





'''simple docstring'''




import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce






def        _lowerCAmelCase			(  __snake_case  : List[Any]			)     ->     Union[str, Any]:
     return (torch.arange(state.num_processes			) + 1.0 + (state.num_processes * state.process_index)).to(state.device			)






def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     List[Any]:
     __A						: List[str]      =       create_tensor(_UpperCAmelCase			)
     __A						: Tuple      =       gather(_UpperCAmelCase			)
     assert gathered_tensor.tolist() == list(range(1 ,							state.num_processes**2 + 1			)			)






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     __A						: Any      =       [state.process_index]
     __A						: List[Any]      =       gather_object(_UpperCAmelCase			)
     assert len(_UpperCAmelCase			) == state.num_processes, f'{gathered_obj}, {len(_UpperCAmelCase			)} != {state.num_processes}'
     assert gathered_obj == list(range(state.num_processes			)			), f'{gathered_obj} != {list(range(state.num_processes			)			)}'






def        _lowerCAmelCase			(  __snake_case  : Optional[Any]			)     ->     Tuple:
     __A						: Any      =       create_tensor(_UpperCAmelCase			)
     __A						: Tuple      =       broadcast(_UpperCAmelCase			)
     assert broadcasted_tensor.shape == torch.Size([state.num_processes]			)
     assert broadcasted_tensor.tolist() == list(range(1 ,							state.num_processes + 1			)			)






def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     int:
     # We need to pad the tensor with one more element if we are the main process
     # to ensure that we can pad
     if state.is_main_process:
          __A						: Dict      =       torch.arange(state.num_processes + 1			).to(state.device			)
     else:
          __A						: List[Any]      =       torch.arange(state.num_processes			).to(state.device			)
     __A						: Tuple      =       pad_across_processes(_UpperCAmelCase			)
     assert padded_tensor.shape == torch.Size([state.num_processes + 1]			)
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0 ,							state.num_processes			)			) + [0]






def        _lowerCAmelCase			(  __snake_case  : str			)     ->     List[str]:
     # For now runs on only two processes
     if state.num_processes != 2:
          return
     __A						: Optional[Any]      =       create_tensor(_UpperCAmelCase			)
     __A						: Optional[int]      =       reduce(_UpperCAmelCase ,							'sum'			)
     __A						: int      =       torch.tensor([4.0, 6]			).to(state.device			)
     assert torch.allclose(_UpperCAmelCase ,							_UpperCAmelCase			), f'{reduced_tensor} != {truth_tensor}'






def        _lowerCAmelCase			(  __snake_case  : Optional[int]			)     ->     Union[str, Any]:
     # For now runs on only two processes
     if state.num_processes != 2:
          return
     __A						: Tuple      =       create_tensor(_UpperCAmelCase			)
     __A						: Any      =       reduce(_UpperCAmelCase ,							'mean'			)
     __A						: Any      =       torch.tensor([2.0, 3]			).to(state.device			)
     assert torch.allclose(_UpperCAmelCase ,							_UpperCAmelCase			), f'{reduced_tensor} != {truth_tensor}'






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Union[str, Any]:
     # For xla_spawn (TPUs)
     main()






def        _lowerCAmelCase			(  )     ->     str:
     __A						: str      =       PartialState()
     state.print(f'State: {state}'			)
     state.print('testing gather'			)
     test_gather(_UpperCAmelCase			)
     state.print('testing gather_object'			)
     test_gather_object(_UpperCAmelCase			)
     state.print('testing broadcast'			)
     test_broadcast(_UpperCAmelCase			)
     state.print('testing pad_across_processes'			)
     test_pad_across_processes(_UpperCAmelCase			)
     state.print('testing reduce_sum'			)
     test_reduce_sum(_UpperCAmelCase			)
     state.print('testing reduce_mean'			)
     test_reduce_mean(_UpperCAmelCase			)


if __name__ == "__main__":
  main()



'''simple docstring'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


