





"""simple docstring"""
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 					A__				(			UpperCamelCase       ):
							# encoder.embeddings are double copied in original FLAVA
							return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  {}

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

														A		=  key.replace("heads.cmd.mim_head.cls.predictions"					,  "mmm_image_head"       )
														A		=  key.replace("heads.cmd.mlm_head.cls.predictions"					,  "mmm_text_head"       )
														A		=  key.replace("heads.cmd.itm_head.cls"					,  "itm_head"       )
														A		=  key.replace("heads.cmd.itm_head.pooler"					,  "itm_head.pooler"       )
														A		=  key.replace("heads.cmd.clip_head.logit_scale"					,  "flava.logit_scale"       )
														A		=  key.replace("heads.fairseq_mlm.cls.predictions"					,  "mlm_head"       )
														A		=  key.replace("heads.imagenet.mim_head.cls.predictions"					,  "mim_head"       )
														A		=  key.replace("mm_text_projection"					,  "flava.text_to_mm_projection"       )
														A		=  key.replace("mm_image_projection"					,  "flava.image_to_mm_projection"       )
														A		=  key.replace("image_encoder.module"					,  "flava.image_model"       )
														A		=  key.replace("text_encoder.module"					,  "flava.text_model"       )
														A		=  key.replace("mm_encoder.module.encoder.cls_token"					,  "flava.multimodal_model.cls_token"       )
														A		=  key.replace("mm_encoder.module"					,  "flava.multimodal_model"       )
														A		=  key.replace("text_projection"					,  "flava.text_projection"       )
														A		=  key.replace("image_projection"					,  "flava.image_projection"       )

														A		=  value.float()

							for key, value in codebook_state_dict.items():
														A		=  value

							return upgrade






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=None       ):
							if config_path is not None:
														A		=  FlavaConfig.from_pretrained(_snake_case       )
							else:
														A		=  FlavaConfig()

							A		=  FlavaForPreTraining(_snake_case       ).eval()

							A		=  convert_dalle_checkpoint(_snake_case					,  _snake_case					,  save_checkpoint=_snake_case       )

							if os.path.exists(_snake_case       ):
														A		=  torch.load(_snake_case					,  map_location="cpu"       )
							else:
														A		=  torch.hub.load_state_dict_from_url(_snake_case					,  map_location="cpu"       )

							A		=  upgrade_state_dict(_snake_case					,  _snake_case       )
							hf_model.load_state_dict(_snake_case       )
							A		=  hf_model.state_dict()
							A		=  count_parameters(_snake_case       )
							A		=  count_parameters(_snake_case       ) + count_parameters(_snake_case       )

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

							hf_model.save_pretrained(_snake_case       )


if __name__ == "__main__":
		_snake_case     :		List[Any]       						= 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')
		_snake_case     :		Optional[int]       						= parser.parse_args()

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




"""simple docstring"""




UpperCAmelCase__      :    Any    							= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase__      :    Any    							= [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase__      :    Optional[int]    							= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




