code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCAmelCase :
'''simple docstring'''
_A : Dict = None
def lowerCAmelCase ( self : Union[str, Any] ) -> str:
"""simple d... | 233 |
'''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, require_flax
... | 125 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( A , A , A ) -> Dict:
# Initiali... | 356 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase ... | 228 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> list:
'''simple docstring'''
if len(__UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(__UpperCAmelCase ) != 2 or len(b[0] ) != 2:
raise Excepti... | 56 |
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCa... | 56 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase : Any = input("""Enter image url: """).strip()
print(F"""Downloading image from {url} ...""")
lowercase : int = BeautifulSoup(requests.get(url).content, """h... | 225 |
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, ids_tensor, random_attention_mask
f... | 225 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : List[Any] ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a... | 32 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A__ ( tf.keras.layers.Layer ):
def __init__( ... | 162 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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_co... | 352 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingfac... | 87 | 0 |
'''simple docstring'''
from manim import *
class lowerCAmelCase__ ( a ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Tuple ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 ... | 267 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class l... | 267 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class a_ ( torch.nn.Module ):
def __init__( self : List[str] , lowercase : Tuple="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
su... | 147 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_tr... | 147 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Dict = ... | 42 |
"""simple docstring"""
class snake_case :
'''simple docstring'''
def __init__( self : List[str], _lowerCamelCase : list[int] ):
'''simple docstring'''
__A = len(_lowerCamelCase )
__A = [0] * len_array
if len_array > 0:
__A = ar... | 266 | 0 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get... | 365 | """simple docstring"""
import os
from pathlib import Path
def lowerCAmelCase ():
"""simple docstring"""
from torch.utils.cpp_extension import load
__UpperCamelCase =Path(__UpperCamelCase ).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
__Uppe... | 85 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
'configuration_distilbert': [
'DISTILBERT_PRETR... | 61 |
import math
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
while num > 0:
lowerCAmelCase_ = num % 8
lowerCAmelCase_ = octal + (remainder * math.floor(math.pow(10 , __lowerCAme... | 231 | 0 |
'''simple docstring'''
def _lowercase ( __A = 200 ):
'''simple docstring'''
__UpperCamelCase = [1, 2, 5, 10, 20, 50, 100, 200]
__UpperCamelCase = [0] * (pence + 1)
__UpperCamelCase = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i i... | 371 |
'''simple docstring'''
def _lowercase ( __A ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 243 | 0 |
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List,... | 96 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 0 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import... | 241 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = """"""
lowerCamelCase = 1 # (0 is vertical, 1 is horizontal)
de... | 241 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from... | 211 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at ... | 211 | 1 |
"""simple docstring"""
def a_ ( _lowercase ):
if len(_lowercase ) <= 1:
return lst
_UpperCamelCase : Union[str, Any] = 1
while i < len(_lowercase ):
if lst[i - 1] <= lst[i]:
i += 1
... | 128 |
"""simple docstring"""
def a_ ( _lowercase = 100 ):
_UpperCamelCase : List[Any] = 0
_UpperCamelCase : Optional[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
... | 128 | 1 |
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__ ( lowerCAmelCase , unittest.TestCase):
SCREAMING_SNAKE_CASE__ =... | 5 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
... | 5 | 1 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Optional[int] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :Dict , SCREAMING_... | 356 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :list[list[int]] ) -> bool:
