





'''simple docstring'''






from __future__ import annotations

from typing import TypedDict
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   42
	snake_case_      =   42






def      __A						(							a_						:  str	):
		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      __A						(							a_						:  str	):
		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."	)

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






def      __A						(							a_						:  str      ,a_						:  int	):
		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:
				lowerCAmelCase     :    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)."	)

		lowerCAmelCase     :    Tuple						=   [""] * len(a_	)
		for _ in range(len(a_	)	):
				for i in range(len(a_	)	):
						lowerCAmelCase     :    Optional[int]						=   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}\''''
					)










'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 		lowerCamelCase  (      _A      ,   _A      ,   _A       ):



	@register_to_config
	def __init__(   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_ = False		,      ):
			super().__init__()

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)
			lowerCAmelCase     :    int						=   False

			lowerCAmelCase     :    int						=   nn.Dropout(p=a_	)

			lowerCAmelCase     :    str						=   TaConfig(
			    vocab_size=a_		,      d_model=a_		,      num_heads=a_		,      d_kv=a_		,      d_ff=a_		,      dropout_rate=a_		,      feed_forward_proj=a_		,      is_decoder=a_		,      is_encoder_decoder=a_		,      )

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for lyr_num in range(a_	):
					lowerCAmelCase     :    Optional[Any]						=   TaBlock(a_	)
					self.encoders.append(a_	)

			lowerCAmelCase     :    List[str]						=   TaLayerNorm(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   nn.Dropout(p=a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.token_embedder(a_	)

			lowerCAmelCase     :    int						=   encoder_input_tokens.shape[1]
			lowerCAmelCase     :    Optional[int]						=   torch.arange(a_		,      device=encoder_input_tokens.device	)
			x += self.position_encoding(a_	)

			lowerCAmelCase     :    List[str]						=   self.dropout_pre(a_	)

			# inverted the attention mask
			lowerCAmelCase     :    List[Any]						=   encoder_input_tokens.size()
			lowerCAmelCase     :    Dict						=   self.get_extended_attention_mask(a_		,      a_	)

			for lyr in self.encoders:
					lowerCAmelCase     :    Dict						=   lyr(a_		,      a_	)[0]
			lowerCAmelCase     :    int						=   self.layer_norm(a_	)

			return self.dropout_post(a_	), encoder_inputs_mask



