



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


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

lowerCAmelCase: Tuple							=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase: Tuple							=						{
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase: Dict							=						{
    'yjernite/retribert-base-uncased': 5_1_2,
}


lowerCAmelCase: List[Any]							=						{
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}

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







    def __init__(				self			:  Any						,      __snake_case			:  Optional[int]=None						,      __snake_case			:  Dict=None						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  Optional[Any]="[UNK]"						,      __snake_case			:  str="[SEP]"						,      __snake_case			:  Tuple="[PAD]"						,      __snake_case			:  int="[CLS]"						,      __snake_case			:  Optional[Any]="[MASK]"						,      __snake_case			:  Dict=True						,      __snake_case			:  Dict=None						,      **__snake_case			:  Union[str, Any]						,      ):
      super().__init__(
          __snake_case						,      tokenizer_file=__snake_case						,      do_lower_case=__snake_case						,      unk_token=__snake_case						,      sep_token=__snake_case						,      pad_token=__snake_case						,      cls_token=__snake_case						,      mask_token=__snake_case						,      tokenize_chinese_chars=__snake_case						,      strip_accents=__snake_case						,      **__snake_case						,      )

      a						: int					       =					json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
      if (
          normalizer_state.get('lowercase'						,      __snake_case     ) != do_lower_case
          or normalizer_state.get('strip_accents'						,      __snake_case     ) != strip_accents
          or normalizer_state.get('handle_chinese_chars'						,      __snake_case     ) != tokenize_chinese_chars
      ):
        a						: Dict					       =					getattr(__snake_case						,      normalizer_state.pop('type'     )     )
        a						: Tuple					       =					do_lower_case
        a						: Optional[Any]					       =					strip_accents
        a						: List[Any]					       =					tokenize_chinese_chars
        a						: Dict					       =					normalizer_class(**__snake_case     )

      a						: List[Any]					       =					do_lower_case







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  str						,      __snake_case			:  Optional[int]=None     ):
      a						: Union[str, Any]					       =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output







    def       lowercase_	(				self			:  int						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      a						: Optional[Any]					       =					[self.sep_token_id]
      a						: Any					       =					[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep     ) * [0]
      return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  str						,      __snake_case			:  Optional[str] = None     ):
      a						: Optional[Any]					       =					self._tokenizer.model.save(__snake_case						,      name=__snake_case     )
      return tuple(__snake_case     )




'''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)