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 |
|---|---|---|---|---|
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 57 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCAmelCase__ : Optional[Any] = 1_00
UpperCAmelCase__ : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__ : int
for prime in range(3, ceil(NUM_PRIMES**0.5)... | 48 | 0 |
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase__ : Op... | 714 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """timm_backbone"""
def __init__( self... | 194 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/m... | 578 |
from itertools import product
def A(__a: int , __a: int ):
lowerCAmelCase_ = sides_number
lowerCAmelCase_ = max_face_number * dice_number
lowerCAmelCase_ = [0] * (max_total + 1)
lowerCAmelCase_ = 1
lowerCAmelCase_ =... | 122 | 0 |
def snake_case__ ( UpperCAmelCase : float ):
return 1_0 - x * x
def snake_case__ ( UpperCAmelCase : float , UpperCAmelCase : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(UpperCAmelCase... | 111 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.... | 111 | 1 |
'''simple docstring'''
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 ... | 676 | import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked befor... | 305 | 0 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer... | 709 |
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Any ) -> int:
"""simple docstring"""
_UpperCAmelCase = {}
def lowerCamelCase ... | 402 | 0 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
'decoder.version',
... | 605 |
# 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 bet... | 605 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = [
'safety_checker/pytor... | 46 |
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 = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 1 |
'''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_vision
fro... | 48 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipelin... | 38 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"google/efficientnet-b7": "https... | 717 |
import csv
import tweepy
# Twitter API credentials
A = ""
A = ""
A = ""
A = ""
def __UpperCAmelCase ( __A ) -> None:
'''simple docstring'''
UpperCAmelCase__ = tweepy.OAuthHandler(__A , __A )
auth... | 277 | 0 |
def lowercase ( __A : list ) -> list:
'''simple docstring'''
if len(__A ) <= 1:
return lst
snake_case : List[Any] = 1
while i < len(__A ):
if lst[i - 1] <= lst[i]:
i += 1
else:
snake_case , snake_case... | 36 | '''simple docstring'''
import numpy as np
def __A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase = 1E-12 ,UpperCAmelCase = 1_0_0 ,) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(UpperCAmelCase )[0] == np.shape(UpperCAmelCase )[1]
... | 435 | 0 |
def _lowerCamelCase ( snake_case ):
if n == 1 or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
_lowerCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
... | 706 | import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
... | 225 | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"{price_plus_tax(100, 0.2_5) = }")
print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) ... | 27 |
import requests
_SCREAMING_SNAKE_CASE : Optional[int] = "" # <-- Put your OpenWeatherMap appid here!
_SCREAMING_SNAKE_CASE : Optional[Any] = "https://api.openweathermap.org/data/2.5/"
def UpperCAmelCase__ (UpperCamelCase_ = "Chicago" ,UpperCamelCase_ = APPID ... | 550 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase_ : Optional[Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
lowerCamelCase_ : Dict = 3E8 ... | 302 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 a... | 302 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
SCREAMING_S... | 99 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import ... | 400 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_... | 711 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBa... | 339 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments | 30 |
class snake_case__ :
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
"""simple docstring"""
a_ : Optional[Any] = name
a_ : Union[str, Any] = val
def __str__( self ) -> Tup... | 419 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__a : Optional[Any] = logging.get_logger(__name__)
def a_ ( __snake_case ) -> List[int]:
'''simple docstring'''
if isinstance(__snake_case... | 707 |
from __future__ import annotations
def a_ ( __snake_case ) -> list:
'''simple docstring'''
if len(__snake_case ) == 0:
return []
UpperCamelCase_ , UpperCamelCase_ = min(__snake_case ), max(__snake_case )
UpperCamelCase_ ... | 559 | 0 |
'''simple docstring'''
import operator as op
def _a ( _lowerCamelCase ) -> Tuple:
"""simple docstring"""
__snake_case : List[Any] = []
__snake_case : Optional[int] = lambda _lowerCamelCase ... | 26 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_config... | 394 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_A... | 599 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCamelCase_ = "<<<<<<< This should probably be modified because it mentions: "
UpperCa... | 599 | 1 |
def a_ ( __lowercase : int = 1_000 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 686 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # noqa: F401 # Here to have a nice m... | 686 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from tran... | 714 | 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 | 0 |
"""simple docstring"""
from math import factorial
def _lowercase ( __lowerCAmelCase = 100 ) -> int:
return sum(int(__lowerCAmelCase ) for x in str(factorial(__lowerCAmelCase ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))... | 680 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 680 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from... | 714 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgument... | 128 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : int = logging.get_logger(__name__)
a : int = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class _lowercase ( UpperCAmelCase__ ):
'''simple ... | 613 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : List[str] = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class _lowercase ( Upper... | 613 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class A__ :
