

"""simple docstring"""


from __future__ import annotations

from typing import TypedDict

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:str
     _SCREAMING_SNAKE_CASE						:int





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

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




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

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				str:
      if not isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
            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:
            SCREAMING_SNAKE_CASE__   :      str      		=	int(__lowerCAmelCase							)
      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(__lowerCAmelCase							):
            raise ValueError(
                """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""							)

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	[""""""] * len(__lowerCAmelCase							)
      for _ in range(len(__lowerCAmelCase							)							):
            for i in range(len(__lowerCAmelCase							)							):
                  SCREAMING_SNAKE_CASE__   :      Any      		=	bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


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


from typing import TYPE_CHECKING

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


a					: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					:Union[str, 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					:Optional[Any]    = [
      "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					:List[Any]    = [
      "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					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