__lowerCAmelCase : Tuple = len(SCREAMING_SNAKE_CASE )
# We need to create solution object to save path.
__lowerCAmelCase : str = [[0 for _ in ra... | 232 | 0 |
def a_ ( __lowercase : int = 100 ) -> int:
_snake_case = 0
_snake_case = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F'{solution() = }') | 282 |
def a_ ( __lowercase : str ) -> int:
_snake_case = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
_snake_case = hex_num[0] == '-'
if is_negative:
_snake_case = hex_num[1:]
try:
_snake_case = int(__lowercase ... | 282 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError("""days_between_payments mu... | 358 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase_( ... | 184 | 0 |
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():
impor... | 116 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fne... | 131 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.... | 359 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowercase__ : Optional[int] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
lowercase... | 180 | 0 |
"""simple docstring"""
A : List[Any] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple d... | 57 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from f... | 357 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : List[Any] = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
... | 46 | 0 |
"""simple docstring"""
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _lowerCamelCase ( _UpperCamelCase ... | 57 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig... | 370 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers im... | 144 | 0 |
from __future__ import annotations
from math import pow, sqrt
def snake_case_ ( snake_case , snake_case , snake_case ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must ... | 196 |
__lowerCAmelCase = range(2, 20 + 1)
__lowerCAmelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase = {}
def snake_case_ ( snake_case , snake_case , snake_case , snake_case ) -> Optional[int]:
lowercase__: ... | 196 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 364 |
'''simple docstring'''
def _lowercase ( __A = 10 ,__A = 22 ):
'''simple docstring'''
__UpperCamelCase = range(1 ,__A )
__UpperCamelCase = range(1 ,__A )
return sum(
1 for power in powers for base in bases if len(str(base**power ... | 243 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,... | 257 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmel... | 126 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'co... | 189 |
from __future__ import annotations
class a :
def __init__( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = order
# a_{0} ... a_{k}
_UpperCAmelCase : Tuple = [1.0] + [0.0] * order
... | 189 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
a_ = version.parse(version.parse(torch.__version__).base_version)... | 76 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( ... | 9 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 133 |
"""simple docstring"""
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_pipeli... | 133 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encod... | 124 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: List[str] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_M... | 276 | 0 |
"""simple docstring"""
import os
def SCREAMING_SNAKE_CASE ( ):
snake_case__ : Any = os.path.join(os.path.dirname(snake_case_ ) , "num.txt" )
with open(snake_case_ ) as file_hand:
return str(sum(int(snake_case_ ) for line in file_hand ) ... | 367 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .ben... | 286 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise... | 29 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCAmelCase__ ( lowerCamelCase_ : Any):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
lowerCAmelCase__ : Dict ... | 129 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 364 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 60 | 0 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
UpperCAmelCase : Union[str, Any] = sum(__magic_name__ ) / len(__... | 311 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
a : int = datasets.load_iris()
a : Union[str, Any] = np.array(data["data"])
a : Optional[Any] = np.array(data["target"])
a... | 311 | 1 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/hug... | 354 |
'''simple docstring'''
from __future__ import annotations
def snake_case__ ( lowerCamelCase__ : list[int] , lowerCamelCase__ : int ) -> list[int]:
A_ : int = 0
A_ : str = len(lowerCamelCase__ ) - 1
while i < ... | 4 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https:/... | 279 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common impor... | 279 | 1 |
'''simple docstring'''
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
... | 371 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_backbone_common i... | 55 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils im... | 14 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Union[str, Any] = {"""configuration_reformer""": ["""REFORMER... | 24 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging... | 24 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Dict = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 168 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .to... | 168 | 1 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def lowerCAmelCase__ ( lowerCamelCase : np.ndarray ,lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
_A : List[Any] = math.sqrt(low... | 358 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.co... | 227 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_v... | 95 |
"""simple docstring"""
from math import isqrt, loga
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase_ , UpperCamel... | 100 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, ran... | 114 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class A__ ( A__ ):
A__ = 'MCTCTFeatureExtractor'
A__ = 'AutoTokenizer'
def __init__( self : Optional[Any] , _a : Optional[int] ... | 114 | 1 |
"""simple docstring"""
def lowercase ( __snake_case : List[Any] ):
lowercase_ : int = len(UpperCamelCase_ )
for i in range(length - 1 ):