"""simple docstring"""
def __init__( self : int ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase ... | 720 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__a = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftF... | 257 | 0 |
"""simple docstring"""
import operator
def _snake_case ( _snake_case : str , _snake_case : List[str] = False , _snake_case : Optional[Any] = None ) -> list:
'''simple docstring'''
_A = operator.lt if reverse else operator.gt
_A ... | 7 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=Fal... | 205 | 0 |
"""simple docstring"""
import random
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase = False ):
"""simple docstring"""
_UpperCAmelCase = {i: [] for i in range(_lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if proba... | 700 | """simple docstring"""
from __future__ import annotations
from random import choice
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
return choice(lowercase )
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase ... | 275 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'spiece.model... | 685 |
'''simple docstring'''
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_commo... | 634 | 0 |
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
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : ... | 214 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : List[str] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'huggingface/time-series-transformer-tourism-mon... | 214 | 1 |
'''simple docstring'''
import numpy as np
import datasets
UpperCamelCase_ = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
... | 92 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : Optional[Any] = ... | 57 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : List[Any] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
t... | 703 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available()... | 445 | 0 |
import argparse
import os
import re
import packaging.version
UpperCAmelCase_ = """examples/"""
UpperCAmelCase_ = {
"""examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(r"""^__vers... | 2 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""vocab_file""": """vocab.json""",
"""tokenizer_config... | 2 | 1 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def A ... | 708 |
"""simple docstring"""
import string
import numpy
def A ( snake_case :int , snake_case :int ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , snake_case )
class __lowerCAmelCase :
lowercase = string.ascii_uppercase + string.digits
#... | 293 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
a__ : str = """<<<<<<< This should probably be modified because it mentions: """
a__ : Dict = ... | 165 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import Onnx... | 165 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _lowercase ( SCREAMING_SNAKE_CASE_ : dict ):
... | 181 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_en... | 181 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : str = {
'''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_... | 397 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a : Dict = ... | 397 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_... | 474 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 474 | 1 |
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 transformers.models.wavaveca im... | 55 |
'''simple docstring'''
def __UpperCamelCase ( a : int = 50 ) ->int:
snake_case = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_len... | 342 | 0 |
"""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 torchvisi... | 507 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate... | 507 | 1 |
"""simple docstring"""
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,
)
lowercase__ = {
"""configuration... | 610 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ... | 610 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a :
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Collection[float] | None = None ):
if components is None:
... | 707 |
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 ...test_mod... | 189 | 0 |
from __future__ import annotations
def UpperCamelCase_ ( __a ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(__a ) ... | 37 |
def A__ (snake_case : int ) -> bool:
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
_snak... | 658 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsof... | 658 | 1 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> Optional[int]:
# Checks if the entire collection has been sorted
if len(lowerCAmelCase_ ) <= 1 or n <= 1:
return
insert_next(lowerCAmelCase_ , n - 1 )
... | 468 |
def _UpperCamelCase ( lowerCAmelCase_ ) ->int:
if n == 1 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return 0
elif n == 2:
return 1
else:
UpperCAmelCase = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + s... | 377 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class UpperCAmelCase_ ( unittest.TestCase):
lowerCamelCase__ = inspect.getfi... | 712 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_lowerCAmelCase : str = BeautifulSoup(requests.get(_lowerCamelCase ).text , "html.parser"... | 658 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Any = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_SCREAMING_SNAKE_CASE : int = ""
_SCREAMING_SNAKE_CASE : Any = ""
# ... | 249 |
def lowerCamelCase__ (__lowerCamelCase = 50 ):
_SCREAMING_SNAKE_CASE : Dict = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start in range(row_lengt... | 249 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
__UpperCAmelCase : int
__UpperCAmelCase : in... | 721 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_si... | 65 | 0 |
'''simple docstring'''
import argparse
_lowercase : Optional[int] = """docs/source/_static/js/custom.js"""
def lowerCamelCase__ ( A : List[str] ):
'''simple docstring'''
with open(A , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase ... | 210 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__( unittest.TestCa... | 210 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 534 |
from statistics import mean
import numpy as np
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a ):
