





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					:				List[Any]				=			logging.get_logger(__name__)

_A					:				str				=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_A					:				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					:				Optional[Any]				=			{
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Any				=	PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		int				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Union[str, Any]				=	["""input_ids""", """attention_mask"""]
    lowerCamelCase__    :		Tuple				=	GPTaTokenizer



    def __init__(       self   ,				A_=None   ,				A_=None   ,				A_=None   ,				A_="<|endoftext|>"   ,				A_="<|endoftext|>"   ,				A_="<|endoftext|>"   ,				A_=False   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(
            A_   ,				A_   ,				tokenizer_file=A_   ,				unk_token=A_   ,				bos_token=A_   ,				eos_token=A_   ,				add_prefix_space=A_   ,				**A_   ,				)

        SCREAMING_SNAKE_CASE__											=			kwargs.pop('''add_bos_token'''   ,				A_     )

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

        SCREAMING_SNAKE_CASE__											=			add_prefix_space



    def  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_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  lowercase_					(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

        SCREAMING_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  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._tokenizer.model.save(A_   ,				name=A_     )
        return tuple(A_     )




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_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:
            SCREAMING_SNAKE_CASE__											=			input_ids[-self.model_max_length :]
        return input_ids







from torch import nn


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    if act_fn in ["swish", "silu"]:
        return nn.SiLU()
    elif act_fn == "mish":
        return nn.Mish()
    elif act_fn == "gelu":
        return nn.GELU()
    else:
        raise ValueError(f'''Unsupported activation function: {act_fn}'''	)
