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 pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def UpperCamelCase_( lower... | 21 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Dict = "https://downloadgram.net/wp-json/wppress/video-downloader/video?url... | 105 | 0 |
'''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 import BackboneTest... | 229 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = str(id_ )
... | 229 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 184 |
class _lowercase :
"""simple docstring"""
def __init__( self : List[Any] , __lowerCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = size
lowerCamelCase__ : Lis... | 184 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class ... | 370 |
from scipy.stats import spearmanr
import datasets
_snake_case : Optional[Any] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive ... | 134 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__lowerCamelCase : Optional[Any] = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 52 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | 89 | 0 |
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 OnnxConf... | 370 | 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
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditi... | 18 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
import sys
import turtle
def __A ( __lowerCamelCase , __lowerCamelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCam... | 370 |
__UpperCamelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def __A ( ) -> None:
a = input("""Enter message: """ )
a = input("""Enter key [alphanumeric]: """ )
a = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswit... | 347 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main... | 74 |
from __future__ import annotations
import requests
def A ( _SCREAMING_SNAKE_CASE ) -> dict:
lowerCamelCase : Tuple = f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
return requests.get(_SCREAMING_SNAKE_CASE ).json()
def... | 48 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def _A ( _a : int , _a : str , _a : Union[str, Any]=1_0_2_4 , _a : T... | 360 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : str = 9
UpperCAmelCase_ : Tuple = [
[0, 1, 4],
[0, 7, 8],
[1... | 29 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 1 |
'''simple docstring'''
UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( _SCREAMING_SNAKE_CASE : dict[int, list[int]] , _SCREAMING_SNAKE_CASE : int , _SCREAM... | 368 |
'''simple docstring'''
from __future__ import annotations
class __snake_case:
'''simple docstring'''
def __init__( self , A_ = 0 ) -> Dict:
lowerCAmelCase = key
def __snake_case ( self , A_ , A_ ) -> list[str]:
... | 187 | 0 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutput... | 257 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, p... | 257 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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_te... | 367 |
import argparse
from collections import defaultdict
def _A ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int ):
"""simple docstring"""
l... | 221 | 0 |
from collections.abc import Sequence
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__ ) )
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCam... | 273 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 273 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
UpperCAmelCase : Optional[int] = TypeVar("""KT""")
UpperCAmelCase : str = TypeVar("""VT""")
class __lowerCAmelCase ( Generic[KT, VT]):
... | 366 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
fr... | 148 | 0 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state... | 294 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__snake_case : Optional[Any] = [8, 5, 9, 7]
__snake_case : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
... | 269 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils imp... | 360 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import j... | 103 | 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 |
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_torch_available... | 95 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
_a = False
class _lowerCAmelCase ( unittest.Tes... | 144 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}... | 144 | 1 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ :int = logging.get_logger(__name__)
# TODO Update this
a_ :Optional[Any] = {
"facebook/esm-1b": "https://huggingface.co/facebo... | 277 | from __future__ import annotations
class A :
def __init__(self : Union[str, Any] , __UpperCAmelCase : list[list[int]] ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = TypeError(
"Matrices must be for... | 65 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class UpperCamelCase__( _a ):
def __init__( self : int , lowerCAmelCase : Optional[int]="" , lowerCAmelCase : Union[str, Any]="train" )-> s... | 371 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTok... | 91 | 0 |
"""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
if is_torch_available():
impor... | 77 | """simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils i... | 77 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ : Optional[int]... | 369 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 327 | 0 |
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():
import onnxruntime as ort
_lowerCamelCase ... | 287 |
def lowerCamelCase__ ( a , a ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_A: Union[str, Any] = str(bin(a ) )[2:] # remove the leading "0b"
_A: Union[str, Any] = str(bin(a ) )[2:... | 121 | 0 |
'''simple docstring'''
UpperCamelCase_ : Optional[int] = 256
# Modulus to hash a string
UpperCamelCase_ : List[Any] = 1000003
def __a ( _UpperCamelCase: str , _UpperCamelCase: str ) -> bool:
"""simple docstring"""
_... | 354 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 142 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 109 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( UpperCamelCase : Callable , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
UpperCAmelCase : Any... | 109 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lo... | 361 |
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
snake_case_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE )
if number ... | 267 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Con... | 20 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'''configuration_roberta''': ['''... | 301 | 0 |
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
__SCR... | 362 | def snake_case () -> Dict:
'''simple docstring'''
_snake_case : List[str] = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 284 | 0 |
from __future__ import annotations
class a__ :
"""simple docstring"""
def __init__( self , lowercase=None ) -> Optional[int]:
'''simple docstring'''
A__ = data
A__ = None
def __repr__( self ) -> int:
'''simple docstring'''... | 68 |
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,
Com... | 184 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
def is_in_circle(UpperCamelCase : float , UpperCamelCase : float ) -> bool... | 366 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ (UpperCamelCase : list[int] ):
'''simple docstring'''
if not nums:
return 0
_a = nums[0]
_a = 0
for num in nums[1:]:
... | 179 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 129 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected', [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shards': ... | 284 | 0 |
"""simple docstring"""
