



# Copyright 2022 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
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      lowerCAmelCase_					(  _lowercase    :       Optional[int]=None)							->	List[str]:







			"""simple docstring"""
			if subparsers is not None:
						a__       :						str							=  subparsers.add_parser("""env""")
			else:
						a__       :						List[Any]							=  argparse.ArgumentParser("""Accelerate env command""")

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

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

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  torch.__version__
			a__       :						List[Any]							=  torch.cuda.is_available()
			a__       :						Any							=  is_xpu_available()
			a__       :						Union[str, Any]							=  is_npu_available()

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

			a__       :						Optional[Any]							=  {
			    """`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(_lowercase),
			    """PyTorch NPU available""": str(_lowercase),
			    """System RAM""": F'''{psutil.virtual_memory().total / 1024 ** 3:.2f} GB''',
			}
			if pt_cuda_available:
						a__       :						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:""")
			a__       :						List[str]							=  (
			    """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()])
			    if isinstance(_lowercase						,       _lowercase)
			    else F'''\t{accelerate_config}'''
			)
			print(_lowercase)

			a__       :						Dict							=  accelerate_config

			return info

def      lowerCAmelCase_					(  )							->	int:







			"""simple docstring"""
			a__       :						List[Any]							=  env_command_parser()
			a__       :						Any							=  parser.parse_args()
			env_command(_lowercase)
			return 0


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









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       int)							->	list[int]:







			"""simple docstring"""

			if num <= 0:
						a__       :						Tuple							=  F'''{num}: Invalid input, please enter a positive integer.'''
						raise ValueError(_lowercase)

			a__       :						List[Any]							=  [True] * (num + 1)
			a__       :						List[str]							=  []
			a__       :						List[Any]							=  2
			a__       :						Optional[int]							=  int(math.sqrt(_lowercase))

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(_lowercase)

									# Set multiples of start be False
									for i in range(start * start						,       num + 1						,       _lowercase):
												if sieve[i] is True:
															a__       :						Optional[int]							=  False

						start += 1

			for j in range(end + 1						,       num + 1):
						if sieve[j] is True:
									prime.append(_lowercase)

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("Enter a positive integer: ").strip())))




