code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import 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,
... | 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 unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # s... | 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 string
def a__ ( UpperCAmelCase : str ) -> None:
for key in range(len(string.ascii_uppercase ) ):
UpperCAmelCase : int = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
UpperCAmelCase : List[Any] = ... | 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 collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCamelCase : List[Any] = collections.namedtuple("_Datas... | 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 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, ... | 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 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
... | 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 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class __UpperCAmelCase ( lowerCamelCase__ ):
# `task` is not a ClassVar since we want it to be part of the `asdi... | 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 torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = (UnCLIPScheduler,)
def __magic_name__ ( self : Optional[int], **__A : Union[str, Any]... | 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
def a__ ( UpperCAmelCase : str , UpperCAmelCase : list[str] | None = None , UpperCAmelCase : dict[str, float] | None = None , UpperCAmelCase : bool = False , ) -> tuple[int, float, str]:
UpperCAmelCa... | 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 json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 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 |
def a__ ( UpperCAmelCase : float , UpperCAmelCase : float , UpperCAmelCase : int ):
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
... | 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 |
_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 )... | 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 |
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... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : int = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
... | 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 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."
)
| 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 |
def a__ ( UpperCAmelCase : int = 10**12 ) -> int:
UpperCAmelCase : List[Any] = 1
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : int = 1
UpperCAmelCase : str = 1
while numerator <= 2 * min_total - 1:
prev_numerato... | 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 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[int] , UpperCAmelCase : int ) -> list[list[int]]:
UpperCAmelCase : list[list[int]] = []
UpperCAmelCase : list[int] = []
UpperCAmelCase : Any = 0
UpperCAme... | 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 gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,... | 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 |
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... | 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 Counter
from timeit import timeit
def a__ ( UpperCAmelCase : str = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def a__ ( UpperCAmelCase : 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 |
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... | 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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtrac... | 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 random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
fro... | 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 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://... | 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 os
import pytest
from attr import dataclass
_lowerCamelCase : Union[str, Any] = "us-east-1" # defaults region
@dataclass
class __UpperCAmelCase :
UpperCamelCase = 4_2
UpperCamelCase = """arn:aws:iam::558105141721:role/sagemaker_execution_r... | 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 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... | 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 |
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 a__ ( UpperCAmelCase : list , UpperCAmelCase : list , UpperCAmelCase : list , UpperCAme... | 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 typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self ... | 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 : str ) -> List[str]:
stooge(UpperCAmelCase , 0 , len(UpperCAmelCase ) - 1 )
return arr
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Unio... | 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 shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 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 |
import logging
from transformers.configuration_utils import PretrainedConfig
_lowerCamelCase : Optional[Any] = logging.getLogger(__name__)
class __UpperCAmelCase ( lowerCamelCase__ ):
UpperCamelCase = """masked_bert"""
def __init__( self : ... | 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 json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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_... | 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 |
# Imports
import numpy as np
class __UpperCAmelCase :
def __init__( self : Optional[int], __A : Dict=None, __A : Union[str, Any]=None, __A : Union[str, Any]=None, __A : Optional[Any]=None, __A : Optional[int]=None ):
self... | 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 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowerCamelCase : Dict = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
... | 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 |
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 ... | 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 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False)... | 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
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... | 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 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __UpperCAme... | 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 |
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 ... | 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 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 (
HfArgumentPar... | 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 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 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 |
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[list[int]] ) -> int:
def update_area_of_max_square(UpperCAmelCase : int , UpperCAmelCase : int ) -> int:
# BASE CASE
if row >= rows ... | 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 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 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 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require... | 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 |
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 ... | 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 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... | 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 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... | 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 |
from string import ascii_lowercase, ascii_uppercase
def a__ ( UpperCAmelCase : str ) -> str:
if not sentence:
return ""
UpperCAmelCase : Tuple = dict(zip(UpperCAmelCase , UpperCAmelCase ) )
return lower_to_upper.get(sentence[0] , sentence... | 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 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatur... | 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 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowerCamelCase : ... | 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 |
