"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__   :      Dict							=logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      Dict							={'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__   :      Any							={
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

SCREAMING_SNAKE_CASE__   :      Dict							={'bert_for_seq_generation': 512}



class      _UpperCAmelCase						( UpperCamelCase__						):






    """simple docstring"""
    __snake_case    =       VOCAB_FILES_NAMES
    __snake_case    =       PRETRAINED_VOCAB_FILES_MAP
    __snake_case    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __snake_case    =       []
    __snake_case    =       ['input_ids', 'attention_mask']







    def __init__(			self    , _lowercase    , _lowercase="<s>"    , _lowercase="</s>"    , _lowercase="<unk>"    , _lowercase="<pad>"    , _lowercase="<::::>"    , _lowercase = None    , **_lowercase    , )	->	List[Any]:
        _lowerCamelCase	: str       =       {} if sp_model_kwargs is None else sp_model_kwargs

        # Add extra_ids to the special token list
        super().__init__(
            bos_token=_lowercase    , eos_token=_lowercase    , unk_token=_lowercase    , pad_token=_lowercase    , sep_token=_lowercase    , sp_model_kwargs=self.sp_model_kwargs    , **_lowercase    , )

        _lowerCamelCase	: Dict       =       vocab_file

        _lowerCamelCase	: List[Any]       =       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(_lowercase					)







    @property
    def 				a__					(			self					)	->	str:
        return self.sp_model.get_piece_size()







    def 				a__					(			self					)	->	str:
        _lowerCamelCase	: Optional[int]       =       {self.convert_ids_to_tokens(_lowercase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab







    def __getstate__(			self					)	->	Tuple:
        _lowerCamelCase	: str       =       self.__dict__.copy()
        _lowerCamelCase	: str       =       None
        return state







    def __setstate__(			self    , _lowercase					)	->	Any:
        _lowerCamelCase	: Any       =       d

        # for backward compatibility
        if not hasattr(self    , '''sp_model_kwargs'''					):
            _lowerCamelCase	: str       =       {}

        _lowerCamelCase	: Optional[Any]       =       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(self.vocab_file					)







    def 				a__					(			self    , _lowercase					)	->	List[Any]:
        return self.sp_model.encode(_lowercase    , out_type=_lowercase					)







    def 				a__					(			self    , _lowercase					)	->	Any:
        return self.sp_model.piece_to_id(_lowercase					)







    def 				a__					(			self    , _lowercase					)	->	int:
        _lowerCamelCase	: Optional[int]       =       self.sp_model.IdToPiece(_lowercase					)
        return token







    def 				a__					(			self    , _lowercase					)	->	Any:
        _lowerCamelCase	: Tuple       =       []
        _lowerCamelCase	: str       =       ''''''
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(_lowercase					) + token
                _lowerCamelCase	: Optional[Any]       =       []
            else:
                current_sub_tokens.append(_lowercase					)
        out_string += self.sp_model.decode(_lowercase					)
        return out_string.strip()







    def 				a__					(			self    , _lowercase    , _lowercase = None					)	->	Optional[Any]:
        if not os.path.isdir(_lowercase					):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
            return
        _lowerCamelCase	: Dict       =       os.path.join(
            _lowercase    , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

        if os.path.abspath(self.vocab_file					) != os.path.abspath(_lowercase					) and os.path.isfile(self.vocab_file					):
            copyfile(self.vocab_file    , _lowercase					)
        elif not os.path.isfile(self.vocab_file					):
            with open(_lowercase    , '''wb'''					) as fi:
                _lowerCamelCase	: List[str]       =       self.sp_model.serialized_model_proto()
                fi.write(_lowercase					)

        return (out_vocab_file,)




'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
