





from __future__ import annotations

from typing import TypedDict



class        _lowerCAmelCase    (			__snake_case							):


  '''simple docstring'''
  a_							:       Optional[Any]      					=42
  a_							:       int      					=42




def     lowerCamelCase_	(      lowerCAmelCase:	List[str]				)->		Tuple:
  if not isinstance(lowercase__    ,							lowercase__				):
    raise TypeError('The parameter s type must be str.'				)

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


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		Tuple:
  if not isinstance(lowercase__    ,							lowercase__				):
    raise TypeError('The parameter s type must be str.'				)
  if not s:
    raise ValueError('The parameter s must not be empty.'				)

  _snake_case   :				List[str]														=      all_rotations(lowercase__				)
  rotations.sort()  # sort the list of rotations in alphabetically order
  # make a string composed of the last char of each rotation
  _snake_case   :				Optional[int]														=      {
      'bwt_string': ''.join([word[-1] for word in rotations]				),
      'idx_original_string': rotations.index(lowercase__				),
  }
  return response


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Tuple				)->		Union[str, Any]:
  if not isinstance(lowercase__    ,							lowercase__				):
    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:
    _snake_case   :				Dict														=      int(lowercase__				)
  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(lowercase__				):
    raise ValueError(
        'The parameter idx_original_string must be lower than' ' len(bwt_string).'				)

  _snake_case   :				Optional[int]														=      [''] * len(lowercase__				)
  for _ in range(len(lowercase__				)				):
    for i in range(len(lowercase__				)				):
      _snake_case   :				str														=      bwt_string[i] + ordered_rotations[i]
    ordered_rotations.sort()
  return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowerCAmelCase_						   =    """Provide a string that I will generate its BWT transform: """
      lowerCAmelCase_						   =    input(entry_msg).strip()
      lowerCAmelCase_						   =    bwt_transform(s)
      print(
          F"""Burrows Wheeler transform for string '{s}' results """
          F"""in '{result["bwt_string"]}'"""
      )
      lowerCAmelCase_						   =    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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       int      					="""roberta"""







  def __init__(      self      :					int      ,				UpperCamelCase      :					Tuple=5_02_65      ,				UpperCamelCase      :					Any=7_68      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Dict=30_72      ,				UpperCamelCase      :					Any="gelu"      ,				UpperCamelCase      :					List[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=5_12      ,				UpperCamelCase      :					List[str]=2      ,				UpperCamelCase      :					Optional[Any]=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					str=1      ,				UpperCamelCase      :					int=0      ,				UpperCamelCase      :					Any=2      ,				UpperCamelCase      :					int="absolute"      ,				UpperCamelCase      :					int=True      ,				UpperCamelCase      :					List[Any]=None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase      ,				bos_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Any														=	vocab_size
    _snake_case   :				List[str]														=	hidden_size
    _snake_case   :				List[str]														=	num_hidden_layers
    _snake_case   :				Dict														=	num_attention_heads
    _snake_case   :				List[str]														=	hidden_act
    _snake_case   :				Union[str, Any]														=	intermediate_size
    _snake_case   :				Union[str, Any]														=	hidden_dropout_prob
    _snake_case   :				Optional[int]														=	attention_probs_dropout_prob
    _snake_case   :				Dict														=	max_position_embeddings
    _snake_case   :				Optional[int]														=	type_vocab_size
    _snake_case   :				Tuple														=	initializer_range
    _snake_case   :				int														=	layer_norm_eps
    _snake_case   :				Dict														=	position_embedding_type
    _snake_case   :				Union[str, Any]														=	use_cache
    _snake_case   :				str														=	classifier_dropout




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case   :				Optional[Any]														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case   :				Dict														=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]							)




