



'''simple docstring'''





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase      						=	50_0000

_lowerCAmelCase						,			_lowerCAmelCase      						=	os.path.split(__file__)
_lowerCAmelCase      						=	os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					dataset.map(**UpperCamelCase			)
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					dataset.filter(**UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					{"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase__ :  List[str]							      =					datasets.Features({"""text""": datasets.Value("""string"""			), """numbers""": datasets.Value("""float32"""			)}			)
        lowerCAmelCase__ :  Union[str, Any]							      =					generate_example_dataset(
            os.path.join(UpperCamelCase							,    """dataset.arrow"""			)							,    UpperCamelCase							,    num_examples=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[Any]							      =					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""							,    use_fast=UpperCamelCase			)

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

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase			)

        lowerCAmelCase__ :  List[Any]							      =					map(UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""numpy"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""pandas"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""torch"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Any							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""tensorflow"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[int]							      =					map(UpperCamelCase							,    function=UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Any							      =					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()








# 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 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 argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     ArgumentParser("Accelerate CLI tool"     ,	usage="accelerate <command> [<args>]"     ,	allow_abbrev=_A							)
	snake_case_		    =     parser.add_subparsers(help="accelerate command helpers"							)

	# Register commands
	get_config_parser(subparsers=_A							)
	env_command_parser(subparsers=_A							)
	launch_command_parser(subparsers=_A							)
	tpu_command_parser(subparsers=_A							)
	test_command_parser(subparsers=_A							)

	# Let's go
	snake_case_		    =     parser.parse_args()

	if not hasattr(_A     ,	"func"							):
		parser.print_help()
		exit(1							)

	# Run
	args.func(_A							)


if __name__ == "__main__":
		main()
