



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						Dict:







	"""simple docstring"""
	return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     {}

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

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

		lowerCAmelCase   =     value.float()

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

	return upgrade






@torch.no_grad()
def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Dict=None							)      ->						Dict:







	"""simple docstring"""




	if config_path is not None:
		lowerCAmelCase   =     FlavaConfig.from_pretrained(lowerCamelCase__							)
	else:
		lowerCAmelCase   =     FlavaConfig()

	lowerCAmelCase   =     FlavaForPreTraining(lowerCamelCase__							).eval()

	lowerCAmelCase   =     convert_dalle_checkpoint(lowerCamelCase__							,		lowerCamelCase__							,		save_checkpoint=lowerCamelCase__							)

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

	lowerCAmelCase   =     upgrade_state_dict(lowerCamelCase__							,		lowerCamelCase__							)
	hf_model.load_state_dict(lowerCamelCase__							)
	lowerCAmelCase   =     hf_model.state_dict()
	lowerCAmelCase   =     count_parameters(lowerCamelCase__							)
	lowerCAmelCase   =     count_parameters(lowerCamelCase__							) + count_parameters(lowerCamelCase__							)

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

	hf_model.save_pretrained(lowerCamelCase__							)


if __name__ == "__main__":
				UpperCAmelCase     		=   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')
				UpperCAmelCase     		=   parser.parse_args()

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




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase     		=   logging.get_logger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		"timm_backbone"


						def __init__( self     ,	A_=None     ,	A_=3     ,	A_=True     ,	A_=True     ,	A_=None     ,	**A_     ,	)     ->     int:
							super().__init__(**A_				)
							lowerCAmelCase   =     backbone
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     features_only
							lowerCAmelCase   =     use_pretrained_backbone
							lowerCAmelCase   =     True
							lowerCAmelCase   =     out_indices if out_indices is not None else (-1,)