code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _SCREAMING_SNAKE_CASE ( ) -> int:
"""simple docstring"""
__... | 61 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
... | 61 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( A : int ) -> list[int]:
"""simple docstring"""
__snake_case : Optional[Any] = 2
__snake_case : Union[str, Any] =... | 61 |
'''simple docstring'''
import math
class a_ :
def __init__(self , __a=0) -> Any: # a graph with Node 0,1,...,N-1
"""simple docstring"""
__snake_case : List[str] = n
__snake_case : Tuple ... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int , A : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
__snake_case : int = st... | 61 |
'''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,
res... | 61 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
... | 61 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) ... | 61 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class a_ :
def __init__(self) -> None:
"""simple docstring"""
__snake_case : list[Any] = []
... | 61 |
'''simple docstring'''
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
enabl... | 61 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_co... | 61 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_scor... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : str , A : str ) -> int:
"""simple docstring"""
if len(A ) != len(A ):
raise ValueError('String lengths must match!' )
__snake_case : str = 0
for... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int , A : int ) -> int:
"""simple docstring"""
while second != 0:
__snake_case : Dict = first & second
first ^= second
__snake_case : int... | 61 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithT... | 61 | 1 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__A = 2_9_9_7_9_2_4_5_8
# Symbols
__A , __A , __A , __A = symbols('''ct x y z''')
def _SCREAMING_SNAKE_CASE ( A : ... | 61 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__A = TypeVar('''KEY''')
__A = TypeVar('''VAL''')
@dataclass(frozen=UpperCamelCase_ , slots=UpperCamelCase_ ... | 61 |
'''simple docstring'''
__A = {str(digit): digit**5 for digit in range(1_0)}
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def _SCREAM... | 61 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''P... | 61 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 1 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 61 | 1 |
'''simple docstring'''
from math import asin, atan, cos, radians, sin, sqrt, tan
__A = 637_8137.0
__A = 635_6752.31_4245
__A = 6_3_7_8_1_3_7
def _SCREAMING_SNAKE_CASE ( A : float , A : float , A : float , A : float ... | 61 |
'''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.o... | 61 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, L... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
__snake_case : str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE ( ... | 61 | 1 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 61 |
'''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... | 61 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def _SCREAMING_SNAKE_CASE ( A : List[Any] , A : List[str] , A : Union[str, Any]=None , **A : List[Any] ) -> List[Any]:
"""simple docst... | 61 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See a... | 61 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_toke... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : float , A : list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueE... | 61 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 61 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int = 10_00 ) -> int:
"""simple docstring"""
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 61 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
... | 61 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__A = logging.get_logger(__name__)
class a_ ( UpperCamelCase_ ):
def __init__(self , *__a , **__a) ->... | 61 |
'''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'''],
'... | 61 | 1 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dis... | 61 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] ) -> int:
"""simple docstring"""... | 61 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from... | 61 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
... | 61 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def _SCREAMING_SNAKE_CASE ( A : int = 8 , A : int | None = None ) -> str:
"""simple docstring"""
__snake_case : str = np.random.default_rng(seed=A )
... | 61 |
'''simple docstring'''
import math
class a_ :
def __init__(self , __a=0) -> Any: # a graph with Node 0,1,...,N-1
"""simple docstring"""
__snake_case : List[str] = n
__snake_case : Tuple ... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 |
'''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,
res... | 61 | 1 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calcula... | 61 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) ... | 61 | 1 |
'''simple docstring'''
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
enabl... | 61 |
'''simple docstring'''
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
enabl... | 61 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
... | 61 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_scor... | 61 | 1 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( A : Union[str, Any] , A : Any , A : Tuple ) -> Any:
"""simple docstring"""
__snake_case ... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 | 1 |
'''simple docstring'''
__A = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
... | 61 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithT... | 61 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
war... | 61 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 | 1 |
'''simple docstring'''
from collections.abc import Callable
def _SCREAMING_SNAKE_CASE ( A : Callable[[float], float] , A : float , A : float ) -> float:
"""simple docstring"""
__snake_case : float = a
... | 61 |
'''simple docstring'''
__A = {str(digit): digit**5 for digit in range(1_0)}
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def _SCREAM... | 61 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
... | 61 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _SCREAMING_SNAKE_CASE ( A : Optional[int] , A : bool = True , A : float = math.inf , A : float = -math.inf , A : ... | 61 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffus... | 61 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 61 | 1 |
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
... | 61 |
'''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.o... | 61 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import Bart... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
