


'''simple docstring'''



from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)









'''simple docstring'''



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      :      str            =					logging.get_logger(__name__)

_lowercase      :      Any            =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Tuple            =					{
    """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      :      Union[str, Any]            =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	VOCAB_FILES_NAMES
				__magic_name__    :				str  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				List[Any]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				List[Any]  		=	GPTaTokenizer




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

										"""simple docstring"""




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

										UpperCAmelCase					    =     kwargs.pop('''add_bos_token'''				,   lowerCAmelCase				)

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

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Union[str, Any]				,   **lowerCAmelCase      :			Dict				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     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 					a__(		self      :			List[str]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			Tuple				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     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 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										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:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





