


from __future__ import annotations

from typing import TypedDict






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str
    UpperCAmelCase__						:		int

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

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

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

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					int				):
     if not isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
          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:
          __UpperCamelCase    							=int(SCREAMING_SNAKE_CASE__				)
     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(SCREAMING_SNAKE_CASE__				):
          raise ValueError(
              'The parameter idx_original_string must be lower than' ' len(bwt_string).'				)

     __UpperCamelCase    							=[''] * len(SCREAMING_SNAKE_CASE__				)
     for _ in range(len(SCREAMING_SNAKE_CASE__				)				):
          for i in range(len(SCREAMING_SNAKE_CASE__				)				):
               __UpperCamelCase    							=bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


