



"""simple docstring"""




from __future__ import annotations

from typing import TypedDict






class       UpperCAmelCase_    (							__lowercase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					4_2
      __SCREAMING_SNAKE_CASE		:  str						=					4_2







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     if not isinstance(__snake_case		,    __snake_case							):
          raise TypeError("The parameter s type must be str."							)

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     if not isinstance(__snake_case		,    __snake_case							):
          raise TypeError("The parameter s type must be str."							)
     if not s:
          raise ValueError("The parameter s must not be empty."							)

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     if not isinstance(__snake_case		,    __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     :      List[Any]														=       int(__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(__snake_case							):
          raise ValueError(
              "The parameter idx_original_string must be lower than" " len(bwt_string)."							)

     _UpperCAmelCase     :      List[Any]														=       [""] * len(__snake_case							)
     for _ in range(len(__snake_case							)							):
          for i in range(len(__snake_case							)							):
               _UpperCAmelCase     :      Optional[int]														=       bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 _lowerCAmelCase							:   Dict				=				"Provide a string that I will generate its BWT transform: "
 _lowerCAmelCase							:   Dict				=				input(entry_msg).strip()
 _lowerCAmelCase							:   Optional[int]				=				bwt_transform(s)
 print(
     F"Burrows Wheeler transform for string \'{s}\' results "
     F"in \'{result['bwt_string']}\'"
 )
 _lowerCAmelCase							:   Optional[Any]				=				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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

												super().__init__(
												    pad_token_id=_A ,			bos_token_id=_A ,			eos_token_id=_A ,			decoder_start_token_id=_A ,			**_A ,			)
