






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


snake_case__   :						Optional[Any]													=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case__   :						Optional[int]													=   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

snake_case__   :						Tuple													=   {
    """allenai/led-base-16384""": 1_6_3_8_4,
}


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = VOCAB_FILES_NAMES
  A_						       = PRETRAINED_VOCAB_FILES_MAP
  A_						       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						       = LEDTokenizer
  A_						       = ["""input_ids""", """attention_mask"""]




  def __init__(  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase="replace"		,		_UpperCAmelCase="<s>"		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="<s>"		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase="<pad>"		,		_UpperCAmelCase="<mask>"		,		_UpperCAmelCase=False		,		_UpperCAmelCase=True		,		**_UpperCAmelCase		,		)      ->			Tuple:
     super().__init__(
         _UpperCAmelCase		,		_UpperCAmelCase		,		tokenizer_file=_UpperCAmelCase		,		errors=_UpperCAmelCase		,		bos_token=_UpperCAmelCase		,		eos_token=_UpperCAmelCase		,		sep_token=_UpperCAmelCase		,		cls_token=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		mask_token=_UpperCAmelCase		,		add_prefix_space=_UpperCAmelCase		,		trim_offsets=_UpperCAmelCase		,		**_UpperCAmelCase		,		)

     UpperCamelCase_						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
     if pre_tok_state.get('add_prefix_space'		,		_UpperCAmelCase			) != add_prefix_space:
        UpperCamelCase_						=  getattr(_UpperCAmelCase		,		pre_tok_state.pop('type'			)			)
        UpperCamelCase_						=  add_prefix_space
        UpperCamelCase_						=  pre_tok_class(**_UpperCAmelCase			)

     UpperCamelCase_						=  add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     UpperCamelCase_						=  'post_processor'
     UpperCamelCase_						=  getattr(self.backend_tokenizer		,		_UpperCAmelCase		,		_UpperCAmelCase			)
     if tokenizer_component_instance:
        UpperCamelCase_						=  json.loads(tokenizer_component_instance.__getstate__()			)

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
           UpperCamelCase_						=  tuple(state['sep']			)
        if "cls" in state:
           UpperCamelCase_						=  tuple(state['cls']			)

        UpperCamelCase_						=  False

        if state.get('add_prefix_space'		,		_UpperCAmelCase			) != add_prefix_space:
           UpperCamelCase_						=  add_prefix_space
           UpperCamelCase_						=  True

        if state.get('trim_offsets'		,		_UpperCAmelCase			) != trim_offsets:
           UpperCamelCase_						=  trim_offsets
           UpperCamelCase_						=  True

        if changes_to_apply:
           UpperCamelCase_						=  getattr(_UpperCAmelCase		,		state.pop('type'			)			)
           UpperCamelCase_						=  component_class(**_UpperCAmelCase			)
           setattr(self.backend_tokenizer		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  @property
  # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
  def  _UpperCAmelCase  (  self			)      ->			str:
     if self._mask_token is None:
        if self.verbose:
           logger.error('Using mask_token, but it is not set yet.'			)
        return None
     return str(self._mask_token			)




  @mask_token.setter
  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  AddedToken(_UpperCAmelCase		,		lstrip=_UpperCAmelCase		,		rstrip=_UpperCAmelCase			) if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			) else value
     UpperCamelCase_						=  value




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			BatchEncoding:
     UpperCamelCase_						=  kwargs.get('is_split_into_words'		,		_UpperCAmelCase			)

     if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            'to use it with pretokenized inputs.'			)

     return super()._batch_encode_plus(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			BatchEncoding:
     UpperCamelCase_						=  kwargs.get('is_split_into_words'		,		_UpperCAmelCase			)

     if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            'to use it with pretokenized inputs.'			)

     return super()._encode_plus(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			Tuple[str]:
     UpperCamelCase_						=  self._tokenizer.model.save(_UpperCAmelCase		,		name=_UpperCAmelCase			)
     return tuple(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			Optional[Any]:
     UpperCamelCase_						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			List[int]:
     UpperCamelCase_						=  [self.sep_token_id]
     UpperCamelCase_						=  [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 + sep + token_ids_a + sep			) * [0]




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PaddingStrategy.DO_NOT_PAD		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		)      ->			dict:
     UpperCamelCase_						=  super()._pad(
         encoded_inputs=_UpperCAmelCase		,		max_length=_UpperCAmelCase		,		padding_strategy=_UpperCAmelCase		,		pad_to_multiple_of=_UpperCAmelCase		,		return_attention_mask=_UpperCAmelCase		,		)

     # Load from model defaults
     if return_attention_mask is None:
        UpperCamelCase_						=  'attention_mask' in self.model_input_names

     if return_attention_mask and "global_attention_mask" in encoded_inputs:
        UpperCamelCase_						=  encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        UpperCamelCase_						=  len(encoded_inputs['global_attention_mask']			) != len(_UpperCAmelCase			)

        if needs_to_be_padded:
           UpperCamelCase_						=  len(_UpperCAmelCase			) - len(encoded_inputs['global_attention_mask']			)

           if self.padding_side == "right":
              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
              UpperCamelCase_						=  (
                  encoded_inputs['global_attention_mask'] + [-1] * difference
              )
           elif self.padding_side == "left":
              UpperCamelCase_						=  [-1] * difference + encoded_inputs[
                  'global_attention_mask'
              ]
           else:
              raise ValueError('Invalid padding strategy:' + str(self.padding_side			)			)

     return encoded_inputs









import math
class 		__magic_name__						:





     '''simple docstring'''



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







def SCREAMING_SNAKE_CASE	(			)    ->				None:
  # Training Examples ( m, n )
  snake_case__						= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

      # Compute the winning vector
      snake_case__						= self_organizing_map.get_winner(__lowerCAmelCase							,   __lowerCAmelCase				)

      # Update the winning vector
      snake_case__						= self_organizing_map.update(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

    # classify test sample
  snake_case__						= [0, 0, 0, 1]
  snake_case__						= 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()

