



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


lowerCAmelCase: Dict							=						logging.get_logger(__name__)

lowerCAmelCase: str							=						'▁'

lowerCAmelCase: List[Any]							=						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase: int							=						{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase: Union[str, Any]							=						{
    'facebook/xglm-564M': 2_0_4_8,
}

class 							a__( __UpperCamelCase     ):
    lowercase__			=   VOCAB_FILES_NAMES
    lowercase__			=   PRETRAINED_VOCAB_FILES_MAP
    lowercase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__			=   ["input_ids", "attention_mask"]







    def __init__(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  Optional[int]="<s>"						,      __snake_case			:  int="</s>"						,      __snake_case			:  Any="</s>"						,      __snake_case			:  str="<s>"						,      __snake_case			:  List[Any]="<unk>"						,      __snake_case			:  Union[str, Any]="<pad>"						,      __snake_case			:  Optional[Dict[str, Any]] = None						,      **__snake_case			:  Dict						,      ):
      a						: Union[str, Any]					       =					{} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      a						: Dict					       =					7
      a						: List[Any]					       =					[F"""<madeupword{i}>""" for i in range(self.num_madeup_words     )]

      a						: int					       =					kwargs.get('additional_special_tokens'						,      []     )
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          bos_token=_lowerCAmelCase						,      eos_token=_lowerCAmelCase						,      unk_token=_lowerCAmelCase						,      sep_token=_lowerCAmelCase						,      cls_token=_lowerCAmelCase						,      pad_token=_lowerCAmelCase						,      sp_model_kwargs=self.sp_model_kwargs						,      **_lowerCAmelCase						,      )

      a						: List[Any]					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(str(_lowerCAmelCase     )     )
      a						: List[Any]					       =					vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      a						: Optional[Any]					       =					1

      # Mimic fairseq token-to-id alignment for the first 4 token
      a						: Optional[int]					       =					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

      a						: Optional[int]					       =					len(self.sp_model     )
      a						: Optional[Any]					       =					{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words     )}
      self.fairseq_tokens_to_ids.update(_lowerCAmelCase     )

      a						: Tuple					       =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







    def __getstate__(				self			:  str     ):
      a						: int					       =					self.__dict__.copy()
      a						: List[Any]					       =					None
      a						: int					       =					self.sp_model.serialized_model_proto()
      return state







    def __setstate__(				self			:  Optional[Any]						,      __snake_case			:  Tuple     ):
      a						: Tuple					       =					d

      # for backward compatibility
      if not hasattr(self						,      'sp_model_kwargs'     ):
        a						: Optional[int]					       =					{}

      a						: Dict					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):

      if token_ids_a is None:
        return [self.sep_token_id] + token_ids_a
      a						: Optional[Any]					       =					[self.sep_token_id]
      return sep + token_ids_a + sep + sep + token_ids_a







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None						,      __snake_case			:  bool = False     ):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCAmelCase						,      token_ids_a=_lowerCAmelCase						,      already_has_special_tokens=_lowerCAmelCase     )

      if token_ids_a is None:
        return [1] + ([0] * len(_lowerCAmelCase     ))
      return [1] + ([0] * len(_lowerCAmelCase     )) + [1, 1] + ([0] * len(_lowerCAmelCase     ))







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      a						: str					       =					[self.sep_token_id]

      if token_ids_a is None:
        return len(sep + token_ids_a     ) * [0]
      return len(sep + token_ids_a + sep + sep + token_ids_a     ) * [0]







    @property
    def       lowercase_	(				self			:  Union[str, Any]     ):
      return len(self.sp_model     ) + self.fairseq_offset + self.num_madeup_words







    def       lowercase_	(				self			:  str     ):
      a						: Tuple					       =					{self.convert_ids_to_tokens(_lowerCAmelCase     ): i for i in range(self.vocab_size     )}
      vocab.update(self.added_tokens_encoder     )
      return vocab







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  str     ):
      return self.sp_model.encode(_lowerCAmelCase						,      out_type=_lowerCAmelCase     )







    def       lowercase_	(				self			:  Any						,      __snake_case			:  Tuple     ):
      if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
      a						: Dict					       =					self.sp_model.PieceToId(_lowerCAmelCase     )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Optional[int]     ):
      if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple     ):
      a						: str					       =					"""""".join(_lowerCAmelCase     ).replace(_lowerCAmelCase						,      ' '     ).strip()
      return out_string







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  str						,      __snake_case			:  Optional[str] = None     ):
      if not os.path.isdir(_lowerCAmelCase     ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
      a						: List[Any]					       =					os.path.join(
          _lowerCAmelCase						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

      if os.path.abspath(self.vocab_file     ) != os.path.abspath(_lowerCAmelCase     ) and os.path.isfile(self.vocab_file     ):
        copyfile(self.vocab_file						,      _lowerCAmelCase     )
      elif not os.path.isfile(self.vocab_file     ):
        with open(_lowerCAmelCase						,      'wb'     ) as fi:
          a						: Dict					       =					self.sp_model.serialized_model_proto()
          fi.write(_lowerCAmelCase     )

      return (out_vocab_file,)




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						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.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						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)