


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


_SCREAMING_SNAKE_CASE			: str			      =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			: List[Any]			      =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE			: List[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',
    },
}

_SCREAMING_SNAKE_CASE			: Optional[int]			      =    {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}

class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		VOCAB_FILES_NAMES
    __magic_name__			  =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__			  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__			  =		['input_ids', 'attention_mask']
    __magic_name__			  =		GPTaTokenizer
    def __init__(       self			,					__snake_case=None			,					__snake_case=None			,					__snake_case=None			,					__snake_case="<|endoftext|>"			,					__snake_case="<|endoftext|>"			,					__snake_case="<|endoftext|>"			,					__snake_case=False			,					**__snake_case			,					):
         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			,					)

         snake_case		 =				kwargs.pop('''add_bos_token'''			,					__snake_case    )

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

         snake_case		 =				add_prefix_space
    def 		a_			(       self			,					*__snake_case			,					**__snake_case    ):
         snake_case		 =				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 		a_			(       self			,					*__snake_case			,					**__snake_case    ):
         snake_case		 =				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 		a_			(       self			,					__snake_case			,					__snake_case = None    ):
         snake_case		 =				self._tokenizer.model.save(__snake_case			,					name=__snake_case    )
         return tuple(__snake_case    )




    def 		a_			(       self			,					__snake_case    ):
         snake_case		 =				[]
         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:
              snake_case		 =				input_ids[-self.model_max_length :]
         return input_ids












from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




