




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       :      List[str]  =   '▁'

SCREAMING_SNAKE_CASE__       :      Any  =   {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__       :      Dict  =   {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    'google/reformer-crime-and-punishment': 524288,
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	List[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	str	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	Optional[Any]	     =      ["""input_ids""", """attention_mask"""]







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__=[]     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      None:
           lowerCamelCase   :      List[Any]			 =					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      str			 =					vocab_file
           lowerCamelCase   :      List[Any]			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      Dict:
           return self.sp_model.get_piece_size()







    def     _lowercase     (    self     )     ->      Dict[str, int]:
           lowerCamelCase   :      Optional[Any]			 =					{self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
           vocab.update(self.added_tokens_encoder     )
           return vocab







    def __getstate__(    self     )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					self.__dict__.copy()
           lowerCamelCase   :      Dict			 =					None
           return state







    def __setstate__(    self     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase   :      Any			 =					d

           # for backward compatibility
           if not hasattr(self     ,  "sp_model_kwargs"     ):
                  lowerCamelCase   :      Any			 =					{}

           lowerCamelCase   :      int			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(self.vocab_file     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           return self.sp_model.encode(UpperCamelCase__     ,  out_type=UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           return self.sp_model.piece_to_id(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           if index < self.sp_model.get_piece_size():
                  lowerCamelCase   :      Dict			 =					self.sp_model.IdToPiece(UpperCamelCase__     )
           return token







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           lowerCamelCase   :      Any			 =					[]
           lowerCamelCase   :      str			 =					""
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCamelCase__     ) + token
                         lowerCamelCase   :      Union[str, Any]			 =					[]
                  else:
                         current_sub_tokens.append(UpperCamelCase__     )
           out_string += self.sp_model.decode(UpperCamelCase__     )
           return out_string.strip()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Optional[int]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
                  copyfile(self.vocab_file     ,  UpperCamelCase__     )
           elif not os.path.isfile(self.vocab_file     ):
                  with open(UpperCamelCase__     ,  "wb"     ) as fi:
                         lowerCamelCase   :      Tuple			 =					self.sp_model.serialized_model_proto()
                         fi.write(UpperCamelCase__     )

           return (out_vocab_file,)











"""simple docstring"""
import math
class 				lowerCamelCase  :






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							list[list[float]]						,     snake_case:							list[int]						)   ->	int:
											snake_case_ :Any						=			0.0
											snake_case_ :Tuple						=			0.0
											for i in range(len(snake_case						)						):
																	da += math.pow((sample[i] - weights[0][i])						,     2						)
																	da += math.pow((sample[i] - weights[1][i])						,     2						)
																	return 0 if da > da else 1
											return 0






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							list[list[int | float]]						,     snake_case:							list[int]						,     snake_case:							int						,     snake_case:							float						)   ->	list[list[int | float]]:
											for i in range(len(snake_case						)						):
																	weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						snake_case_ :List[Any]						=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						snake_case_ :Optional[Any]						=			SelfOrganizingMap()
						snake_case_ :Dict						=			3
						snake_case_ :Dict						=			0.5

						for _ in range(_lowercase	):
												for j in range(len(_lowercase	)	):
																		# training sample
																		snake_case_ :List[Any]						=			training_samples[j]

																		# Compute the winning vector
																		snake_case_ :Optional[int]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

																		# Update the winning vector
																		snake_case_ :List[str]						=			self_organizing_map.update(_lowercase,					_lowercase,					_lowercase,					_lowercase	)

    # classify test sample
						snake_case_ :str						=			[0, 0, 0, 1]
						snake_case_ :List[Any]						=			self_organizing_map.get_winner(_lowercase,					_lowercase	)

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""	)
						print(f"""Weights that have been trained : {weights}"""	)


# running the main() function
if __name__ == "__main__":
						main()
