


'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase      :List[Any]    	=			logging.get_logger(__name__)

lowerCAmelCase      :str    	=			{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		A_	:			Optional[Any]          =				'xlm-roberta-xl'







		def __init__(					self		:						str  ,     _A		:						str=250880  ,     _A		:						Any=2560  ,     _A		:						Optional[Any]=36  ,     _A		:						str=32  ,     _A		:						Optional[int]=10240  ,     _A		:						List[Any]="gelu"  ,     _A		:						Tuple=0.1  ,     _A		:						Tuple=0.1  ,     _A		:						Dict=514  ,     _A		:						Optional[Any]=1  ,     _A		:						List[str]=0.02  ,     _A		:						str=1E-05  ,     _A		:						Optional[Any]=1  ,     _A		:						Optional[int]=0  ,     _A		:						List[str]=2  ,     _A		:						str="absolute"  ,     _A		:						Dict=True  ,     _A		:						Optional[int]=None  ,     **_A		:						Dict  ,     )  -> List[str]:
			super().__init__(pad_token_id=_A  ,     bos_token_id=_A  ,     eos_token_id=_A  ,     **_A			)
			__magic_name__       :    Optional[Any]     	=     vocab_size
			__magic_name__       :    int     	=     hidden_size
			__magic_name__       :    Optional[Any]     	=     num_hidden_layers
			__magic_name__       :    Tuple     	=     num_attention_heads
			__magic_name__       :    Dict     	=     hidden_act
			__magic_name__       :    Dict     	=     intermediate_size
			__magic_name__       :    Any     	=     hidden_dropout_prob
			__magic_name__       :    Any     	=     attention_probs_dropout_prob
			__magic_name__       :    Any     	=     max_position_embeddings
			__magic_name__       :    int     	=     type_vocab_size
			__magic_name__       :    Optional[int]     	=     initializer_range
			__magic_name__       :    Optional[int]     	=     layer_norm_eps
			__magic_name__       :    Optional[int]     	=     position_embedding_type
			__magic_name__       :    Dict     	=     use_cache
			__magic_name__       :    List[str]     	=     classifier_dropout







class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		@property
		def    __lowerCAmelCase		(					self		:						Optional[Any]			)  -> Mapping[str, Mapping[int, str]]:



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




'''simple docstring'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__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 lowerCAmelCase  (__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.''')

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




def lowerCAmelCase  (__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:
          _a      =  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).''')

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


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



