


'''simple docstring'''
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      {'''vocab_file''': '''vocab.txt'''}

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

UpperCamelCase__    					=      {
    '''facebook/esm2_t6_8M_UR50D''': 1_0_2_4,
    '''facebook/esm2_t12_35M_UR50D''': 1_0_2_4,
}





def 					a__       (					lowerCAmelCase__						)    ->	Union[str, Any]:
    with open(lowerCAmelCase__  ,					'''r'''						) as f:
        UpperCAmelCase__			:		List[Any]													=				f.read().splitlines()
        return [l.strip() for l in lines]







class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']






      def __init__(					self				:    Optional[Any]						,						_A				:    str						,						_A				:    List[Any]="<unk>"						,						_A				:    str="<cls>"						,						_A				:    Tuple="<pad>"						,						_A				:    Any="<mask>"						,						_A				:    Any="<eos>"						,						**_A				:    List[Any]						,						):


          '''simple docstring'''


          super().__init__(**_A )
          UpperCAmelCase__			:		List[Any]													=				load_vocab_file(_A )
          UpperCAmelCase__			:		Tuple													=				dict(enumerate(self.all_tokens ) )
          UpperCAmelCase__			:		Optional[int]													=				{tok: ind for ind, tok in enumerate(self.all_tokens )}
          UpperCAmelCase__			:		Tuple													=				unk_token
          UpperCAmelCase__			:		Optional[Any]													=				cls_token
          UpperCAmelCase__			:		Any													=				pad_token
          UpperCAmelCase__			:		str													=				mask_token
          UpperCAmelCase__			:		Tuple													=				eos_token
          UpperCAmelCase__			:		List[str]													=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens )






      def lowercase_							(					self				:    Any						,						_A				:    int ):


          '''simple docstring'''


          return self._id_to_token.get(_A						,						self.unk_token )






      def lowercase_							(					self				:    List[Any]						,						_A				:    str ):


          '''simple docstring'''


          return self._token_to_id.get(_A						,						self._token_to_id.get(self.unk_token ) )






      def lowercase_							(					self				:    Dict						,						_A				:    Optional[int]						,						**_A				:    Optional[Any] ):


          '''simple docstring'''


          return text.split()






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Union[str, Any]=False ):


          '''simple docstring'''


          return len(self._id_to_token )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return {token: i for i, token in enumerate(self.all_tokens )}






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str ):


          '''simple docstring'''


          return self._token_to_id.get(_A						,						self._token_to_id.get(self.unk_token ) )






      def lowercase_							(					self				:    Dict						,						_A				:    int ):


          '''simple docstring'''


          return self._id_to_token.get(_A						,						self.unk_token )






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


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				[self.cls_token_id]
          UpperCAmelCase__			:		List[str]													=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
              if self.eos_token_id is None:
                  return cls + token_ids_a
              else:
                  return cls + token_ids_a + sep
          elif self.eos_token_id is None:
              raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token






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


          '''simple docstring'''


          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      '''You should not supply a second sequence if the provided sequence of '''
                      '''ids is already formatted with special tokens for the model.''' )

              return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          UpperCAmelCase__			:		List[Any]													=				[1] + ([0] * len(_A )) + [1]
          if token_ids_a is not None:
              mask += [0] * len(_A ) + [1]
          return mask






      def lowercase_							(					self				:    List[str]						,						_A				:    str						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				os.path.join(_A						,						(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
          with open(_A						,						'''w''' ) as f:
              f.write('''\n'''.join(self.all_tokens ) )
          return (vocab_file,)






      @property
      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          return self.get_vocab_size(with_added_tokens=_A )






      def lowercase_							(					self				:    List[str]						,						_A				:    Union[List[str], List[AddedToken]]						,						_A				:    bool = False ):


          '''simple docstring'''


          return super()._add_tokens(_A						,						special_tokens=_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()