lowercase_ : List[Any] = i
for k in range(i + 1 , UpperCamelCase_ ):
if collection[k] < c... | 33 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _lowerCAmelCase ( UpperC... | 100 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 359 | """simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCAmelCase__ ( lowerCAmelCase__ :Union[str, Any] ) -> Dict:
'''simple docstring'''
if "img... | 32 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
... | 113 |
"""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_funnel import FunnelTokenizer
__UpperCamelCase = logging.ge... | 113 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowerCamelCase : Dict = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""num_... | 140 |
import math
def A_ ( _lowerCAmelCase ) -> list:
UpperCamelCase : str = [True] * n
UpperCamelCase : Optional[int] = False
UpperCamelCase : str = False
UpperCamelCase : List[Any] = True
for i in range(3 , int(n**0.5... | 140 | 1 |
"""simple docstring"""
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import ... | 57 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase_ ( _lowercase : Dict , _lowercase : str , _lowercase : str , _lowercase : Optional[Any]=1024) -> List[Any]:... | 170 | 0 |
lowerCamelCase = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_ba... | 211 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/confi... | 211 | 1 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
"""simple docstring"""
return base * power(_a , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
SCREAMING_SN... | 116 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fne... | 131 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
_... | 32 | """simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCAmelCase__ ( lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[str] ) -> Union[str, Any]:
'... | 32 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 66 |
'''simple docstring'''
import re
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_ )]
def _a( UpperCamelCase__ : str ):
... | 152 | 0 |
"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class ... | 24 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
SCREAMING_SNAKE_CASE : Tuple = None
try:
import msvcrt
except ImportError:
SCREAMING_SNAKE_CASE : List[str] = None
try:
import fcntl
except ImportError:
SC... | 24 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Any = {
'microsoft/wavlm-base': 'https://huggingface.... | 48 |
def A ( _SCREAMING_SNAKE_CASE ) -> list:
if n_term == "":
return []
lowerCamelCase : list = []
for temp in range(int(_SCREAMING_SNAKE_CASE ) ):
series.append(f'''1/{temp + 1}''' if series else "1" )
return s... | 48 | 1 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRob... | 366 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__SCREAMING_SNAKE_CASE :Any = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the ... | 156 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 37 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_t... | 312 | 0 |
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 import TokenizerTesterMixin
@require_toke... | 319 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
a... | 319 | 1 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
lowerCAmelCase__ :Tuple = [1]
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ :str = 0, 0, 0
lowerCAmelCase__ :List[Any] = ... | 293 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available(... | 293 | 1 |
import sys
import turtle
def __UpperCAmelCase ( __a : tuple[float, float] ,__a : tuple[float, float] ) -> tuple[float, float]:
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __UpperCAmelCase ( __a :... | 15 |
def __UpperCAmelCase ( __a : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__a ,__a ):
return 0
elif n == 2:
return 1
else:
_a : Any = [0, 1]
for i in range(2 ,n + 1 ... | 15 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase_ :
"""simple docstring"""
__UpperCamelCase : int
__UpperCamelCase : TreeNode | None ... | 25 |
"""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 FeatureExtractionMixin, PreTrainedTo... | 238 | 0 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_A = [file for file in filepaths i... | 205 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ ... | 205 | 1 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
_lowerCAmelCase = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def _SCREAMING_SNAKE_CASE... | 37 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[str] = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'CLI... | 24 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase = 1, lowerCamelCase = 1, lowerCamelCase = 1.0e4, lowerCamelCase = False, lowerCamelCase = 1.0, ):
assert timesteps.ndim == 1, "Timesteps sho... | 357 |
from collections.abc import Iterable
from typing import Generic, TypeVar
_UpperCAmelCase : Any = TypeVar("_T")
class __lowerCAmelCase ( Generic[_T]):
def __init__( self: Union[str, Any] , _lowerCAmelCase: Iterable[_T] | None = None ):
lowercase ... | 158 | 0 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : int , __magic_name__ : list ) -> int:
"""simple docstring"""
_enforce_args(__magic_name__ , __magic_name__ )
if n == 0:
return 0
UpperCamelCase :List[Any] = float("""-inf""" )
for i in range(1 , ... | 38 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid in... | 321 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ ... | 155 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
... | 155 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface impo... | 19 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstri... | 177 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __lowercase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : int , ):
UpperCamelCase_... | 353 | import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any]=1024 ... | 50 | 0 |
from PIL import Image
def _UpperCAmelCase ( snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
def contrast(snake_case ) -> int:
return int(1_28 + factor * (c - 1_28) )
return img.... | 82 |
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 FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