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_avail... | 365 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_v... | 24 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
... | 186 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case =logging.get_l... | 364 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__snake_case =logging.getLogger(__name__)
class UpperCAmelCase_ ( __lowercase ):
... | 55 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : str , snake_case : int ):
'''simple docstring'''
snake_case_ = word.split()
def justify(snake_case : list , snake_case : int , sna... | 85 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import... | 158 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriev... | 110 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class ... | 110 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> List[str]:
_snake_case = []
_snake_case = []
_snake_case = 0
_snake_case = sum(_SCREAMING_SNAKE_CASE )
create_state_space_tree(_SCREAMING_SNAKE_C... | 42 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__... | 341 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 311 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:... | 311 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_co... | 169 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAme... | 169 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {
"""configuration_perceiver""": ["""... | 363 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatur... | 88 | 0 |
"""simple docstring"""
A: List[str] = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/hugg... | 109 |
'''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 os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constan... | 233 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _a ( _SCREAMING_SNAKE_CASE = 8 ) -> str:
snake_case_ = ascii_letters + digits + punctuation
return "".join(secre... | 233 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import f... | 328 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 328 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Optional[int] = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
... | 326 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_UpperCAmelCase = gray_code_... | 326 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at ... | 64 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__A = logging.get_logger(_... | 164 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | 266 |
from __future__ import annotations
import requests
_lowercase : Dict =set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories crea... | 266 | 1 |
import inspect
import re
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_docstrings.py
UpperCAmelCase : Tuple = """src/transformers"""
#... | 95 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A : List[str] = logging.get_l... | 202 | 0 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_UpperCAmelCase : str = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_UpperCAmelCase : List[str] = ... | 358 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0)... | 45 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except ... | 28 |
'''simple docstring'''
import os
def __snake_case ( UpperCAmelCase_ : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(UpperCAmelCase_ ) , UpperCAmelCase_ ) ) as in_file:
lowerCamelCase_ = in_file.read()
lowerCamelCase_ = [[int(UpperCAme... | 55 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrate... | 359 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=a_ )
class UpperCamelCase_ ( a_ ):
_A : str = field(default='ima... | 248 | 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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 69 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCAmelCase__ ( lowerCamelCase : int ):
_A : str = SwinConfig(image_size=192 ... | 227 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenize... | 227 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling... | 307 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from ... | 307 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : int , A__ : int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowerCAmelCase_ : Union[str, Any] = ... | 89 |
'''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, ViTM... | 89 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 154 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Optional[int] = ["image_processor", "tokenizer"]
_UpperCamelCase:Tuple = "ChineseCLIPImageProce... | 154 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
__UpperCAmelCase = """https://api.github.com"""
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__UpperCAmelCase = BASE_URL + """/user"""
# https:/... | 139 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__UpperCAmelCase = logging.get_logger(__name__)
de... | 139 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 158 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
re... | 158 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(sel... | 24 |
"""simple docstring"""
def lowercase ( ) ->int:
"""simple docstring"""
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(_snake_case , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
p... | 24 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 10_00 ) -> int:
__lowerCamelCase , __lowerCamelCase : List[Any] = 1, 1
__lowerCamelCase : Optional[Any] = []
for i in range(1 , n + 1 ):
__lowerCamelCas... | 185 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] ) -> str:
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Optional[int] = len(UpperCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCA... | 185 | 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, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feat... | 31 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common impo... | 31 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def snake_case_ (_a : str ):
UpperCAm... | 34 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :Union[str, Any] = {
'''configuration_vision_encoder_decoder''': ['''VisionEncode... | 331 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A__ : Optional[Any] = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONF... | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 0 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
... | 174 |
"""simple docstring"""
lowercase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowercase ( lowerCAmelCase__ : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase__ , lowerCAmel... | 45 | 0 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def _lowerCAmelCase ( A__: List[str] , A__: Any ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) ... | 363 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import RO... | 152 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __A( __lowerCAmelCase ):
snake_case_ =... | 6 | from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ):
pass
class SCREAMING_SNAKE_CASE_ :
def __init__( self : List[Any] , lowerCamelCase_ : Any ):
"""simple docstring"""
UpperCamelCase = d... | 343 | 0 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import Pr... | 358 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
... | 58 | 0 |
def __snake_case ( _UpperCAmelCase ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__a = len(_UpperCAmelCase )
__a = max(_UpperCAmelCase )
__a = min... | 49 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME,... | 165 | import argparse
_SCREAMING_SNAKE_CASE = """docs/source/_static/js/custom.js"""
