code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
_lowerCamelCase : Optional[Any] = 2_5_6
# Modulus to hash a string
_lowerCamelCase : Tuple = 1_0_0_0_0_0_3
def a__ ( UpperCAmelCase : str , UpperCAmelCase : str ) -> bool:
UpperCAmelCase : Dict = len(UpperCAme... | 99 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 99 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelC... | 99 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 1 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 99 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Optional[Any] = {
... | 99 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 1 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 1 |
from manim import *
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : int ):
UpperCAmelCase : List[str] = Rectangle(height=0.5, width=0.5 )
UpperCAmelCase : Dict = Rectangle(height=0.4_6, width=0.4_6 ).... | 99 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import ... | 99 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a__ ( ) -> str:
UpperCAmelCase : str = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
UpperCAmelCase : Dict = parser.add_subparsers(help='''d... | 99 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 1 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowerCamelCase : List[str] = TypeVar("KT")
_lowerCamelCase : str = TypeVar("VT")
class __UpperCAmelCase ( Generic[KT, VT] ):
def __init__( self ... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 1 |
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : int ) -> float:
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >=... | 99 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : List[Any] = {}
try:
if not is_sentencepiece_available():
raise ... | 99 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 99 | 1 |
import string
import numpy
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , UpperCAmelCase )
class __UpperCAmelCase :
UpperCamelCase = string.ascii_uppercas... | 99 |
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, slow, torch_device
from ...test_configuration_common imp... | 99 | 1 |
from math import sqrt
def a__ ( UpperCAmelCase : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All... | 99 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 99 | 1 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 1 |
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_available
... | 99 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 99 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://... | 99 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> f... | 99 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
req... | 99 |
from __future__ import annotations
import queue
class __UpperCAmelCase :
def __init__( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = data
UpperCAmelCase : Tuple = None
UpperCAmelCase : Any = None
... | 99 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 |
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 ...test_conf... | 99 | 1 |
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : list[int] ) -> None:
UpperCAmelCase : Optional[int] = len(UpperCAmelCase )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCas... | 99 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_availa... | 99 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase : List[str] = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisp... | 99 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 99 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 99 | 1 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> None:
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 ... | 99 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCamelCase : Optional[int] = logging.get_l... | 99 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 99 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 1 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase : Optional[int] = get_tests_dir("fi... | 99 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 99 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """ClapFeatureExtractor"""
UpperCamelCase = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
de... | 99 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 1 |
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 BartConfig
from transformers.models.bert.tokenization_bert ... | 99 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 99 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 | 1 |
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 __UpperCAmelCase ( datasets.BeamBasedBuilder ):
def __magic_name__ ( self : Tuple... | 99 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 1 |
from collections.abc import Generator
from math import sin
def a__ ( UpperCAmelCase : bytes ) -> bytes:
if len(UpperCAmelCase ) != 32:
raise ValueError('''Input must be of length 32''' )
UpperCAmelCase : List[str] = b''''''
for i in [3, 2, 1, 0]:
... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 1 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a__ ( UpperCAmelCase : np.ndarray , UpperCAmelCase : np.ndarray , UpperCAmelCase : np.ndarray , UpperCAmelCase : int , UpperCAmelCase : ... | 99 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 99 | 1 |
import os
from collections.abc import Iterator
def a__ ( UpperCAmelCase : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(UpperCAmelCase ):
UpperCAmelCase : Any = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']... | 99 |
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, slow, torch_device
from ...test_configuration_common imp... | 99 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument("--dump_path", defau... | 99 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 99 | 1 |
import math
import random
