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


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

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

lowercase_  : str								=					{
    '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',
    },
}

lowercase_  : int								=					{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   VOCAB_FILES_NAMES
    __a						=   PRETRAINED_VOCAB_FILES_MAP
    __a						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __a						=   ["input_ids", "attention_mask"]
    __a						=   GPTaTokenizer
    def __init__(	self			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase="<|endoftext|>"			,			lowerCAmelCase="<|endoftext|>"			,			lowerCAmelCase="<|endoftext|>"			,			lowerCAmelCase=False			,			**lowerCAmelCase			,			)							->       Union[str, Any]:
     super().__init__(
         lowerCAmelCase			,			lowerCAmelCase			,			tokenizer_file=lowerCAmelCase			,			unk_token=lowerCAmelCase			,			bos_token=lowerCAmelCase			,			eos_token=lowerCAmelCase			,			add_prefix_space=lowerCAmelCase			,			**lowerCAmelCase			,			)

     SCREAMING_SNAKE_CASE__: Dict=   kwargs.pop('''add_bos_token'''			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
     if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase ) != add_prefix_space:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   getattr(lowerCAmelCase			,			pre_tok_state.pop('''type''' ) )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   add_prefix_space
      SCREAMING_SNAKE_CASE__: Any=   pre_tok_class(**lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   add_prefix_space
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       BatchEncoding:
     SCREAMING_SNAKE_CASE__: int=   kwargs.get('''is_split_into_words'''			,			lowerCAmelCase )
     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(*lowerCAmelCase			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       BatchEncoding:
     SCREAMING_SNAKE_CASE__: Optional[int]=   kwargs.get('''is_split_into_words'''			,			lowerCAmelCase )

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



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

     if len(lowerCAmelCase ) > self.model_max_length:
      SCREAMING_SNAKE_CASE__: Dict=   input_ids[-self.model_max_length :]
     return input_ids


"""simple docstring"""



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			[1]
							for i in range(2			,  lowerCAmelCase							):
														factorials.append(factorials[-1] * i							)
							assert 0 <= k < factorials[-1] * n, "k out of bounds"

							A__       :     Union[str, Any]								=			[]
							A__       :     int								=			list(range(lowerCAmelCase							)							)

							# Find permutation
							while factorials:
														A__       :     Optional[int]								=			factorials.pop()
														A__				,							A__       :     Union[str, Any]								=			divmod(lowerCAmelCase			,  lowerCAmelCase							)
														permutation.append(elements[number]							)
														elements.remove(elements[number]							)
							permutation.append(elements[0]							)

							return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()





