




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowercase__     						=						{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						["""BlipImageProcessor"""]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """BlipModel""",
			    """BlipPreTrainedModel""",
			    """BlipForConditionalGeneration""",
			    """BlipForQuestionAnswering""",
			    """BlipVisionModel""",
			    """BlipTextModel""",
			    """BlipForImageTextRetrieval""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFBlipModel""",
			    """TFBlipPreTrainedModel""",
			    """TFBlipForConditionalGeneration""",
			    """TFBlipForQuestionAnswering""",
			    """TFBlipVisionModel""",
			    """TFBlipTextModel""",
			    """TFBlipForImageTextRetrieval""",
			]

if TYPE_CHECKING:
			from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
			from .processing_blip import BlipProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_blip import BlipImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blip import (
						    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlipForConditionalGeneration,
						    BlipForImageTextRetrieval,
						    BlipForQuestionAnswering,
						    BlipModel,
						    BlipPreTrainedModel,
						    BlipTextModel,
						    BlipVisionModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blip import (
						    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFBlipForConditionalGeneration,
						    TFBlipForImageTextRetrieval,
						    TFBlipForQuestionAnswering,
						    TFBlipModel,
						    TFBlipPreTrainedModel,
						    TFBlipTextModel,
						    TFBlipVisionModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







"""simple docstring"""



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		:       str  				=		parser.parse_args()

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

						__A		:       Dict  				=		CLIPImageProcessor()
						__A		:       Union[str, Any]  				=		CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

						__A		:       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)



