



"""simple docstring"""


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase__  		=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase__  		=						{
    """facebook/blenderbot_small-90M""": 5_1_2,
}


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  List[str]     							=							VOCAB_FILES_NAMES
    _snake_case     :  List[Any]     							=							PRETRAINED_VOCAB_FILES_MAP
    _snake_case     :  Optional[Any]     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case     :  Optional[Any]     							=							BlenderbotSmallTokenizer


    def __init__(					self	:  str   ,				__lowerCAmelCase	:  List[Any]=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				__lowerCAmelCase	:  List[Any]="<|endoftext|>"   ,				__lowerCAmelCase	:  Tuple="<|endoftext|>"   ,				__lowerCAmelCase	:  Optional[Any]="<|endoftext|>"   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  Union[str, Any]=True   ,				**__lowerCAmelCase	:  Optional[int]   ,				):
      super().__init__(
          ByteLevelBPETokenizer(
              vocab=__lowerCAmelCase   ,				merges=__lowerCAmelCase   ,				add_prefix_space=__lowerCAmelCase   ,				trim_offsets=__lowerCAmelCase   ,				)   ,				bos_token=__lowerCAmelCase   ,				eos_token=__lowerCAmelCase   ,				unk_token=__lowerCAmelCase   ,				**__lowerCAmelCase   ,				)
      _UpperCAmelCase						 =      add_prefix_space


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]=None					):
      _UpperCAmelCase						 =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
        return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  List[int]   ,				__lowerCAmelCase	:  Optional[List[int]] = None					):
      _UpperCAmelCase						 =      [self.sep_token_id]
      _UpperCAmelCase						 =      [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 + sep + token_ids_a + sep					) * [0]

"""simple docstring"""


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    UpperCAmelCase__  		=						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.""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    UpperCAmelCase__  		=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    UpperCAmelCase__  		=						CLIPImageProcessor()
    UpperCAmelCase__  		=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

    UpperCAmelCase__  		=						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)
