





import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 							SCREAMING_SNAKE_CASE__  (			__a      ):
	return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ):
	snake_case_						:	str      =		{}

	for key, value in state_dict.items():
		if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
			continue

		snake_case_						:	int      =		key.replace('heads.cmd.mim_head.cls.predictions'      ,       'mmm_image_head'      )
		snake_case_						:	Optional[int]      =		key.replace('heads.cmd.mlm_head.cls.predictions'      ,       'mmm_text_head'      )
		snake_case_						:	List[Any]      =		key.replace('heads.cmd.itm_head.cls'      ,       'itm_head'      )
		snake_case_						:	List[Any]      =		key.replace('heads.cmd.itm_head.pooler'      ,       'itm_head.pooler'      )
		snake_case_						:	List[Any]      =		key.replace('heads.cmd.clip_head.logit_scale'      ,       'flava.logit_scale'      )
		snake_case_						:	Optional[Any]      =		key.replace('heads.fairseq_mlm.cls.predictions'      ,       'mlm_head'      )
		snake_case_						:	str      =		key.replace('heads.imagenet.mim_head.cls.predictions'      ,       'mim_head'      )
		snake_case_						:	Tuple      =		key.replace('mm_text_projection'      ,       'flava.text_to_mm_projection'      )
		snake_case_						:	Union[str, Any]      =		key.replace('mm_image_projection'      ,       'flava.image_to_mm_projection'      )
		snake_case_						:	Tuple      =		key.replace('image_encoder.module'      ,       'flava.image_model'      )
		snake_case_						:	Any      =		key.replace('text_encoder.module'      ,       'flava.text_model'      )
		snake_case_						:	str      =		key.replace('mm_encoder.module.encoder.cls_token'      ,       'flava.multimodal_model.cls_token'      )
		snake_case_						:	Dict      =		key.replace('mm_encoder.module'      ,       'flava.multimodal_model'      )
		snake_case_						:	Tuple      =		key.replace('text_projection'      ,       'flava.text_projection'      )
		snake_case_						:	Union[str, Any]      =		key.replace('image_projection'      ,       'flava.image_projection'      )

		snake_case_						:	Union[str, Any]      =		value.float()

	for key, value in codebook_state_dict.items():
		snake_case_						:	Union[str, Any]      =		value

	return upgrade


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a=None      ):







	if config_path is not None:
		snake_case_						:	List[str]      =		FlavaConfig.from_pretrained(__a      )
	else:
		snake_case_						:	Union[str, Any]      =		FlavaConfig()

	snake_case_						:	Any      =		FlavaForPreTraining(__a      ).eval()

	snake_case_						:	Tuple      =		convert_dalle_checkpoint(__a      ,       __a      ,       save_checkpoint=__a      )

	if os.path.exists(__a      ):
		snake_case_						:	Any      =		torch.load(__a      ,       map_location='cpu'      )
	else:
		snake_case_						:	List[Any]      =		torch.hub.load_state_dict_from_url(__a      ,       map_location='cpu'      )

	snake_case_						:	List[str]      =		upgrade_state_dict(__a      ,       __a      )
	hf_model.load_state_dict(__a      )
	snake_case_						:	Any      =		hf_model.state_dict()
	snake_case_						:	str      =		count_parameters(__a      )
	snake_case_						:	Optional[int]      =		count_parameters(__a      ) + count_parameters(__a      )

	assert torch.allclose(__a      ,       __a      ,       atol=1E-3      )

	hf_model.save_pretrained(__a      )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
						parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						_SCREAMING_SNAKE_CASE							=						parser.parse_args()

						convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






