


'''simple docstring'''



import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def     _UpperCamelCase      (		UpperCamelCase__=None					):
			if subparsers is not None:
						UpperCAmelCase__  :					Dict        =     subparsers.add_parser("""env"""					)
			else:
						UpperCAmelCase__  :					Dict        =     argparse.ArgumentParser("""Accelerate env command"""					)

			parser.add_argument(
			    """--config_file""" ,      default=UpperCamelCase__ ,      help="""The config file to use for the default values in the launching script."""					)

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







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[Any]        =     torch.__version__
			UpperCAmelCase__  :					Union[str, Any]        =     torch.cuda.is_available()
			UpperCAmelCase__  :					Dict        =     is_xpu_available()
			UpperCAmelCase__  :					Tuple        =     is_npu_available()

			UpperCAmelCase__  :					int        =     """Not found"""
			# Get the default from the config file.
			if args.config_file is not None or os.path.isfile(UpperCamelCase__					):
						UpperCAmelCase__  :					List[str]        =     load_config_from_file(args.config_file					).to_dict()

			UpperCAmelCase__  :					List[str]        =     {
			    """`Accelerate` version""": version,
			    """Platform""": platform.platform(),
			    """Python version""": platform.python_version(),
			    """Numpy version""": np.__version__,
			    """PyTorch version (GPU?)""": f'''{pt_version} ({pt_cuda_available})''',
			    """PyTorch XPU available""": str(UpperCamelCase__					),
			    """PyTorch NPU available""": str(UpperCamelCase__					),
			    """System RAM""": f'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
			}
			if pt_cuda_available:
						UpperCAmelCase__  :					str        =     torch.cuda.get_device_name()

			print("""\nCopy-and-paste the text below in your GitHub issue\n"""					)
			print("""\n""".join([f'''- {prop}: {val}''' for prop, val in info.items()]					)					)

			print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""					)
			UpperCAmelCase__  :					str        =     (
			    """\n""".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]					)
			    if isinstance(UpperCamelCase__ ,      UpperCamelCase__					)
			    else f'''\t{accelerate_config}'''
			)
			print(UpperCamelCase__					)

			UpperCAmelCase__  :					Dict        =     accelerate_config

			return info







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Any        =     env_command_parser()
			UpperCAmelCase__  :					Tuple        =     parser.parse_args()
			env_command(UpperCamelCase__					)
			return 0


if __name__ == "__main__":
						raise SystemExit(main())



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__					):
			# if the collection is empty, returns empty
			if collection == []:
						return []

			# get some information about the collection
			UpperCAmelCase__  :					List[str]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     max(UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[Any]        =     min(UpperCamelCase__					)

			# create the counting array
			UpperCAmelCase__  :					Dict        =     coll_max + 1 - coll_min
			UpperCAmelCase__  :					Optional[int]        =     [0] * counting_arr_length

			# count how much a number appears in the collection
			for number in collection:
						counting_arr[number - coll_min] += 1

			# sum each position with it's predecessors. now, counting_arr[i] tells
			# us how many elements <= i has in the collection
			for i in range(1 ,      UpperCamelCase__					):
						UpperCAmelCase__  :					List[str]        =     counting_arr[i] + counting_arr[i - 1]

			# create the output collection
			UpperCAmelCase__  :					Tuple        =     [0] * coll_len

			# place the elements in the output, respecting the original order (stable
			# sort) from end to begin, updating counting_arr
			for i in reversed(range(0 ,      UpperCamelCase__					)					):
						UpperCAmelCase__  :					Any        =     collection[i]
						counting_arr[collection[i] - coll_min] -= 1

			return ordered







def     _UpperCamelCase      (		UpperCamelCase__					):
			return "".join([chr(UpperCamelCase__					) for i in counting_sort([ord(UpperCamelCase__					) for c in string]					)]					)


if __name__ == "__main__":
		# Test string sort
		assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

		__A	=input('Enter numbers separated by a comma:\n').strip()
		__A	=[int(item) for item in user_input.split(',')]
		print(counting_sort(unsorted))