import sys
import unittest
__a: Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_map... | 108 | 0 |
# 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 install -e .[dev]' when switching... | 705 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord impor... | 336 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:str = list(range(len(snake_case ) ) )
SCREAMING_SNAKE_CASE:Any = [v / w for v, w in zip(snake_case , snake_ca... | 143 | """simple docstring"""
from __future__ import annotations
def _A( lowerCAmelCase ):
if len(lowerCAmelCase ) == 0:
return []
A__ , A__ : Dict = min(lowerCAmelCase ), max(lowerCAmelCase )
A__ : List[Any] = int(... | 363 | 0 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_snake_case : List[Any] = """"""
_snake_case : Union[str, Any] = """"""
_snake_case : int = """"""
_snake_case : Optional[Any] = 1 # (0 is vertical, ... | 493 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _a ( _SCREAMING_SNAKE_CASE : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(_SCREAMING_SNAKE_CASE : float ,... | 493 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 264 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase (__snake_case ):
def __init__( s... | 264 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from... | 505 |
"""simple docstring"""
import os
import unicodedata
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 ... | 505 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : List[str] = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCH... | 439 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import... | 12 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
snake_case_ : Union[str, Any] = [
os.path.join(os.path.dirname(__file__), dirname)
for di... | 709 |
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : Optional[int]):
UpperCamelCase = []
UpperCamelCase = []
UpperCamelCase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} ... | 350 | 0 |
'''simple docstring'''
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
a__ = (0, 0)
a__ = None
a__ = 0
... | 394 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
'Salesforce/blip-vqa-base': 'https://huggingface.co... | 394 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def _A( UpperCamelCase__ : Iterable[str] , UpperCamelCase__ : int ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
__lowercase = iter(UpperCamelCas... | 362 |
import argparse
import copy
def _A( UpperCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__lowercase = {}
with open(UpperCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbour... | 362 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''facebook/wav2vec2-base-960h''': '''https:... | 673 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import c... | 673 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : str ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 28 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
Mobi... | 28 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : List[Any] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_available():
raise Op... | 402 | 0 |
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase, __UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ =(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCa... | 720 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
... | 588 | 0 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase_ : List[Any] = {
# 1536-bit
5: {
'''prime''': int(
... | 17 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def __A ( a_ : List[Any] ):
lowerCAmelCase : Any = tf.convert_to_tensor(a_ )
lowerCAmelCase : List[Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.... | 525 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if not sentence:
return ""
snake_case_ : List[str] = dict(zip(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE ... | 92 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
a_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or mu... | 92 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase__ ( lowerCamelCase_) -> Union[str, Any]:
raise No... | 34 |
def UpperCamelCase_( _A :list[list] )-> list[list]:
UpperCamelCase__ = current_set.copy()
for row_index, row in enumerate(_A ):
UpperCamelCase__ = row[0]
for column_index, column in enumerate(_A ):
if magnitude == 0:
UpperCamelCase__ = ... | 551 | 0 |
import csv
import tweepy
# Twitter API credentials
_SCREAMING_SNAKE_CASE : Tuple = ''
_SCREAMING_SNAKE_CASE : Any = ''
_SCREAMING_SNAKE_CASE : Tuple = ''
_SCREAMING_SNAKE_CASE : Dict = ''
def SCREAMING_SNAKE_CASE ( __UpperCamel... | 55 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
_SCREAMING_SNAKE_CASE : Union[str, Any] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\... | 55 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common impo... | 65 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowercase_ ( __snake_case : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if (
(... | 241 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Optional[int] = ['''flax''']
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> List[str]:
... | 720 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impo... | 160 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 171 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tr... | 56 | 0 |
from __future__ import annotations
class lowerCamelCase__ :
def __init__(self : Union[str, Any] , _snake_case : str , _snake_case : str ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ , lowerCamelCase_ : Tuple ... | 144 |
from PIL import Image
def _a ( lowerCamelCase__ , lowerCamelCase__ ) -> Image:
def brightness(lowerCamelCase__ ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' ... | 144 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> list:
'''simple docstring'''
_lowerCamelCase : Tuple = []
_lowerCamelCase, _lowerCamelC... | 46 |
from math import factorial
