





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__  :	str     =50_00_00

UpperCAmelCase__ ,UpperCAmelCase__  :	Dict     =os.path.split(__file__)
UpperCAmelCase__  :	str     =os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def    _lowercase    (  _UpperCAmelCase )  ->     List[Any]:
				lowerCamelCase		       =dataset.select(range(0	,					len(_UpperCAmelCase )	,					2 ) )





@get_duration
def    _lowercase    (  _UpperCAmelCase )  ->     Tuple:
				lowerCamelCase		       =dataset.sort("""numbers""" )





@get_duration
def    _lowercase    (  _UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =dataset.shuffle()





@get_duration
def    _lowercase    (  _UpperCAmelCase )  ->     List[Any]:
				lowerCamelCase		       =dataset.train_test_split(0.1 )





@get_duration
def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=10 )  ->     Optional[int]:
				for shard_id in range(_UpperCAmelCase ):
								lowerCamelCase		       =dataset.shard(_UpperCAmelCase	,					_UpperCAmelCase )





def    _lowercase    (  )  ->     Optional[int]:
				lowerCamelCase		       ={"""num examples""": SPEED_TEST_N_EXAMPLES}
				lowerCamelCase		       =(select, sort, shuffle, train_test_split, shard)
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset""" )
								lowerCamelCase		       =datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
								lowerCamelCase		       =generate_example_dataset(
								    os.path.join(_UpperCAmelCase	,					"""dataset.arrow""" )	,					_UpperCAmelCase	,					num_examples=_UpperCAmelCase )
								print("""Functions""" )
								for func in functions:
												print(func.__name__ )
												lowerCamelCase		       =func(_UpperCAmelCase )

				with open(_UpperCAmelCase	,					"""wb""" ) as f:
								f.write(json.dumps(_UpperCAmelCase ).encode("""utf-8""" ) )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_indices_mapping()











'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case      :      Tuple   							=		{
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Tuple   							=		[
				    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ConvNextV2ForImageClassification',
				    'ConvNextV2Model',
				    'ConvNextV2PreTrainedModel',
				    'ConvNextV2Backbone',
				]


if TYPE_CHECKING:
				from .configuration_convnextva import (
				    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ConvNextVaConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_convnextva import (
								    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ConvNextVaBackbone,
								    ConvNextVaForImageClassification,
								    ConvNextVaModel,
								    ConvNextVaPreTrainedModel,
								)

else:
				import sys

				_snake_case      :      Any   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)