snake_case_ : Optional[Any] = 0
# Number of processes finished
snake_case_ : List[str] = 0
# Displays the finished process.
# If it is 0, th... | 534 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import ... | 178 |
from collections.abc import Generator
from math import sin
def _A (UpperCamelCase : bytes ) ->bytes:
'''simple docstring'''
if len(UpperCamelCase ) != 32:
raise ValueError("""Input must be of length 32""" )
lowerCamelCase__ : Tuple = B""""""
for i in [... | 157 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_U... | 584 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a__ ( __SCREAMING_SNAKE_CASE ):
_A = ["image_processor", "tokenizer"]
_A = "CLIPImageProcessor"
_A = ("CLIPTokeni... | 584 | 1 |
'''simple docstring'''
from __future__ import annotations
a : Optional[Any] = '''Muhammad Umer Farooq'''
a : int = '''MIT'''
a : Dict = '''1.0.0'''
a : Optional[int] = '''Muhammad Umer Farooq'''
a : Optional[Any] ... | 69 |
import re
def __a ( __UpperCAmelCase : str ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def __a ( __UpperCAmelCase : str ) -> str:
"""simple docstring"""
... | 488 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
A__ : Optional[Any] =version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tok... | 499 |
'''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,
... | 499 | 1 |
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
_SCREAMING_SNAKE_CASE ... | 401 | import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import Adde... | 401 | 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_avai... | 68 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
snake_case_ = True
except (ImportError, ModuleNotFoundError):
snake_case_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt'... | 68 | 1 |
def lowerCamelCase__ (__lowerCamelCase ):
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for div... | 249 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[Any] = 'timm_backbone'
def __init__( self : List[Any] , __lower... | 277 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase__ ( snake_c... | 397 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :list[int] ):
__UpperCAmelCase = len(snake_case_ ) // 2
# choose the middle 3 elements
__UpperCAmelCase = lst[m - 1 : m + 2]
# if middle element is peak
if thre... | 397 | 1 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( a__ : int = 100 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 17 |
def _lowerCAmelCase ( __lowerCAmelCase = 200 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case__ : List[Any] = [0] * (pence + 1)
snake_case__ : str = 1 # base case: 1 w... | 252 | 0 |
'''simple docstring'''
def _lowerCamelCase( UpperCamelCase__ : list[int] , UpperCamelCase__ : int ) -> bool:
A : int = len(UpperCamelCase__ )
A : Dict = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for ea... | 537 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _lowerCamelCase( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ) -> List[Any]:
A : Any = {
... | 537 | 1 |
def __lowerCAmelCase ( A , A ):
UpperCAmelCase_ = ""
for i in table:
res += inp[i - 1]
return res
def __lowerCAmelCase ( A ):
return data[1:] + data[0]
def __lowerCAmelCase ( A , A ):
UpperCAmelCase_ = ""
for i in range(le... | 162 |
snake_case = {str(digit): digit**5 for digit in range(1_0)}
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(snake_case__ ) )
def SCREAMING_SNAKE_CASE__ ( ) -> int:
return sum(
numb... | 67 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a__ ( snake_case__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def __SCREAMING_SNAKE_CASE ( lo... | 711 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, r... | 98 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = {
"microsoft/xprophetnet-large-wiki... | 567 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Tuple = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
"asapp/sew-tiny-100k": "https://huggingface.co/as... | 567 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memor... | 718 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__a = '\\n\n'
__a = '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt... | 300 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logg... | 413 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = "src/transformers"
# This is to make sure the transformers m... | 346 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase ( snake_case__ , unittest.TestCase ):
... | 709 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils imp... | 304 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config... | 26 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 26 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeline... | 711 | import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
lowerCAmelCase : Tuple =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def A__ ( ... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_SCREAMING_SNAKE_CASE = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise Optional... | 369 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, Vi... | 369 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0 for i in range(r + 1 )]
# nc0 = 1
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , n + 1 ):
... | 553 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
return n == n[::-1]
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 ):
... | 553 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 69 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( snake_case ):
"""simple docstring"""
__magic_name__ :Optional[Any] = [
'''encoder.version''',
'''decoder.version''',
... | 0 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageIn... | 720 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
_a , _a = 9, 14 # noqa: F841
_a = [
[0, 1, 4],
... | 276 | 0 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class A ( _a ):
def __init__( self : Union[str, Any] , low... | 22 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concate... | 499 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 240 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetY... | 240 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet ... | 77 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 | 0 |
'''simple docstring'''
import itertools
import os
import re