def a__ ( UpperCAmelCase : float , UpperCAmelCase : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_lowerCamelCase : Optional[Any] = 0.0_2
... | 99 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 99 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 99 | 1 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Union[str, Any] = 1
for n in range(m + 1 ):
fo... | 99 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> f... | 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 |
from __future__ import annotations
import queue
class __UpperCAmelCase :
def __init__( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = data
UpperCAmelCase : Tuple = None
UpperCAmelCase : Any = None
... | 99 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 99 |
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 ...test_conf... | 99 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 99 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_availa... | 99 | 1 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 99 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 99 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def a__ ( UpperCAmelCase : bytes , UpperCAmelCase : int ) -> np.array:
UpperCAmelCase : Optional[Any] = f'''{sampling_rate}'''
UpperCAmelCa... | 99 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : Union[str, Any] = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not ... | 99 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 | 1 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import S... | 99 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 99 | 1 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require... | 99 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_lowerCamelCase : List[str] = re.compile(R"\b(a|an|the)\b", re.UNICODE)
_lowerCamelCase : Optional[int] = None
def a__ ( ) -> Tuple:
UpperC... | 99 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : int, __A : Tuple, __A : int ):
UpperCAmelCase : Optional[Any] = params
... | 99 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 | 1 |
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float ) -> float:
if mass < 0:
raise ValueError('''The mass of a body cannot be negative''' )
return 0.5 * mass * abs(UpperCAmelCase ) * abs(UpperCAmelCase )
if __name__ == "__main__":
im... | 99 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {"vocab... | 99 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 1 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 99 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 1 |
import math
def a__ ( UpperCAmelCase : int ) -> bool:
assert isinstance(UpperCAmelCase , UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number %... | 99 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 99 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# S... | 99 |
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, slow, torch_device
from ...test_configuration_common imp... | 99 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_lowerCamelCase : str ... | 99 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 99 | 1 |
import os
import pytest
from attr import dataclass
_lowerCamelCase : Union[str, Any] = "us-east-1" # defaults region
@dataclass
class __UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = """arn:aws:iam::558105141721:role/sagemaker_execution_ro... | 99 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCamelCase : List[str] = "src/transformers"
# This is t... | 99 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 99 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.ut... | 99 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> f... | 99 | 1 |
def a__ ( UpperCAmelCase : list ) -> list:
UpperCAmelCase : Optional[Any] = len(UpperCAmelCase )
for _ in range(UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
UpperCAmelCase , Uppe... | 99 |
from __future__ import annotations
import queue
class __UpperCAmelCase :
def __init__( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = data
UpperCAmelCase : Tuple = None
UpperCAmelCase : Any = None
... | 99 | 1 |
_lowerCamelCase : Any = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def a__ ( UpperCAmelCase : dict , UpperCAmelCase : str , UpperCAmelCase ... | 99 |
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 ...test_conf... | 99 | 1 |
import os
import string
import sys
_lowerCamelCase : List[Any] = 1 << 8
_lowerCamelCase : Union[str, Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 2_7,
"up": 6_5 + ARROW_KEY_FLAG,
"down": 6_6 + ARROW_KEY_FLAG,
"right": 6_7 + ARROW_KEY_FL... | 99 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_availa... | 99 | 1 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 99 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 99 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
... | 99 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 1 |
from collections import defaultdict
class __UpperCAmelCase :
def __init__( self : Dict, __A : int, __A : List[Any] ):
UpperCAmelCase : Dict = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially a... | 99 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContex... | 99 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 1 |
import argparse
from collections import defaultdict
import yaml
_lowerCamelCase : Dict = "docs/source/en/_toctree.yml"
def a__ ( UpperCAmelCase : Optional[Any] ) -> List[Any]:
UpperCAmelCase : str = defaultdict(UpperCAmelCase )
UpperCAme... | 99 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 99 | 1 |
_lowerCamelCase : dict[tuple[int, int, int], int] = {}
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : int ) -> int:
# if we are absent twice, or late 3 consecutive days,
# no further prize strings ar... | 99 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 1 |
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
@require_torch
