

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      		=     logging.get_logger(__name__)

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

__A      		=     {
    '''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      		=     {
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		int										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    ["input_ids", "attention_mask"]
      UpperCamelCase_      :		Optional[int]										=    GPTaTokenizer



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

        _snake_case						=							kwargs.pop('''add_bos_token'''      , A__		)

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

        _snake_case						=							add_prefix_space



      def 						UpperCamelCase_		(      self				:    Any      , *A__				:    Optional[Any]      , **A__				:    Any		)   ->					BatchEncoding:
        _snake_case						=							kwargs.get('''is_split_into_words'''      , A__		)
        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(*A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    str      , *A__				:    Union[str, Any]      , **A__				:    Optional[int]		)   ->					BatchEncoding:
        _snake_case						=							kwargs.get('''is_split_into_words'''      , A__		)

        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(*A__      , **A__		)



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







      def 						UpperCamelCase_		(      self				:    List[str]      , A__				:    "Conversation"		)   ->					List[int]:
        _snake_case						=							[]
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(A__      , add_special_tokens=A__		) + [self.eos_token_id]		)

        if len(A__		) > self.model_max_length:
          _snake_case						=							input_ids[-self.model_max_length :]
        return input_ids




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

