





"""simple docstring"""





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:
  lowercase__				:Tuple				   =       None


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

lowercase__				:str				   =       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__				:Dict				   =       {
    '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
lowercase__				:List[Any]				   =       {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Dict         =       VOCAB_FILES_NAMES
      _A     :       List[str]         =       PRETRAINED_VOCAB_FILES_MAP
      _A     :       List[str]         =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A     :       Tuple         =       ['input_ids', 'attention_mask']
      _A     :       int         =       TaTokenizer

      _A     :       List[int]         =       []

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      List[Any]=None						,				__lowercase       :      Optional[int]=None						,				__lowercase       :      Any="</s>"						,				__lowercase       :      Any="<unk>"						,				__lowercase       :      Optional[Any]="<pad>"						,				__lowercase       :      int=100						,				__lowercase       :      List[str]=None						,				**__lowercase       :      List[Any]						,				):


        '''simple docstring'''




        if extra_ids > 0 and additional_special_tokens is None:
          __UpperCAmelCase    :						int       =						[f'''<extra_id_{i}>''' for i in range(__lowercase )]
        elif extra_ids > 0 and additional_special_tokens is not None:
          # Check that we have the right number of extra special tokens
          __UpperCAmelCase    :						List[Any]       =						len(set(filter(lambda __lowercase : bool('''extra_id_''' in str(__lowercase ) )						,				__lowercase ) ) )
          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__(
            __lowercase						,				tokenizer_file=__lowercase						,				eos_token=__lowercase						,				unk_token=__lowercase						,				pad_token=__lowercase						,				extra_ids=__lowercase						,				additional_special_tokens=__lowercase						,				**__lowercase						,				)

        __UpperCAmelCase    :						Tuple       =						vocab_file
        __UpperCAmelCase    :						Dict       =						False if not self.vocab_file else True
        __UpperCAmelCase    :						Tuple       =						extra_ids

      @staticmethod
      def       A_					(							__lowercase       :      Optional[Any]						,				__lowercase       :      Optional[Any]						,				__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
          __UpperCAmelCase    :						Any       =						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.'''						,				__lowercase						,				)

        return max_model_length

      def       A_					(							self       :      List[Any]						,				__lowercase       :      str						,				__lowercase       :      Optional[str] = None ):


        '''simple docstring'''




        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(__lowercase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
          return
        __UpperCAmelCase    :						Any       =						os.path.join(
            __lowercase						,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

        return (out_vocab_file,)

      def       A_					(							self       :      Optional[int]						,				__lowercase       :      List[int]						,				__lowercase       :      Optional[List[int]] = None ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a
        else:
          __UpperCAmelCase    :						Optional[int]       =						token_ids_a + [self.eos_token_id]
          return self.prefix_tokens + token_ids_a + token_ids_a

      def       A_					(							self       :      Dict						,				__lowercase       :      List[int]						,				__lowercase       :      Optional[List[int]] = None ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						[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       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return list(
            set(filter(lambda __lowercase : bool(re.search(R'''<extra_id_\d+>'''						,				__lowercase ) ) is not None						,				self.additional_special_tokens ) ) )







      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        return [self.convert_tokens_to_ids(__lowercase ) for token in self.get_sentinel_tokens()]






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


  return sorted(UpperCAmelCase_  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			UpperCAmelCase_  ):
    for j in range(max(0  ,			i - 6  )  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						Dict       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(UpperCAmelCase_  ,			UpperCAmelCase_  )

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))