


"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase    :     Dict   						=		50_0000

UpperCAmelCase						, UpperCAmelCase    :     Optional[int]   						=		os.path.split(__file__)
UpperCAmelCase    :     int   						=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def      lowerCamelCase       (     _UpperCamelCase		:      datasets.Dataset       ,			**_UpperCamelCase		:      int							)		->       Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[Any]      							=					dataset.map(**_UpperCamelCase							)
@get_duration
def      lowerCamelCase       (     _UpperCamelCase		:      datasets.Dataset       ,			**_UpperCamelCase		:      str							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					dataset.filter(**_UpperCamelCase							)
def      lowerCamelCase       (     )		->       List[str]:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					{"""num examples""": SPEED_TEST_N_EXAMPLES}
							with tempfile.TemporaryDirectory() as tmp_dir:
														__UpperCAmelCase   :						Any      							=					datasets.Features({"""text""": datasets.Value("""string"""							), """numbers""": datasets.Value("""float32"""							)}							)
														__UpperCAmelCase   :						Optional[int]      							=					generate_example_dataset(
														    os.path.join(_UpperCamelCase       ,			"""dataset.arrow"""							)       ,			_UpperCamelCase       ,			num_examples=_UpperCamelCase							)

														__UpperCAmelCase   :						List[Any]      							=					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,			use_fast=_UpperCamelCase							)

														def tokenize(_UpperCamelCase		:      Any							):
																					return tokenizer(examples["""text"""]							)

														__UpperCAmelCase   :						Optional[int]      							=					map(_UpperCamelCase							)

														__UpperCAmelCase   :						List[Any]      							=					map(_UpperCamelCase       ,			batched=_UpperCamelCase							)

														__UpperCAmelCase   :						Optional[Any]      							=					map(_UpperCamelCase       ,			function=lambda _UpperCamelCase							: None       ,			batched=_UpperCamelCase							)

														with dataset.formatted_as(type="""numpy"""							):
																					__UpperCAmelCase   :						Tuple      							=					map(_UpperCamelCase       ,			function=lambda _UpperCamelCase							: None       ,			batched=_UpperCamelCase							)

														with dataset.formatted_as(type="""pandas"""							):
																					__UpperCAmelCase   :						List[Any]      							=					map(_UpperCamelCase       ,			function=lambda _UpperCamelCase							: None       ,			batched=_UpperCamelCase							)

														with dataset.formatted_as(type="""torch"""       ,			columns="""numbers"""							):
																					__UpperCAmelCase   :						Union[str, Any]      							=					map(_UpperCamelCase       ,			function=lambda _UpperCamelCase							: None       ,			batched=_UpperCamelCase							)

														with dataset.formatted_as(type="""tensorflow"""       ,			columns="""numbers"""							):
																					__UpperCAmelCase   :						List[Any]      							=					map(_UpperCamelCase       ,			function=lambda _UpperCamelCase							: None       ,			batched=_UpperCamelCase							)

														__UpperCAmelCase   :						Any      							=					map(_UpperCamelCase       ,			function=_UpperCamelCase       ,			batched=_UpperCamelCase							)

														__UpperCAmelCase   :						Tuple      							=					filter(_UpperCamelCase							)

														# Activate later when tokenizer support batched inputs
														# with dataset.formatted_as(type='numpy'):
														#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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




"""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_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
