




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 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[Any]   ):






			'''simple docstring'''







			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 			__magic_name__				( A		:					Union[str, Any],  A		:					List[Any],  A		:					List[Any],  A		:					Any=None   ):






			'''simple docstring'''







			if config_path is not None:
						a    =			FlavaConfig.from_pretrained(A   )
			else:
						a    =			FlavaConfig()

			a    =			FlavaForPreTraining(A   ).eval()

			a    =			convert_dalle_checkpoint(A,  A,  save_checkpoint=A   )

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

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

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

			hf_model.save_pretrained(A   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Optional[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')
		__lowerCAmelCase		:				Union[str, Any]       			=		parser.parse_args()

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












from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={"""help""": """The output directory where the model will be written."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don't set if you want to train an encoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don't set if you want to train a decoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments,)   )
			((a)    ,		)    =			parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.encoder_config_name   )
			# Use pretrained encoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.decoder_config_name   )
			# Use pretrained decoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a    =			True
			a    =			True

			a    =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,  encoder_config=A,  decoder_config=A,  )

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a    =			decoder_config.eos_token_id
			a    =			decoder_start_token_id
			a    =			pad_token_id

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

			a    =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
			a    =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

			model.save_pretrained(model_args.output_dir   )
			image_processor.save_pretrained(model_args.output_dir   )
			tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
		main()






