

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_ta import TaTokenizer
else:
   _SCREAMING_SNAKE_CASE  :						Optional[int]         =    None


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE  :						str         =    {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      ["input_ids", "attention_mask"]
  a     =      TaTokenizer

  a     =      []






  def __init__(							self		:	str			,     __lowerCamelCase		:	Optional[int]=None			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Dict="</s>"			,     __lowerCamelCase		:	List[str]="<unk>"			,     __lowerCamelCase		:	Any="<pad>"			,     __lowerCamelCase		:	Tuple=100			,     __lowerCamelCase		:	int=None			,     **__lowerCamelCase		:	List[str]			,     )	->							Any:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
            SCREAMING_SNAKE_CASE__					=					[f'''<extra_id_{i}>''' for i in range(__lowerCamelCase      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            SCREAMING_SNAKE_CASE__					=					len(set(filter(lambda __lowerCamelCase      : bool('''extra_id_''' in str(__lowerCamelCase      )      )			,     __lowerCamelCase      )      )      )
            if extra_tokens != extra_ids:
                 raise ValueError(
                     f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''      )

       super().__init__(
           __lowerCamelCase			,     tokenizer_file=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     extra_ids=__lowerCamelCase			,     additional_special_tokens=__lowerCamelCase			,     **__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					vocab_file
       SCREAMING_SNAKE_CASE__					=					False if not self.vocab_file else True
       SCREAMING_SNAKE_CASE__					=					extra_ids






  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	int      )	->							Tuple:
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            SCREAMING_SNAKE_CASE__					=					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
            elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                     f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                     f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''			,     __lowerCamelCase			,     )

       return max_model_length






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       if not self.can_save_slow_tokenizer:
            raise ValueError(
                '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                '''tokenizer.'''      )

       if not os.path.isdir(__lowerCamelCase      ):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
            return
       SCREAMING_SNAKE_CASE__					=					os.path.join(
           __lowerCamelCase			,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
            copyfile(self.vocab_file			,     __lowerCamelCase      )
            logger.info(f'''Copy vocab file to {out_vocab_file}'''      )

       return (out_vocab_file,)






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
       else:
            SCREAMING_SNAKE_CASE__					=					token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.eos_token_id]

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






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[int]:
       return list(
           set(filter(lambda __lowerCamelCase      : bool(re.search(r'''<extra_id_\d+>'''			,     __lowerCamelCase      )      ) is not None			,     self.additional_special_tokens      )      )      )







  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       return [self.convert_tokens_to_ids(__lowerCamelCase      ) for token in self.get_sentinel_tokens()]





import math






class  UpperCAmelCase__	:






  """simple docstring"""


  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	list[list[float]]			,     __lowerCamelCase		:	list[int]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0.0
       SCREAMING_SNAKE_CASE__					=					0.0
       for i in range(len(__lowerCamelCase      )      ):
            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		:	Optional[int]			,     __lowerCamelCase		:	list[list[int | float]]			,     __lowerCamelCase		:	list[int]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	float      )	->							list[list[int | float]]:
       for i in range(len(__lowerCamelCase      )      ):
            weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

     # training
     SCREAMING_SNAKE_CASE__					=					SelfOrganizingMap()
     SCREAMING_SNAKE_CASE__					=					3
     SCREAMING_SNAKE_CASE__					=					0.5

     for _ in range(_A       ):
          for j in range(len(_A       )       ):
               # training sample
               SCREAMING_SNAKE_CASE__					=					training_samples[j]

               # Compute the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

               # Update the winning vector
               SCREAMING_SNAKE_CASE__					=					self_organizing_map.update(_A   ,	_A   ,	_A   ,	_A       )

    # classify test sample
     SCREAMING_SNAKE_CASE__					=					[0, 0, 0, 1]
     SCREAMING_SNAKE_CASE__					=					self_organizing_map.get_winner(_A   ,	_A       )

     # 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()


