


'''simple docstring'''




import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def 	a_					( lowerCamelCase	:     Any				):
   return EnvironmentCommand()





def 	a_					( lowerCamelCase	:     Optional[Any]				):
   return EnvironmentCommand(args.accelerate_config_file				)




class 		UpperCAmelCase_      (  __lowercase		):





     @staticmethod
     def 							__UpperCAmelCase      ( UpperCAmelCase__       :      ArgumentParser    )				->  str:
        lowerCAmelCase     =			parser.add_parser('env'    )
        download_parser.set_defaults(func=__UpperCAmelCase    )
        download_parser.add_argument(
            '--accelerate-config_file'    ,      default=__UpperCAmelCase    ,      help='The accelerate config file to use for the default values in the launching script.'    ,      )
        download_parser.set_defaults(func=__UpperCAmelCase    )





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Any    ,      *UpperCAmelCase__       :      List[Any]    )				->  None:
        lowerCAmelCase     =			accelerate_config_file





     def 							__UpperCAmelCase      ( self       :      int    )				->  Tuple:
        lowerCAmelCase     =			'not installed'
        if is_safetensors_available():
           import safetensors

           lowerCAmelCase     =			safetensors.__version__
        elif importlib.util.find_spec('safetensors'    ) is not None:
           import safetensors

           lowerCAmelCase     =			F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			lowerCAmelCase     =			'not found'
        if is_accelerate_available():
           import accelerate
           from accelerate.commands.config import default_config_file, load_config_from_file

           lowerCAmelCase     =			accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(__UpperCAmelCase    ):
              lowerCAmelCase     =			load_config_from_file(self._accelerate_config_file    ).to_dict()

           lowerCAmelCase     =			(
               '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]    )
               if isinstance(__UpperCAmelCase    ,      __UpperCAmelCase    )
               else F'''\t{accelerate_config}'''
           )

        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			'NA'
        if is_torch_available():
           import torch

           lowerCAmelCase     =			torch.__version__
           lowerCAmelCase     =			torch.cuda.is_available()

        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			'NA'
        if is_tf_available():
           import tensorflow as tf

           lowerCAmelCase     =			tf.__version__
           try:
              # deprecated in v2.1
              lowerCAmelCase     =			tf.test.is_gpu_available()
           except AttributeError:
              # returns list of devices, convert to bool
              lowerCAmelCase     =			bool(tf.config.list_physical_devices('GPU'    )    )

        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			'not installed'
        lowerCAmelCase     =			'NA'
        if is_flax_available():
           import flax
           import jax
           import jaxlib

           lowerCAmelCase     =			flax.__version__
           lowerCAmelCase     =			jax.__version__
           lowerCAmelCase     =			jaxlib.__version__
           lowerCAmelCase     =			jax.lib.xla_bridge.get_backend().platform

        lowerCAmelCase     =			{
            '`transformers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'Huggingface_hub version': huggingface_hub.__version__,
            'Safetensors version': F'''{safetensors_version}''',
            'Accelerate version': F'''{accelerate_version}''',
            'Accelerate config': F'''{accelerate_config_str}''',
            'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
            'Tensorflow version (GPU?)': F'''{tf_version} ({tf_cuda_available})''',
            'Flax version (CPU?/GPU?/TPU?)': F'''{flax_version} ({jax_backend})''',
            'Jax version': F'''{jax_version}''',
            'JaxLib version': F'''{jaxlib_version}''',
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'    )
        print(self.format_dict(__UpperCAmelCase    )    )

        return info





     @staticmethod
     def 							__UpperCAmelCase      ( UpperCAmelCase__       :      List[Any]    )				->  Any:
        return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]    ) + "\n"













from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCAmelCase__				   =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 for i in range(9 ):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3 ):
  for j in range(3 ):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True
def 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1	,							10 ):
  if is_safe(a	,							a	,							a	,							a ):
   a		  =      digit

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							end=''' ''' )
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("\nExample grid:\n" + "=" * 20)
          print_solution(example_grid)
          print("\nExample grid solution:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


