






"""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 				lowercase__		(					lowerCamelCase       ):
				# 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 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    {}

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

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

								_SCREAMING_SNAKE_CASE   :		str       		=    value.float()

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

				return upgrade





@torch.no_grad()
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None       ):
				if config_path is not None:
								_SCREAMING_SNAKE_CASE   :		int       		=    FlavaConfig.from_pretrained(lowerCamelCase       )
				else:
								_SCREAMING_SNAKE_CASE   :		Any       		=    FlavaConfig()

				_SCREAMING_SNAKE_CASE   :		str       		=    FlavaForPreTraining(lowerCamelCase       ).eval()

				_SCREAMING_SNAKE_CASE   :		str       		=    convert_dalle_checkpoint(lowerCamelCase,						lowerCamelCase,						save_checkpoint=lowerCamelCase       )

				if os.path.exists(lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    torch.load(lowerCamelCase,						map_location='cpu'       )
				else:
								_SCREAMING_SNAKE_CASE   :		str       		=    torch.hub.load_state_dict_from_url(lowerCamelCase,						map_location='cpu'       )

				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    upgrade_state_dict(lowerCamelCase,						lowerCamelCase       )
				hf_model.load_state_dict(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    hf_model.state_dict()
				_SCREAMING_SNAKE_CASE   :		int       		=    count_parameters(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    count_parameters(lowerCamelCase       ) + count_parameters(lowerCamelCase       )

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

				hf_model.save_pretrained(lowerCamelCase       )


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

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









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

