


from math import factorial

a					  =     {str(digit): factorial(digit) for digit in range(1_0)}


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										raise TypeError("""Parameter number must be int"""  )

					if number < 0:
										raise ValueError("""Parameter number must be greater than or equal to 0"""  )

					# Converts number in string to iterate on its digits and adds its factorial.
					return sum(DIGIT_FACTORIAL[digit] for digit in str(UpperCAmelCase__  )  )


def 			UpperCAmelCase_				(      UpperCAmelCase__ = 6_0						,							UpperCAmelCase__ = 1_0_0_0_0_0_0  ):

					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ) or not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										raise TypeError("""Parameters chain_length and number_limit must be int"""  )

					if chain_length <= 0 or number_limit <= 0:
										raise ValueError(
										    """Parameters chain_length and number_limit must be greater than 0"""  )

					# the counter for the chains with the exact desired length
					lowercase_  							=			0
					# the cached sizes of the previous chains
					lowercase_  							=			{}

					for start_chain_element in range(1						,							UpperCAmelCase__  ):
										# The temporary set will contain the elements of the chain
										lowercase_  							=			set()
										lowercase_  							=			0

										# Stop computing the chain when you find a cached size, a repeating item or the
										# length is greater then the desired one.
										lowercase_  							=			start_chain_element
										while (
										    chain_element not in chain_sets_lengths
										    and chain_element not in chain_set
										    and chain_set_length <= chain_length
										):
															chain_set.add(UpperCAmelCase__  )
															chain_set_length += 1
															lowercase_  							=			digit_factorial_sum(UpperCAmelCase__  )

										if chain_element in chain_sets_lengths:
															chain_set_length += chain_sets_lengths[chain_element]

										lowercase_  							=			chain_set_length

										# If chain contains the exact amount of elements increase the counter
										if chain_set_length == chain_length:
															chains_counter += 1

					return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'''{solution()}''')






# 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 			UpperCAmelCase_				(      UpperCAmelCase__=None  ):
					if subparsers is not None:
										lowercase_  							=			subparsers.add_parser("""env"""  )
					else:
										lowercase_  							=			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__  ):
					lowercase_  							=			torch.__version__
					lowercase_  							=			torch.cuda.is_available()
					lowercase_  							=			is_xpu_available()
					lowercase_  							=			is_npu_available()

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

					lowercase_  							=			{
					    """`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:
										lowercase_  							=			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:"""  )
					lowercase_  							=			(
					    """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
					    if isinstance(UpperCAmelCase__						,							UpperCAmelCase__  )
					    else F'''\t{accelerate_config}'''
					)
					print(UpperCAmelCase__  )

					lowercase_  							=			accelerate_config

					return info


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			env_command_parser()
					lowercase_  							=			parser.parse_args()
					env_command(UpperCAmelCase__  )
					return 0


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