UpperCAmelCase : Tuple = {str(d): factorial(d) for d in range(10)}
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(SCREAMING_SNAKE_CASE ) )
def _A ( ):
... | 563 | 0 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVis... | 463 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ,A__ ):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
UpperCAmelCase_ : Lis... | 463 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
f... | 43 |
import cva
import numpy as np
class _A :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if k in (0.04, 0.06):
SCREAMING_SNAKE_CASE_ : Any = k
SCREAMING_SNAKE_CASE_ ... | 511 | 0 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vis... | 720 | from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowercase = datasets.load_iris()
__lowercase = np.array(data['''data'''])
__lowercase = np.array(data['''target'''])
__lowercase ... | 452 | 0 |
'''simple docstring'''
import math
def a_ ( _lowerCAmelCase ,_lowerCAmelCase = 0 ,_lowerCAmelCase = 0 ) -> list:
__lowerCamelCase : List[Any] = end or len(_lowerCAmelCase )
for i in range(_lowerCAmelCase ,_lowerCAmelCase ):
__lowerCamelCase : Optio... | 459 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {'vocab_file': 'vocab.json'}
_Uppe... | 459 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],... | 478 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_earl... | 478 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/c... | 32 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __magic... | 358 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.p... | 715 |
from collections.abc import Generator
def A ( ) -> Generator[int, None, None]:
UpperCamelCase__ , UpperCamelCase__ :str = 0, 1
while True:
UpperCamelCase__ , UpperCamelCase__ :Tuple = b, a + b
yield b
def A ( lowercase__ : int =... | 383 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__snake_case = logging.get_logger(__name__)
class _a ( _lowerCAmelCase ):
"""simple docstring"""
def __init__( self : List[Any] , *lowerc... | 451 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Dict =logging.get_logger(__name__)
lowerCAmelCase : List[Any] ... | 172 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 430 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _UpperCAmelCase ( __lowerCamelCase : Optional[int] ) -> Dict:
_snake_case = [
'''decoder.version''',
... | 430 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case :
UpperCAmelCase__ = field(
metadata={''... | 626 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class snake_case :
def __init__(self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4]
def... | 626 | 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 .tokenizat... | 332 |
from math import pow, sqrt
def a__ (*__lowercase :float ) -> bool:
_A : List[str] = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def a__ (__lowercase :float , __lowercase :float ... | 332 | 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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampl... | 302 |
from cva import destroyAllWindows, imread, imshow, waitKey
def A_ ( A__ ) -> Tuple:
# getting number of pixels in the image
a__ , a__ : Any = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(A__... | 302 | 1 |
"""simple docstring"""
import numpy as np
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709 |
"""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 model through reduction of a normal pre-trained model, but ... | 168 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_... | 38 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common... | 562 | 0 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : Tuple = TypeVar('''T''')
class lowerCamelCase_( Generic[T] ):
'''simple docstring'''
lowercase__ : ... | 713 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 )... | 623 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
Wav... | 24 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPip... | 24 | 1 |
from datetime import datetime as dt
import os
from github import Github
_a = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def snake_case__ ( ):
lowerCAmelCase__ :Dict... | 719 |
from __future__ import annotations
import numpy as np
def snake_case__ ( UpperCAmelCase : np.ndarray ):
lowerCAmelCase__ ,lowerCAmelCase__ :List[str] = np.shape(UpperCAmelCase )
if rows != columns:
lowerCAmelCase__ :Tuple ... | 111 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase , _lowercase = None , _lowercase = None , _lowercase = False , ) -> tuple[int, float, str]:
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = ciph... | 422 |
'''simple docstring'''
def lowercase_ ( _lowercase ) -> set:
'''simple docstring'''
lowerCamelCase_ : str = set()
# edges = list of graph's edges
lowerCamelCase_ : Optional[Any] = get_edges(_lowercase )
# While there are still elements in edges list, ta... | 422 | 1 |
"""simple docstring"""
import sys
UpperCAmelCase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66... | 711 | """simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCAmelCase = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": operator.ge,
"""... | 342 | 0 |
'''simple docstring'''
import os
def __A ( ) -> List[Any]:
'''simple docstring'''
with open(os.path.dirname(UpperCAmelCase ) + "/grid.txt" ) as f:
_UpperCamelCase : Dict = [] # noqa: E741
for _ in range(2_0 )... | 435 | '''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase_ : List[str] = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a compa... | 435 | 1 |
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_common import ConfigTester
from ...test_modeling_co... | 682 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = '▁'
_A = {
... | 682 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase : Any = {
'configuratio... | 241 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
],
}