import operator as op
_lowerCamelCase : Any = "scaler.pt"
_lowerCamelCase : Optional[Any] = "pytorch_model"
_lowerCamelCase : Dict = "random_states"
_lowerCamelCase : Optional[int] = "optimizer"
_lowerCamelCase : List[s... | 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 |
def a__ ( UpperCAmelCase : int = 1_000 ) -> int:
return sum(e for e in range(3 , UpperCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 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 |
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... | 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 |
def a__ ( UpperCAmelCase : list , UpperCAmelCase : list , UpperCAmelCase : int ) -> int:
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
... | 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 |
def a__ ( UpperCAmelCase : int ):
UpperCAmelCase : int = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( UpperCAmelCase : int = 5_000 ):
UpperCAmelCase : int = [(i * (3 * i - 1)) // 2 for i in range(1 , UpperCAm... | 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 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 )
UpperCAmelCas... | 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 |
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 ... | 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 copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ca... | 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 |
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 : ... | 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 |
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... | 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 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a__ ( UpperCAmelCase : str ... | 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"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import He... | 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_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {
"configuration_whisper": ["WHISPER_PRETRAINED... | 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 |
import random
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str ) -> List[Any]:
UpperCAmelCase : Dict = a[left_index]
UpperCAmelCase : Union[str, Any] = left_index + 1
for j in ... | 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 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 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 ...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] = {
... | 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 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... | 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 unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCam... | 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 multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike... | 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 inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_envir... | 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 |
_lowerCamelCase : Dict = tuple[float, float, float]
_lowerCamelCase : Union[str, Any] = tuple[float, float, float]
def a__ ( UpperCAmelCase : Pointad , UpperCAmelCase : Pointad ) -> Vectorad:
UpperCAmelCase : int ... | 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
class __UpperCAmelCase :
def __init__( self : int, __A : int ):
UpperCAmelCase : Tuple = data
UpperCAmelCase : Node | None = None
UpperCAmelCase : Node | None = None
... | 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 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
class __... | 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 |
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... | 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 |
def a__ ( UpperCAmelCase : int ) -> None:
UpperCAmelCase : Optional[Any] = generate_pascal_triangle(UpperCAmelCase )
for row_idx in range(UpperCAmelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' ... | 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 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... | 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 |
from __future__ import annotations
def a__ ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Dict ): # noqa: E741
while r - l > 1:
UpperCAmelCase : Optional[An... | 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 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... | 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 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase : Optional[int] = 0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0... | 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 |
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... | 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 |
_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... | 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 |
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... | 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 |
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... | 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 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 ... | 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 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_lowerCamelCase : List[Any] = parse(importlib.metadata.version("torch"))
def a__ ( UpperCAmelCase : Union[str, Version] , U... | 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 typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCAmelCase ( lowe... | 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 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... | 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 |
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... | 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 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_modeling_common import ModelTesterM... | 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 html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)... | 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 |
from __future__ import annotations
def a__ ( UpperCAmelCase : int ) -> list[int]:
UpperCAmelCase : List[str] = [True] * limit
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Union[... | 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 |
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 ... | 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 |
# 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... | 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 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowerCamelCase : List[An... | 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 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_... | 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 |
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 : Tuple ... | 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 itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def a__ ( UpperCAmelCase : Any , UpperCAmelCase : List[Any] ) -> Optional[int]:
UpperCAmelCase : ... | 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 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __UpperCAmelCase ( lowerCamelCase__ ):
def __magic_name__ ( self : Any ):
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "... | 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 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCamelCase__ ( __snake_case = 2_00_00_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = [0]
_UpperCamelCase = 42
... | 100 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase__ ( __snake_case ) -> None:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase ... | 100 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 100 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mo... | 100 | 1 |
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