


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		:				Any									=       {
    '''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
    '''processing_vision_text_dual_encoder''': ['''VisionTextDualEncoderProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Optional[int]									=       ['''VisionTextDualEncoderModel''']


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Optional[int]									=       ['''FlaxVisionTextDualEncoderModel''']

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A		:				Union[str, Any]									=       ['''TFVisionTextDualEncoderModel''']


if TYPE_CHECKING:
			from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
			from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
			import sys

			_A		:				Dict									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure)






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       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.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

   _A		:				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)