class __UpperCAmelCase ( unittest.TestCase ... | 99 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 1 |
def a__ ( UpperCAmelCase : list[list[int | float]] ) -> int:
UpperCAmelCase : str = len(UpperCAmelCase )
UpperCAmelCase : List[str] = len(matrix[0] )
UpperCAmelCase : Optional[int] = min(UpperCAmelCase , UpperCAmelCase )... | 99 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoCon... | 99 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_a... | 99 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 1 |
_lowerCamelCase : Union[str, Any] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def a__ ( UpperCAmelCase : bytes ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(UpperCAmelCase , UpperCAmelCase ... | 99 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_availa... | 99 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 1 |
def a__ ( 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(multiplication_table(number=5, number_of_ter... | 99 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 99 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impo... | 350 |
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, slow, torch_device
from ...test_configuration_common imp... | 99 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torc... | 351 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 99 | 0 |
_lowerCamelCase : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-... | 352 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVec... | 353 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_a... | 99 | 0 |
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, prepare_image_inputs
if is_torch_available():
i... | 354 |
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCamelCase : Tuple = 3_0_0 # TEMPERATURE (unit = K)
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : float , ) -> f... | 99 | 0 |
from __future__ import annotations
from typing import Any
class __UpperCAmelCase ( lowercase__ ):
pass
class __UpperCAmelCase :
def __init__( self : Dict, __A : Any ):
UpperCAmelCase : Any = data
UpperCAmelCase : Node | N... | 355 |
from __future__ import annotations
import queue
class __UpperCAmelCase :
def __init__( self : str, __A : Union[str, Any] ):
UpperCAmelCase : Dict = data
UpperCAmelCase : Tuple = None
UpperCAmelCase : Any = None
... | 99 | 0 |
class __UpperCAmelCase :
def __init__( self : List[str] ):
UpperCAmelCase : Union[str, Any] = ''''''
UpperCAmelCase : Optional[int] = ''''''
UpperCAmelCase : str = []
def __magic_name__ ( self : Tuple, __A ... | 356 |
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 ...test_conf... | 99 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 357 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_availa... | 99 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Union[str, Any] = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig'... | 358 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling... | 99 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_lowerCamelCase : str ... | 359 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tr... | 99 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : Optional[Any] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Optional[int] = typing.Union[np.floataa, i... | 360 |
def a__ ( UpperCAmelCase : int ) -> bool:
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
UpperCAmelCase : List[str] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCAmelCase )
if number < 0:
return Fal... | 99 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 361 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowerCamelCase : int = datasets.logging.get_logger(__name__)
_lowerCamelCase : Any = "\\n@InProceedings... | 99 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCamelCase : Dict = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
... | 362 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
UpperCAmelCase : List[Any] = str(bin(UpperCAmelCase ) )[2:] # remove the leading "... | 99 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
... | 363 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 99 | 0 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration... | 364 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from... | 99 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the ro... | 365 |
import logging
import os
from .state import PartialState
class __UpperCAmelCase ( logging.LoggerAdapter ):
@staticmethod
def __magic_name__ ( __A : str ):
UpperCAmelCase : Dict = PartialState()
return not main_process_only or (main_process_only a... | 99 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[str] = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfi... | 366 |
import torch
from torch import nn
class __UpperCAmelCase ( nn.Module ):
def __init__( self : List[Any], __A : List[Any], __A : Optional[Any], __A : int, __A : List[Any], __A : int=1, __A : List[str]=False ):
... | 99 | 0 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[int | str] ) -> Optional[int]:
create_state_space_tree(a_ , [] , 0 , [0 for i in range(len(a_ ) )] )
def a__ ( UpperCAmelCase : list[int | str] , Uppe... | 367 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger... | 99 | 0 |
def a__ ( UpperCAmelCase : int ) -> bool:
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...")
_lowerCamelCase : Any ... | 368 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 369 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : str = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 99 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def a__ ( UpperCAmelCase : Callable[[int | float], int | float] , UpperCAmelCase : int | float , UpperCAmelCase : int | float , UpperCAmelCase : int = 100 , ... | 370 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUI... | 371 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 99 | 0 |
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