__lowercase : List[str] = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
__lowercase : Optional[int] = re.compile(r'''([a-z\d])([A-Z])''')
__lowercase : int = re.compile(r'''(?<!_)_(?!_)''')
__lowercase : ... | 714 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : List[Any] = logging.get_logger(__name__)
__lowercase : Optional[Any] = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretra... | 315 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase... | 50 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -... | 50 | 1 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__UpperCAmelCase : Union[str, Any] = logging.getLogger(__name__)
def lowerCamelCase_ ( ):
_a : Optional[int] = argparse.ArgumentParser(
descri... | 703 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase_ ): # This function is recursive
_a : Any = len(UpperCamelCase_ )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
... | 249 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""configuration_x_clip""": [
"""XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XCLIPConfig""",
"""XCLIPTextConfig... | 0 |
import numpy as np
from PIL import Image
def _UpperCAmelCase ( UpperCAmelCase : np.ndarray , UpperCAmelCase : int , UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : List[str] = np.array(UpperCAmelCase... | 519 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceF... | 671 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A__ : Any = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
... | 671 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_a : Any = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetCo... | 56 | 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,... | 520 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/con... | 709 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils... | 331 | 0 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, 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 M... | 420 | """simple docstring"""
from collections.abc import Sequence
from queue import Queue
class UpperCAmelCase_ :
def __init__( self : Optional[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : Optional[int]=... | 420 | 1 |
import functools
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ):
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ )
@functools.cache
def min_dis... | 682 |
# 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 applic... | 682 | 1 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : list , UpperCamelCase__ : int | None = None , UpperCamelCase__ : int | None = None ):
if start is None:
_A : Dict = 0
if end is None:
_A : O... | 503 |
import math
def _UpperCAmelCase (UpperCamelCase__ : int ):
return math.sqrt(UpperCamelCase__ ) * math.sqrt(UpperCamelCase__ ) == num
def _UpperCAmelCase (UpperCamelCase__ : int ):
_A : Dict = 0
_A : Dict = n
while left <=... | 503 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[str] = logging.get_logger(__name__)
A__ : int = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24... | 706 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowercase ... | 660 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbos... | 500 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( a):
lowerCamelCase__ = (UniP... | 500 | 1 |
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 : Union[str, Any] = logging.getLogger(__name__)
if __... | 704 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import In... | 188 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
if not is... | 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 1 |
from __future__ import annotations
class snake_case__:
'''simple docstring'''
def __init__( self , __lowercase ) -> Any:
lowerCAmelCase_ : Union[str, Any] = TypeError(
'''Matrices must be formed from a list of zero or mor... | 619 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
_UpperCAmelCase : Tuple =logging.get_logger(__name__)
class snake_case__( UpperCAmelCase__ ):
... | 619 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_m... | 687 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a = """"""
a = """"""
a = """"""
a = 1 # (0 is vertical, 1 is horizontal)
def UpperCamelCase_( ):
"""simple docstring"""... | 687 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _UpperCamelCase ( unittest.TestCase ):
'''simple docst... | 670 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case_ ( _UpperCamelCase ):
... | 488 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
ex... | 112 | 0 |
'''simple docstring'''
from manim import *
class a ( _a ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ) -> Any:
__UpperCAmelCase : Tuple = Rectangle(height=0.5 , width=0.5 )
... | 266 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_trans... | 266 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractio... | 28 | def snake_case__ ( lowercase , lowercase ):
lowerCAmelCase_: list[list[str]] = [[] for _ in range(lowercase )]
lowerCAmelCase_: Optional[Any] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(lowercase ... | 613 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A__: str = logging.get_logger(__name__)
class _a ( UpperCamelCase__):
"""simple docstring"""
def __init__( self: int , *__lowerCamelCase: Optio... | 716 |
def lowerCAmelCase_ ( A_):
UpperCamelCase__: Union[str, Any] = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCAmelCase_ ( A_):
UpperCamelCase__... | 221 | 0 |
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : Optional[Any] ):
# we need a list not a string, so do something to change the type
A = arr.split(',' )
def __a ( self : ... | 690 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessi... | 690 | 1 |
from math import log
from scipy.constants import Boltzmann, physical_constants
lowerCAmelCase : List[Any] =300 # TEMPERATURE (unit = K)
def A__ ( __A , __A , __A , ):
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("""Dono... | 718 | import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (... | 15 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.... | 418 |
"""simple docstring"""
def a__ ( snake_case__ = 50_00_00_00 ) -> int:
lowerCamelCase = set()
lowerCamelCase = int((limit - 24) ** (1 / 2) )
lowerCamelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 ... | 543 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class snake_case ( _A ):
''... | 713 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 215 | 0 |
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