





'''simple docstring'''






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


A__    :       Tuple              =logging.get_logger(__name__)

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

A__    :       Optional[int]              ={
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

A__    :       Union[str, Any]              ={
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Tuple											=       VOCAB_FILES_NAMES
      _lowercase:      Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
      _lowercase:      int											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:      Dict											=       ['''input_ids''', '''attention_mask''']
      _lowercase:      Optional[int]											=       GPTaTokenizer




      def __init__(      self      :			str      ,      __snake_case      :			str=None      ,      __snake_case      :			Union[str, Any]=None      ,      __snake_case      :			Optional[Any]=None      ,      __snake_case      :			Optional[int]="<|endoftext|>"      ,      __snake_case      :			Optional[int]="<|endoftext|>"      ,      __snake_case      :			Optional[Any]="<|endoftext|>"      ,      __snake_case      :			Dict=False      ,      **__snake_case      :			List[str]      ,      )    ->				Dict:
            super().__init__(
                __snake_case      ,      __snake_case      ,      tokenizer_file=__snake_case      ,      unk_token=__snake_case      ,      bos_token=__snake_case      ,      eos_token=__snake_case      ,      add_prefix_space=__snake_case      ,      **__snake_case      ,      )

            _lowerCAmelCase												=							kwargs.pop("""add_bos_token"""      ,      __snake_case  )

            _lowerCAmelCase												=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
            if pre_tok_state.get("""add_prefix_space"""      ,      __snake_case  ) != add_prefix_space:
                  _lowerCAmelCase												=							getattr(__snake_case      ,      pre_tok_state.pop("""type"""  )  )
                  _lowerCAmelCase												=							add_prefix_space
                  _lowerCAmelCase												=							pre_tok_class(**__snake_case  )

            _lowerCAmelCase												=							add_prefix_space




      def     lowercase__   (      self      :			Optional[Any]      ,      *__snake_case      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				BatchEncoding:
            _lowerCAmelCase												=							kwargs.get("""is_split_into_words"""      ,      __snake_case  )
            assert self.add_prefix_space or not is_split_into_words, (
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                "to use it with pretokenized inputs."
            )

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




      def     lowercase__   (      self      :			str      ,      *__snake_case      :			int      ,      **__snake_case      :			Optional[Any]  )    ->				BatchEncoding:
            _lowerCAmelCase												=							kwargs.get("""is_split_into_words"""      ,      __snake_case  )

            assert self.add_prefix_space or not is_split_into_words, (
                f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
                "to use it with pretokenized inputs."
            )

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




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			str      ,      __snake_case      :			Optional[str] = None  )    ->				Tuple[str]:
            _lowerCAmelCase												=							self._tokenizer.model.save(__snake_case      ,      name=__snake_case  )
            return tuple(__snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			"Conversation"  )    ->				List[int]:
            _lowerCAmelCase												=							[]
            for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(__snake_case      ,      add_special_tokens=__snake_case  ) + [self.eos_token_id]  )

            if len(__snake_case  ) > self.model_max_length:
                  _lowerCAmelCase												=							input_ids[-self.model_max_length :]
            return input_ids













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if not (isinstance(lowerCAmelCase							,     lowerCAmelCase  ) and isinstance(lowerCAmelCase							,     lowerCAmelCase  )):
            raise ValueError("""longest_common_substring() takes two strings for inputs"""  )

      _lowerCAmelCase												=							len(lowerCAmelCase  )
      _lowerCAmelCase												=							len(lowerCAmelCase  )

      _lowerCAmelCase												=							[[0] * (texta_length + 1) for _ in range(texta_length + 1  )]
      _lowerCAmelCase												=							0
      _lowerCAmelCase												=							0

      for i in range(1							,     texta_length + 1  ):
            for j in range(1							,     texta_length + 1  ):
                  if texta[i - 1] == texta[j - 1]:
                        _lowerCAmelCase												=							1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              _lowerCAmelCase												=							i
                              _lowerCAmelCase												=							dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






