


'''simple docstring'''
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

UpperCamelCase__    					=      {'''allegro/herbert-base-cased''': 5_1_4}
UpperCamelCase__    					=      {}




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_INIT_CONFIGURATION
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     HerbertTokenizer






      def __init__(					self				:    Optional[Any]						,						_A				:    str=None						,						_A				:    Union[str, Any]=None						,						_A				:    Union[str, Any]=None						,						_A				:    List[str]="<s>"						,						_A				:    Union[str, Any]="<unk>"						,						_A				:    Any="<pad>"						,						_A				:    Union[str, Any]="<mask>"						,						_A				:    Union[str, Any]="</s>"						,						**_A				:    str						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						_A						,						tokenizer_file=_A						,						cls_token=_A						,						unk_token=_A						,						pad_token=_A						,						mask_token=_A						,						sep_token=_A						,						**_A						,						)






      def lowercase_							(					self				:    List[Any]						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				[self.cls_token_id]
          UpperCAmelCase__			:		List[str]													=				[self.sep_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep

          return cls + token_ids_a + sep + token_ids_a + sep






      def lowercase_							(					self				:    Dict						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None						,						_A				:    bool = False ):


          '''simple docstring'''


          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_A						,						token_ids_a=_A						,						already_has_special_tokens=_A )

          if token_ids_a is None:
              return [1] + ([0] * len(_A )) + [1]
          return [1] + ([0] * len(_A )) + [1] + ([0] * len(_A )) + [1]






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				[self.sep_token_id]
          UpperCAmelCase__			:		Any													=				[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    str						,						_A				:    Optional[str] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self._tokenizer.model.save(_A						,						name=_A )
          return tuple(_A )




'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              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 lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # 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()
