





from __future__ import annotations

from typing import TypedDict




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       42
      UpperCamelCase__  		=       42



def 				lowerCAmelCase_ (							A_):
    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 				lowerCAmelCase_ (							A_):
    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.")

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



def 				lowerCAmelCase_ (							A_    ,A_):
    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:
        UpperCamelCase__:	Optional[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).")

    UpperCamelCase__:	int					=     [""] * len(A_)
    for _ in range(len(A_)):
        for i in range(len(A_)):
            UpperCamelCase__:	List[str]					=     bwt_string[i] + ordered_rotations[i]
        ordered_rotations.sort()
    return ordered_rotations[idx_original_string]


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


A__:    Optional[Any]  	=	{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[Any]  	=	[
       '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RobertaPreLayerNormForCausalLM''',
       '''RobertaPreLayerNormForMaskedLM''',
       '''RobertaPreLayerNormForMultipleChoice''',
       '''RobertaPreLayerNormForQuestionAnswering''',
       '''RobertaPreLayerNormForSequenceClassification''',
       '''RobertaPreLayerNormForTokenClassification''',
       '''RobertaPreLayerNormModel''',
       '''RobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Tuple  	=	[
       '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRobertaPreLayerNormForCausalLM''',
       '''TFRobertaPreLayerNormForMaskedLM''',
       '''TFRobertaPreLayerNormForMultipleChoice''',
       '''TFRobertaPreLayerNormForQuestionAnswering''',
       '''TFRobertaPreLayerNormForSequenceClassification''',
       '''TFRobertaPreLayerNormForTokenClassification''',
       '''TFRobertaPreLayerNormMainLayer''',
       '''TFRobertaPreLayerNormModel''',
       '''TFRobertaPreLayerNormPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''FlaxRobertaPreLayerNormForCausalLM''',
       '''FlaxRobertaPreLayerNormForMaskedLM''',
       '''FlaxRobertaPreLayerNormForMultipleChoice''',
       '''FlaxRobertaPreLayerNormForQuestionAnswering''',
       '''FlaxRobertaPreLayerNormForSequenceClassification''',
       '''FlaxRobertaPreLayerNormForTokenClassification''',
       '''FlaxRobertaPreLayerNormModel''',
       '''FlaxRobertaPreLayerNormPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roberta_prelayernorm import (
       ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
       RobertaPreLayerNormConfig,
       RobertaPreLayerNormOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta_prelayernorm import (
          ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaPreLayerNormForCausalLM,
          RobertaPreLayerNormForMaskedLM,
          RobertaPreLayerNormForMultipleChoice,
          RobertaPreLayerNormForQuestionAnswering,
          RobertaPreLayerNormForSequenceClassification,
          RobertaPreLayerNormForTokenClassification,
          RobertaPreLayerNormModel,
          RobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta_prelayernorm import (
          TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaPreLayerNormForCausalLM,
          TFRobertaPreLayerNormForMaskedLM,
          TFRobertaPreLayerNormForMultipleChoice,
          TFRobertaPreLayerNormForQuestionAnswering,
          TFRobertaPreLayerNormForSequenceClassification,
          TFRobertaPreLayerNormForTokenClassification,
          TFRobertaPreLayerNormMainLayer,
          TFRobertaPreLayerNormModel,
          TFRobertaPreLayerNormPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormPreTrainedModel,
      )

else:
   import sys

   A__:    Union[str, Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

