





"""simple docstring"""




# 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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )					, )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


