


'''simple docstring'''



# 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:
						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'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i + 1][j] + matrix[i][j]					)

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums					)


if __name__ == "__main__":
		print(f"""{solution() = }""")