def A__  (			lowercase:     int					)				->		None:
       A						:     str         =generate_pascal_triangle(lowercase					)
       for row_idx in range(lowercase					):
              # Print left spaces
              for _ in range(num_rows - row_idx - 1					):
                     print(end=' '					)
              # Print row values
              for col_idx in range(row_idx + 1					):
                     if col_idx != row_idx:
                            print(triangle[row_idx][col_idx],		end=' '					)
                     else:
                            print(triangle[row_idx][col_idx],		end=''					)
              print()



def A__  (			lowercase:     int					)				->		list[list[int]]:

       if not isinstance(lowercase,		lowercase					):
              raise TypeError('The input value of \'num_rows\' should be \'int\''					)

       if num_rows == 0:
              return []
       elif num_rows < 0:
              raise ValueError(
                  'The input value of \'num_rows\' should be greater than or equal to 0'					)

       A						:     list[list[int]]         =[]
       for current_row_idx in range(lowercase					):
              A						:     List[str]         =populate_current_row(lowercase,		lowercase					)
              triangle.append(lowercase					)
       return triangle



def A__  (			lowercase:     list[list[int]],		lowercase:     int					)				->		list[int]:
       A						:     Tuple         =[-1] * (current_row_idx + 1)
       # first and last elements of current row are equal to 1
       A						:     List[str]         =1, 1
       for current_col_idx in range(1,		lowercase					):
              calculate_current_element(
                  lowercase,		lowercase,		lowercase,		lowercase					)
       return current_row



def A__  (			lowercase:     list[list[int]],		lowercase:     list[int],		lowercase:     int,		lowercase:     int,		)				->		None:
       A						:     Optional[int]         =triangle[current_row_idx - 1][current_col_idx - 1]
       A						:     Optional[Any]         =triangle[current_row_idx - 1][current_col_idx]
       A						:     int         =above_to_left_elt + above_to_right_elt



def A__  (			lowercase:     int					)				->		list[list[int]]:

       if not isinstance(lowercase,		lowercase					):
              raise TypeError('The input value of \'num_rows\' should be \'int\''					)

       if num_rows == 0:
              return []
       elif num_rows < 0:
              raise ValueError(
                  'The input value of \'num_rows\' should be greater than or equal to 0'					)

       A						:     list[list[int]]         =[[1]]

       for row_index in range(1,		lowercase					):
              A						:     Dict         =[0] + result[-1] + [0]
              A						:     Union[str, Any]         =row_index + 1
              # Calculate the number of distinct elements in a row
              A						:     Any         =sum(divmod(lowercase,		2					)					)
              A						:     Any         =[
                  temp_row[i - 1] + temp_row[i] for i in range(1,		distinct_elements + 1					)
              ]
              A						:     Dict         =row_first_half[: (row_index + 1) // 2]
              row_second_half.reverse()
              A						:     Any         =row_first_half + row_second_half
              result.append(lowercase					)

       return result



def A__  (			)				->		None:
       from collections.abc import Callable
       from timeit import timeit

       def benchmark_a_function(lowercase:     Callable,		lowercase:     int					) -> None:
              A						:     int         =F'{func.__name__}({value})'
              A						:     Optional[int]         =timeit(F'__main__.{call}',		setup='import __main__'					)
              # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
              print(F'{call:38} -- {timing:.4f} seconds'					)

       for value in range(15					):  # (1, 7, 14):
              for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
                     benchmark_a_function(lowercase,		lowercase					)
              print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()







# 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.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase							:      List[str]      							='''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def A__  (			)				->		List[Any]:
       A						:     Any         =_ask_options(
           'In which compute environment are you running?',		['This machine', 'AWS (Amazon SageMaker)'],		_convert_compute_environment,		)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A						:     Tuple         =get_sagemaker_input()
       else:
              A						:     str         =get_cluster_input()
       return config



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=lowercase					)

       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 A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

       if config_file.endswith('.json'					):
              config.to_json_file(lowercase					)
       else:
              config.to_yaml_file(lowercase					)
       print(F'accelerate configuration saved at {config_file}'					)



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






