


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=_a						)
class 	__lowerCamelCase			(_a						):
       _lowercase											=     field(default="""audio-classification"""				,      metadata={"""include_in_asdict_even_if_is_default""": True}						)
       _lowercase											=     Features({"""audio""": Audio()}						)
       _lowercase											=     Features({"""labels""": ClassLabel}						)
       _lowercase											=     "audio"
       _lowercase											=     "labels"







       def 		snake_case_	(			self: str,A_: int					):






            '''simple docstring'''




            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''					)
            if not isinstance(features[self.label_column],A_					):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''					)
            __UpperCamelCase =			copy.deepcopy(self					)
            __UpperCamelCase =			self.label_schema.copy()
            __UpperCamelCase =			features[self.label_column]
            __UpperCamelCase =			label_schema
            return task_template







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						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.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

    __snake_case   							=						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)


