






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__	:			str			   = logging.get_logger(__name__)

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

A__	:			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',
    },
}

A__	:			Optional[int]			   = {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       VOCAB_FILES_NAMES
				lowercase__											=       PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=       ["""input_ids""", """attention_mask"""]
				lowercase__											=       GPTaTokenizer




				def __init__(     self						:    Dict,  lowerCamelCase						:    Union[str, Any]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    Any="<|endoftext|>",  lowerCamelCase						:    str="<|endoftext|>",  lowerCamelCase						:    Any="<|endoftext|>",  lowerCamelCase						:    str=False,  **lowerCamelCase						:    Tuple,  ):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,  lowerCamelCase,  tokenizer_file=lowerCamelCase,  unk_token=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  add_prefix_space=lowerCamelCase,  **lowerCamelCase,  )

								lowercase__    =			kwargs.pop('''add_bos_token''',  lowerCamelCase  )

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

								lowercase__    =			add_prefix_space




				def 				lowercase__   (     self						:    str,  *lowerCamelCase						:    List[str],  **lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			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 				lowercase__   (     self						:    Union[str, Any],  *lowerCamelCase						:    Tuple,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			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 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

								'''simple docstring'''
								lowercase__    =			self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase  )
								return tuple(lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    "Conversation"  ):

								'''simple docstring'''
								lowercase__    =			[]
								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:
												lowercase__    =			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 *




