


'''simple docstring'''







from __future__ import annotations

from typing import TypedDict
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    42
     __magic_name__    			=    42






def 			lowercase				(_A					):







       """simple docstring"""







       if not isinstance(_A   ,						_A					):
              raise TypeError('The parameter s type must be str.'					)

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






def 			lowercase				(_A					):







       """simple docstring"""







       if not isinstance(_A   ,						_A					):
              raise TypeError('The parameter s type must be str.'					)
       if not s:
              raise ValueError('The parameter s must not be empty.'					)

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






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       if not isinstance(_A   ,						_A					):
              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:
              _lowerCAmelCase    :							Any   			=       int(_A					)
       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(_A					):
              raise ValueError(
                  'The parameter idx_original_string must be lower than' ' len(bwt_string).'					)

       _lowerCAmelCase    :							Optional[int]   			=       [''] * len(_A					)
       for _ in range(len(_A					)					):
              for i in range(len(_A					)					):
                     _lowerCAmelCase    :							Optional[Any]   			=       bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
       lowerCAmelCase    :     Optional[int]					=						"""Provide a string that I will generate its BWT transform: """
       lowerCAmelCase    :     str					=						input(entry_msg).strip()
       lowerCAmelCase    :     Tuple					=						bwt_transform(s)
       print(
           F'''Burrows Wheeler transform for string \'{s}\' results '''
           F'''in \'{result["bwt_string"]}\''''
       )
       lowerCAmelCase    :     Optional[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}\''''
       )





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

