code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
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# 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 required... | 666 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
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 = {
"xlm-roberta-base": "https://... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: dict , lowerCAmelCase_: str , lowerCAmelCase_: set , lowerCAmelCase_: set , lowerCAmelCase_: dict , lowerCAmelCase_: dict ... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
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 snake_case__ ... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
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 snake_case__ ( _UpperCamelCase , unittest.Te... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(lowerCAmelCase_ )
snake_case_ ,snake_case_ : Tuple = divmod(lowerCAme... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN,... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 1 |
import functools
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: list[int] ):
# Validation
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not all(isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) for day in days ):
... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCAmelCase = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Sin... | 666 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils imp... | 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 1 |
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 .tokenization_bart import BartTokenizer
... | 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class snake_cas... | 666 | 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 snake_case__ ( _Upp... | 666 | 1 |
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 import TFXLMRo... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list , lowerCAmelCase_: list , lowerCAmelCase_: list , ... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
Upp... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 1 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 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 .tokenization_roberta import RobertaTokenizer... | 666 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Dict ):
snake_case... | 666 | 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 import TFXLMRo... | 666 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class snake_case__ :
def __init__( self : Union[str, Any] , A__ : Optional[Any] , A__ : Optional[Any] , A__ : Dict , A__ : Union[str, Any] , A__ : Union[str, Any]... | 666 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from tra... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...ima... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
import mpmath # for roots of unity
import numpy as np
class snake_case__ :
def __init__( self : str , A__ : List[str]=None , A__ : List[str]=None ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = l... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffu... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 1 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class snake_case__ ( _UpperCamelCase ):
def __init__( self : Dict , *A__ : Dict , **A__... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 666 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 1 |
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 .tokenization_roberta import RobertaTokenizer... | 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = ... | 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 1 |
from math import pi, sqrt, tan
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: float ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def SCREAMING_SNAKE_CASE_ ( lowe... | 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa... | 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 1 |
import json
import sys
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: List[str] , lowerCAmelCase_: List[Any] ):
with open(lowerCAmelCase_ , encoding="utf-8" ) as f:
snake_case_ : Dict = json.load(lowerCAmelCase_ )
snake_case_ : ... | 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int , lowerCAmelCase_: int ):
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("p... | 666 | 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 snake_case__ ( _Upp... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: Optional[Any] , lowerCAmelCase_: Optional[Any] ):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(lowerCAmelCase_ ):
for j in range(lowerCAmelCase_ ):
if dist[i][j]... | 666 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 1 |
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Input value must be an 'int' type" )
snake_case_ : Dict = 0
while number:
position += 1
number ... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 1 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, Prio... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
UpperCAmelCase = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
... | 666 | 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 .tokenization_roberta import RobertaTokenizer... | 666 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
... | 666 | 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 import TFXLMRo... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from di... | 666 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slo... | 666 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
UpperCAmelCase = logging.getLogger... | 666 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import r... | 666 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 1 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_... | 666 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = 1 # (0 is vertical, 1 is horizontal)
def SCREAMING_SNAKE_C... | 666 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
UpperCAmelCase = HUGGINGFACE_HUB_CACHE
UpperCAmelCase = "config.json"
UpperCAmelCase = "diffusion_pytorch_model.bin"
UpperCAmelCase = "diffusion_flax_model.... | 666 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def SCREAMING_SNAKE_CASE_ ... | 666 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
raise Opt... | 666 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgeto... | 666 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: float , lowerCAmelCase_: int ):
snake_case_ : Dict = u
for i in range(1 , lowerCAmelCase_ ):
snake_case_ : Union[str, Any] = ... | 666 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 1 |
from typing import Any
class snake_case__ :
def __init__( self : List[str] , A__ : Any ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = data
snake_case_ : str = None
class ... | 666 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.... | 666 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 1 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax... | 666 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True,... | 666 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tokenization_cani... | 0 | 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 snake_case__ ( _Upp... | 666 | 0 |
from PIL import Image
def _A ( _lowercase ) -> Image:
"""simple docstring"""
__UpperCamelCase, __UpperCamelCase = image.size
__UpperCamelCase = 0
__UpperCamelCase = image.load()
for i in range(_lowercase ):
for j in range(_lowercase ):... | 1 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 0 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 0 |
'''simple docstring'''
def A_( A : int = 5000_0000):
UpperCamelCase = set()
UpperCamelCase = int((limit - 24) ** (1 / 2))
UpperCamelCase = set(range(3 , prime_square_limit + 1 , 2))