try:... | 73 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_avail... | 266 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin,... | 266 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__snake_case :Any ={
# 1536-bit
5: {
'prime': int(
'FFFFF... | 106 | '''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( snake_case_ : Union[dict, list, ... | 78 | 0 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , ):
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 val... | 484 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_ : Tuple = 'src/transformers'
a_ : Any = '... | 484 | 1 |
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... | 413 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("only integers accepted as input" )
else:
lowercase__ = str(abs(SCREAMING_SNAKE_CASE_ ) )
lowercase__ = [list(SCREAMING_SNAKE_CASE_ ... | 413 | 1 |
import heapq
def A__ ( snake_case_ : dict ):
SCREAMING_SNAKE_CASE__: 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 filled like a Priority Queue
# heapq works with a min priority queue, so I used -1... | 107 | from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTi... | 107 | 1 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return round(float(moles / volume ) * nfactor )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return round(float((moles * 0.0_8... | 669 |
import math
def UpperCamelCase__( UpperCamelCase__ : int )->list:
A__ = [True] * n
A__ = False
A__ = False
A__ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
A__ = i * 2
wh... | 190 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .at... | 174 | '''simple docstring'''
import requests
__snake_case : int = 'YOUR API KEY'
def _UpperCAmelCase ( _UpperCamelCase : str, _UpperCamelCase : str = giphy_api_key ) -> list:
A_ = '''+'''.join(query.split() )
A_ = F'''https... | 174 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE( A ):
def __init__( self ,*SCREAMING_SNAKE_CA... | 498 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _SCREAMING_SNAKE_CASE( A ):
@staticmethod
@abstractmethod
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> str:
"... | 498 | 1 |
"""simple docstring"""
import sys
__lowercase : Tuple = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
... | 66 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase_ ( _lowerCamelCase : int = 8 ):
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".joi... | 66 | 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_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"facebook/lev... | 32 |
import torch
def lowerCAmelCase__ ( ) -> int:
'''simple docstring'''
if torch.cuda.is_available():
A__ = torch.cuda.device_count()
else:
A__ = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __name__ == "__main__":
... | 514 | 0 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's ... | 352 |
'''simple docstring'''
lowercase_ = 256
# Modulus to hash a string
lowercase_ = 1_000_003
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = len(__A)
_a = len(__A)
if p_len > t_len:
return False
_a ... | 352 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> List[str]:
_lowercase , _lowercase : Optional[int] = [], []
while len(lowerCamelCase_ ) > 1:
_lowercase , _lowercase : List[str] = min(lowerCamelCase_ ), max(lowerCamelCase_ )... | 89 |
'''simple docstring'''
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_to... | 316 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTest... | 718 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : Optional[Any] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenizatio... | 316 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : int = (UnCLIPScheduler,)
def lowerCAmelCase ( self : Union[str, Any] ,**SCREAMI... | 652 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Da... | 652 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytess... | 703 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowerCAmelCase ( snake_case : Union[dict, list, tuple, torch.Tensor] ) ... | 189 | 0 |
def __lowerCAmelCase ( ) -> int:
'''simple docstring'''
return 1
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __lowerCAmelCase ( _UpperCamelCase... | 439 |
import argparse
import os
import re
a_ : List[str] = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_ : Optional[Any] = re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s... | 439 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
... | 271 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorSt... | 271 | 1 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_con... | 24 |
'''simple docstring'''
def _A ( lowercase__ ):
assert (
isinstance(lowercase__ , lowercase__ ) and number_of_steps > 0
), f'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps == 1:
return 1
lowercase__ ... | 325 | 0 |
"""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_configu... | 95 |
"""simple docstring"""
import functools
def lowercase ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n... | 95 | 1 |
'''simple docstring'''
import qiskit
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
A: Union[str, Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acti... | 135 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils imp... | 135 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
SCREAMING_SNAKE_CASE_ : Tuple = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and th... | 500 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _snake_case ( UpperCAmelCase_ : int ):
A__ = 0
while number > 0:
... | 500 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.I... | 61 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCas... | 61 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
a_ : Optional[int] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
imp... | 712 |
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = []
lowerCamelCase = []
lowerCamelCase = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # P... | 484 | 0 |
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