





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

							__lowerCAmelCase :Tuple     =    parser.parse_args()

							__lowerCAmelCase :Any     =    UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							__lowerCAmelCase :int     =    CLIPImageProcessor()
							__lowerCAmelCase :Optional[int]     =    CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

							__lowerCAmelCase :int     =    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)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase :str     =    {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Optional[int]     =    [
							    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'NllbMoeForConditionalGeneration',
							    'NllbMoeModel',
							    'NllbMoePreTrainedModel',
							    'NllbMoeTop2Router',
							    'NllbMoeSparseMLP',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							__lowerCAmelCase :int     =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)