def lowercase( UpperCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
with open(UpperCamelCase_ , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCamelCase = f.... | 165 | 1 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
__lowercase = logging.get_logger(__name__)
class _A ( _a ):
"""simple docstring"""
def __init__( self : str , __U... | 40 |
"""simple docstring"""
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.transfor... | 315 | 0 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
_lowerCAmelCase ={}
d... | 341 |
"""simple docstring"""
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _lowerCamelCase() -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gat... | 341 | 1 |
"""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 ( l... | 102 |
'''simple docstring'''
def UpperCAmelCase_ (__a : list , __a : list , __a : int ):
"""simple docstring"""
_a : Optional[Any] = len(__a )
_a : int = [[0] * n for i in range(__a )]
for i in range(__a ):
_a : Tuple = y_points[i]
for i ... | 271 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_lowerCamelCase : Optional[Any] = models.Sequential()
#... | 130 |
def a_ ( __lowercase : int = 50_000_000 ) -> int:
_snake_case = set()
_snake_case = int((limit - 24) ** (1 / 2) )
_snake_case = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ... | 130 | 1 |
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_torch_available... | 24 |
import unittest
from transformers import LiltConfig, 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 Model... | 24 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_SCREAMING_SNAKE_CASE = object()
# For specifying empty leaf dict `{}`
_SCREAMING_SNAKE_CASE = object()
... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 31 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_siz... | 31 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : Any = {
'x... | 251 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def A_( A : list[int] , A : list[int] , A : int):
UpperCamelCase = [0] * no_of_processes
UpperCamelCase = [0] * no_of_processes
... | 251 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrO... | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
f... | 0 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 100 |
"""simple docstring"""
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 = {
"""facebook/xlm-rober... | 100 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipel... | 38 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_t... | 152 | 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
lowerCamelCase_ = """▁"""
lowerCamelCase_ = {"""voca... | 359 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCamelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow... | 239 | 0 |
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> tuple:
'''simple docstring'''
lowerCamelCase__ = namedtuple('''result''' ,'''name value''' )
if (voltage, cu... | 209 |
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, ... | 209 | 1 |
from __future__ import annotations
def A ( _lowercase , _lowercase , _lowercase , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif electron_conc < 0:
raise ValueE... | 364 | from __future__ import annotations
from math import pi
def A ( _lowercase , _lowercase , _lowercase ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise Va... | 258 | 0 |
"""simple docstring"""
from typing import Any
class __A :
"""simple docstring"""
def __init__( self , __A ) -> Union[str, Any]:
a =data
a =None
class __A :
"""simple docstring"""
... | 81 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import Pa... | 300 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusion... | 254 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from... | 254 | 1 |
"""simple docstring"""
def a_ ( _lowerCAmelCase : int ):
'''simple docstring'''
lowercase__ : Union[str, Any] = abs(_lowerCAmelCase )
lowercase__ : Union[str, Any] = 0
while n > 0:
res += n % 10
n //= 10
re... | 77 | """simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 77 | 1 |
"""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 ... | 181 |
"""simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ = 1 , lowercase_ = 1 , lowercase_ = 1.0e4 , lowercase_ = False , lowercase_ = 1.0 , ):
assert timesteps.... | 181 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCAmelCase ( a_: str, a_: List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
create_all_state(1, _lowerCAmelCase, _lowerCAmelCase, [], _lowerCAmelCase )
return result... | 145 |
"""simple docstring"""
from math import pi, sqrt
def lowercase (_lowerCAmelCase ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(_lowerCAmelCase ) not in (0, 0.5):
... | 301 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
UpperCAmelCase : Tuple = TypeVar("""T""")
UpperCAmelCase : List[Any] = Union[List[T], Tuple[T, ...]]
UpperCAmelCase : Any = Union[T, List[T], Dict[str, T]]
UpperCAmelCase : str = Union[str, bytes, os.PathLike] | 369 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer... | 267 | 0 |
'''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 : Dict = '''src/transformers'''
# This is to make sure the transformer... | 265 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils ... | 314 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase ( _A ):
"""simple docstring"""
if not isinstance(_A, _A ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError(""... | 138 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__: List[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfi... | 138 | 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
... | 45 |
import os
def lowerCamelCase__ ( ):
with open(os.path.dirname(_a) + "/p022_names.txt") as file:
SCREAMING_SNAKE_CASE : List[str] = str(file.readlines()[0])
SCREAMING_SNAKE_CASE : List[Any] = names.replace("\"" , "").split(",")
names.sort()
SCREAMING_SNAKE_... | 76 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acce... | 141 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Optional[int] = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if no... | 141 | 1 |
import os
import numpy
import onnx
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = a.name
SCREAMING_SNAKE_CASE_: int = b.name
SCREAMING_SNAKE_CASE_: Any = ""
SCREAMING_SNAKE_CASE_: Tuple = ""
... | 13 | import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ( snake_case_ , unittest.TestCase ):
low... | 175 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
lowercase__ = _LazyModule(__name__, globa... | 369 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/... | 161 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__UpperCAmelCase = 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_copie... | 84 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 327 | 0 |
lowerCAmelCase__ : Tuple ={
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def a__ ( A__ ):
assert ty... | 359 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : List[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : Tuple ={
'micros... | 162 | 0 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __lowerCAmelCase :
pass
| 82 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCamelCase : int = 10
def __a ( UpperCAmelCase , UpperCAmelCase , UpperCAm... | 258 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
RandomHorizontalFlip,
... | 185 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ :List[str] = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 185 | 1 |
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