__snake_case : str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE ( ... | 61 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 61 |
'''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... | 61 | 1 |
'''simple docstring'''
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 ( A... | 61 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See a... | 61 | 1 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except ... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : float , A : list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueE... | 61 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute... | 61 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, T... | 61 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
... | 61 | 1 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_mu... | 61 |
'''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'''],
'... | 61 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
... | 61 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] ) -> int:
"""simple docstring"""... | 61 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
... | 61 | 1 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _SCREAMING_SNAKE_CASE ( A : str , A : str , **A : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
... | 61 |
'''simple docstring'''
import math
class a_ :
def __init__(self , __a=0) -> Any: # a graph with Node 0,1,...,N-1
"""simple docstring"""
__snake_case : List[str] = n
__snake_case : Tuple ... | 61 | 1 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ... | 61 |
'''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,
res... | 61 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import... | 61 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) ... | 61 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a_ ( UpperCamelCase_ , unittest.TestCase ):
... | 61 |
'''simple docstring'''
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
enabl... | 61 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__A = True
except (ImportError, ModuleNotFoundError):
__A = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt'''... | 61 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_scor... | 61 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.u... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 | 1 |
'''simple docstring'''
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_... | 61 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithT... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : Any , A : int , A : List[str] , A : List[str] , A : Tuple , A : List[Any] ) -> Tuple:
"""simple docstring"""
if index == r:
for j in range(A ... | 61 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {'''vocab_... | 61 |
'''simple docstring'''
__A = {str(digit): digit**5 for digit in range(1_0)}
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def _SCREAM... | 61 | 1 |
'''simple docstring'''
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,
... | 61 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A = logging.get_logger(__name__)
__A = {
'''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
... | 61 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from ... | 61 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 61 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list[int] , A : list[int] , A : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumer... | 61 |
'''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.o... | 61 | 1 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)... | 61 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
__snake_case : str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE ( ... | 61 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 700 |
'''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... | 61 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : Any , A : Any , A : Tuple ) -> Tuple:
"""simple docstring"""
__snake_case : int = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
... | 701 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See a... | 61 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case__ ):
_snake_case = ["""torch""", """transformers""", """onnx"""]
def __init__(self , *__a , **__a) -> Optional[i... | 702 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : float , A : list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueE... | 61 | 0 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _SCREAMING_SNAKE_CASE ( A : str , A : int , A : Optional[int] = 0 , A : List[str] = -1 ) -> int:
"""simple docstring"""
if hi < 0:
__sn... | 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 | 0 |
'''simple docstring'''
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 ( A : Any , A : int , A : Li... | 704 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
... | 61 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
... | 705 |
'''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'''],
'... | 61 | 0 |
'''simple docstring'''
class a_ :
def __init__(self , __a) -> List[Any]:
"""simple docstring"""
__snake_case : Any = set_counts
__snake_case : Optional[int] = max(UpperCAmelCase_)
__snake_case : Optional[i... | 706 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] ) -> int:
"""simple docstring"""... | 61 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 707 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
... | 61 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test... | 708 |
'''simple docstring'''
import math
class a_ :
def __init__(self , __a=0) -> Any: # a graph with Node 0,1,...,N-1
"""simple docstring"""
__snake_case : List[str] = n
__snake_case : Tuple ... | 61 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 709 |
'''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,
res... | 61 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLe... | 710 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) ... | 61 | 0 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( A : list[int] , A : int ) -> Union[str, Any]:
"""simple docstring"""
if len(_lowerCAmelCase ) < k or k < 0:
raise ValueError('Invalid Input' )
__snake_case ... | 711 |
'''simple docstring'''
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
enabl... | 61 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class a_ :
def __init__(self) -> Optional[int]:
"""simple docstring"""
__snake_case : int =... | 712 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_scor... | 61 | 0 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
... | 713 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] , A : Tuple , A : List[str] , A : List[str] ) -> List[Any]:
# Return True if there is node that has not iterated.
__snake_case : Any = [False] * len... | 714 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithT... | 61 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTest... | 715 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, Imag... | 716 |
'''simple docstring'''
__A = {str(digit): digit**5 for digit in range(1_0)}
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def _SCREAM... | 61 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a_ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ (self) -> Tuple:
"""simple docstring"""
... | 717 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 | 0 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__A = get_logger(__name__)
class a_ :
def __init__(self , __a , __a=None) -> int:
"""simple docstring"""
__snake_... | 718 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__A = (
'''This metric will ... | 719 |
'''simple docstring'''
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_ID... | 61 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( A :... | 720 |
'''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.o... | 61 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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_m... | 721 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
__snake_case : str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE ( ... | 61 | 0 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVis... | 700 |
'''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... | 61 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConf... | 701 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See a... | 61 | 0 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def _SCREAMING_SNAKE_CASE ( A : int , A : int , A : int ) -> int:
"""simple docstring"""
if a == 0:
raise ValueError('Coefficie... | 702 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : float , A : list[float] ) -> float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueE... | 61 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> Union[str, Any]:
"""simple docstring"""
if len(snake_case_ ) <= 1:
return lst
__snake_case : List[Any] = 1
while i < len(snake_case_ ):
if lst[i - ... | 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'''configuration_distilbert'... | 61 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int = 4_00_00_00 ) -> int:
"""simple docstring"""
__snake_case : Any = [0, 1]
__snake_case : List[Any] = 0
while fib[i] <= n:
fib.append(fib[... | 704 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
... | 61 | 0 |
'''simple docstring'''
import operator as op
__A = '''scaler.pt'''
__A = '''pytorch_model'''
__A = '''random_states'''
__A = '''optimizer'''
__A = '''scheduler'''
__A = '''pytorch_model.bin'''
__A = '''pytorch_model.bin.index.json'''
__A ... | 705 |
'''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'''],
'... | 61 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__A = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
ye... | 706 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] ) -> int:
"""simple docstring"""... | 61 | 0 |
'''simple docstring'''
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... | 707 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
... | 61 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : int , A : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def _SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
assert and_gate(0 ... | 708 |
'''simple docstring'''
import math
class a_ :
def __init__(self , __a=0) -> Any: # a graph with Node 0,1,...,N-1
"""simple docstring"""
__snake_case : List[str] = n
__snake_case : Tuple ... | 61 | 0 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
... | 709 |
'''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,
res... | 61 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusion... | 710 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) ... | 61 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from .... | 711 |
'''simple docstring'''
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
enabl... | 61 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pi... | 712 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_scor... | 61 | 0 |
'''simple docstring'''
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is... | 713 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( A : list ) -> list:
"""simple docstring"""
__snake_case : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__snake_case : ... | 61 | 0 |
'''simple docstring'''
import baseaa
def _SCREAMING_SNAKE_CASE ( A : str ) -> int:
return baseaa.baaencode(string.encode('utf-8' ) )
def _SCREAMING_SNAKE_CASE ( A : List[Any] ) -> List[str]:
return baseaa.baadecode(A ).d... | 714 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithT... | 61 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( A ... | 715 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is... | 61 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slo... | 716 |
'''simple docstring'''
__A = {str(digit): digit**5 for digit in range(1_0)}
def _SCREAMING_SNAKE_CASE ( A : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A ) )
def _SCREAM... | 61 | 0 |
'''simple docstring'''
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
__A = logging.get_lo... | 717 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a_ :
_snake_case = 42
_snake_case = None
... | 61 | 0 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( A : Optional[Any] , A : Union[str, Any] ) -> Any:
"""simple docstring"""
if (
not isinstance(__lowerCAmelCase , (int, float) )
or power_factor < -1
... | 718 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class a_ :
def __init__(self , __a = None) -> None:
"""simple docstring"""
... | 61 | 0 |
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