... | 137 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__UpperCamelCase : Optional[int] = datasets.load_iris()
__UpperCamelCase : List[Any] = np.array(data["data"])
__UpperCamelCase : Union[str, Any] = ... | 358 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCamelCase : Union[str, Any] = (720, 1_280) # Height, Width
__UpperCamelCase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
__Upp... | 347 | 0 |
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
UpperCAmelCase : str =logging.get_logger(__name__)
UpperCAmelCase : List[A... | 128 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
UpperCamelCase_ , UpperCamelCase_ = coefficie... | 128 | 1 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as tr... | 163 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def a__ ( __lowerca... | 163 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_do... | 97 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils imp... | 138 | 0 |
"""simple docstring"""
class __a :
def __init__( self , a__ , a__ , a__ ):
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = graph
self._normalize_graph(a__ , a__ )
_low... | 80 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_500_000 )-> int:
_lowerCamelCase = defaultdict(snake_case )
_lowerCamelCase = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
... | 80 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A_ : Dict = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the r... | 333 |
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,
ImageInput,
PILIm... | 333 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (__snake... | 216 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeniza... | 216 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 228 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) !=... | 359 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase : str = {1: (1, 1), 2: (2, 1), 3: (3, 1),... | 320 | 0 |
'''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,
WavaVeca... | 42 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
f... | 298 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 275 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
__magic_name__ : int = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCAmelCase )
if nu... | 275 | 1 |
'''simple docstring'''
import operator
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = False , lowerCAmelCase__ = None ) -> list:
UpperCAmelCase__ : List[Any] = operator.lt if reverse else operator.gt
UpperCAmelCase__ : str = ... | 181 |
'''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 impor... | 181 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torc... | 352 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __a ( _UpperCamelCase: Tuple ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = os.path.j... | 142 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__ ( snake_case_ :Union[str, Any] , snake_case_ ... | 332 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_lowercase : List[str] = logging.get_logger(__name__)
def lowercase__ ( snake_case_ :Union[tf.Tensor, np.ndarray] ):
if isins... | 332 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCamelCase ( a_ , a_ ) -> int:
lowerCAmelCase_ = args.l... | 353 |
from __future__ import annotations
import queue
class a_ :
'''simple docstring'''
def __init__( self , lowercase_ ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ = data
... | 14 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( __a : Union[... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 357 | import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
... | 192 | 0 |
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)
__a :List[str] = logging.getLogger()
de... | 312 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
fr... | 312 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
... | 186 |
"""simple docstring"""
import os
import sys
_UpperCamelCase : str = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Auto... | 186 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
Bnb... | 3 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''Yi... | 319 | 0 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return False
A__ = ... | 364 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : list[tuple[float, float]] )-> Optional[int]:
'''simple docstring'''
... | 282 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataS... | 29 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class __A (snake_case__):
'''simple docstring'''
def __init__( self : ... | 347 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__UpperCamelCase : Tuple = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( folder_based_bui... | 358 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__UpperCamelCase : Tuple = loggi... | 74 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 313 |
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
a__ : Any = logging.... | 313 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = JukeboxTokenizer
UpperCAmelCase__ = {
'''artist''': '... | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ = "" ) -> dict[str, float]:
"""simple docstring"""
A__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
A__ = ... | 231 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_snake_case = '\\n@misc{chen2021eval... | 294 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.jso... | 88 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 88 |
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = """docs/source/en/_toctree.yml"""
def SCREAMING_SNAKE_CASE__ ( __a ):
snake_case_ : List[Any] = defaultdict(__a )
snake_case_ : Optional[Any] = []
... | 88 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import e... | 71 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 1 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "... | 326 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def lowercase ( _SCREAMING_SNAKE_CASE : np.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = np.shape(_SCREAMING_SNAKE_CASE )
if... | 326 | 1 |
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