primes.add(2)
for p in range(3 , prime_... | 3 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ):
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
lowerCAmelCase ... | 4 | 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 .tokenization_roberta import RobertaTokenizer... | 666 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :int ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
_lowercase = int(i... | 5 | 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 import TFXLMRo... | 666 | 0 |
import os
import numpy
import onnx
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Dict , UpperCamelCase__: str ):
SCREAMING_SNAKE_CASE__ = a.name
SCREAMING_SNAKE_CASE__ = b.name
SCREAMING_SNAKE_CASE__ = """"""
SCREAMING_SNAKE_CASE__ =... | 6 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 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,
... | 7 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : O... | 8 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
def A ( __UpperCamelCase = 4_000_000 ) -> int:
A__ = [0, 1]
A__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
A__ = 0
for j in range(len(__UpperCamelCase ) - 1 ):
if fib[... | 9 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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 BackboneTesterMixin
from ...test_c... | 10 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = log... | 11 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class _snake_case :
def __init__( self):
'''simple docstring'''
lowercase__ : List[Any] = {}
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
... | 12 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> str:
return "\n".join(
F'{number} * {i} = {number * i}' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(m... | 13 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 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_vision_av... | 14 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = (EulerDiscreteScheduler,)
A__ = ... | 15 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
from __future__ import annotations
def __a ( A__ : list , A__ : int , A__ : int , A__ : int ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = input_list[low:mid], input_list[mid : high + 1]
while left... | 16 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __SCREAMING_SNAKE_CASE ( a__ : Any ,a__ : Optional[Any] ) ... | 17 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
'''simple docstring'''
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=42 , ... | 18 | from ...configuration_utils import PretrainedConfig
UpperCAmelCase = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://huggingface.co/google/tapas... | 666 | 0 |
"""simple docstring"""
from timeit import timeit
_a = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panam... | 19 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class snake_case__ ( datasets.BeamBasedBuilder ):
def UpperCAmelCase__ ... | 666 | 0 |
import datasets
_lowerCAmelCase: List[str] = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holg... | 20 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | from __future__ import annotations
import bisect
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int] , lowerCAmelCase_: int , lowerCAmelCase_: int = 0 , lowerCAmelCase_: int = -1 ):
if hi < 0:
snake_case_ : Any = len(lowerCA... | 666 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/... | 22 | 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 snake_case__ ( _Upp... | 666 | 0 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 666 | 0 |
'''simple docstring'''
def _UpperCamelCase (_lowerCamelCase : int , _lowerCamelCase : float , _lowerCamelCase : float )-> float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _UpperCamelCase (_lowerCamelC... | 24 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int = 1_0_0_0 ):
snake_case_ ,snake_case_ : List[str] = 1, 1
snake_case_ : List[str] = 2
while True:
snake_case_ : Tuple = 0
snake_case_ : Union[str, Any] = ... | 666 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : ... | 25 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: list[int | float] , lowerCAmelCase_: int , lowerCAmelCase_: int ):
if len(lowerCAmelCase_ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
... | 666 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__UpperCamelC... | 26 | 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 .tokenization_roberta import RobertaTokenizer... | 666 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdP... | 27 | 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 import TFXLMRo... | 666 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {"configuration_xlnet":... | 28 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_ch... | 666 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ ):
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
lowerCamelCase_ = [True] * (num + 1)
lowerCamelCase_ = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ,num + 1 ,low... | 29 | from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: tuple[int, int] , lowerCAmelCase_: int ):
snake_case_ ,snake_case_ : Dict = position
snake_case_ : int = [
(y + 1, x + 2),
(y - 1, x + 2),... | 666 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase__ ( _lowercase ):
... | 30 | from ...configuration_utils import PretrainedConfig
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = "bert-generation"
def __init__( self : Optional[int] , A__ : List[Any]=5_03_58 , A__ : Any=10_24 , ... | 666 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common i... | 31 | import math
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
snake_case_ : Any = []
snake_case_ : List[str] = 2
snake_case_ : Optional[int] = int(math.sqrt(lowerCAmelCase_ ) ) # Size of every segment
snake_case_ : ... | 666 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCAmelCase_ = "\nimport os\n"
UpperCAmelCase_ = "\ndef foo():\n import os\n return False\n"
UpperCAmelCase_ = "\ndef foo():\n def bar():\n if True:\n import os\n ... | 32 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 666 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase__ : List[Any] = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenizat... | 33 | import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenizat... | 666 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging... | 34 | import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/mai... | 666 | 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 NestedDataStructureLike, PathLik... | 35 | def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: str , lowerCAmelCase_: str ):
def get_matched_characters(lowerCAmelCase_: str , lowerCAmelCase_: str ) -> str:
snake_case_ : Tuple = []
snake_case_ : Tuple = min(len(_stra ) ... | 666 | 0 |
from math import pi
def lowercase ( __A : int , __A : int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 36 | import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfi... | 666 | 0 |
from math import sqrt
def UpperCamelCase_ ( __a = 1_000_000 ) -> int:
a__ : int = 0
a__ : int = 0
a__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
... | 37 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Option... | 666 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = (PNDMScheduler,)
lowerCamelC... | 38 | 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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensio... | 666 | 0 |
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 BackboneTesterMixin
from ..... | 39 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRA... | 666 | 0 |
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