

'''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 						__lowerCamelCase	(       __snake_case					:		Optional[int]	):






						"""simple docstring"""





						return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		int	):






						"""simple docstring"""





						A__					:       Tuple     ={}

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

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

												A__					:       int     =value.float()

						for key, value in codebook_state_dict.items():
												A__					:       Optional[Any]     =value

						return upgrade
@torch.no_grad()
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		Tuple,		__snake_case					:		Dict,		__snake_case					:		Optional[Any]=None	):






						"""simple docstring"""





						if config_path is not None:
												A__					:       Optional[int]     =FlavaConfig.from_pretrained(__snake_case	)
						else:
												A__					:       str     =FlavaConfig()

						A__					:       List[Any]     =FlavaForPreTraining(__snake_case	).eval()

						A__					:       Dict     =convert_dalle_checkpoint(__snake_case,		__snake_case,		save_checkpoint=__snake_case	)

						if os.path.exists(__snake_case	):
												A__					:       Dict     =torch.load(__snake_case,		map_location="""cpu"""	)
						else:
												A__					:       str     =torch.hub.load_state_dict_from_url(__snake_case,		map_location="""cpu"""	)

						A__					:       List[Any]     =upgrade_state_dict(__snake_case,		__snake_case	)
						hf_model.load_state_dict(__snake_case	)
						A__					:       Optional[int]     =hf_model.state_dict()
						A__					:       List[Any]     =count_parameters(__snake_case	)
						A__					:       Any     =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			:     List[Any]										=      parser.parse_args()

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




'''simple docstring'''



from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 						__lowerCamelCase	(       )	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =ArgumentParser("""Transformers CLI tool""",		usage="""transformers-cli <command> [<args>]"""	)
						A__					:       List[Any]     =parser.add_subparsers(help="""transformers-cli command helpers"""	)

						# Register commands
						ConvertCommand.register_subcommand(__snake_case	)
						DownloadCommand.register_subcommand(__snake_case	)
						EnvironmentCommand.register_subcommand(__snake_case	)
						RunCommand.register_subcommand(__snake_case	)
						ServeCommand.register_subcommand(__snake_case	)
						UserCommands.register_subcommand(__snake_case	)
						AddNewModelCommand.register_subcommand(__snake_case	)
						AddNewModelLikeCommand.register_subcommand(__snake_case	)
						LfsCommands.register_subcommand(__snake_case	)
						PTtoTFCommand.register_subcommand(__snake_case	)

						# Let's go
						A__					:       List[str]     =parser.parse_args()

						if not hasattr(__snake_case,		"""func"""	):
												parser.print_help()
												exit(1	)

						# Run
						A__					:       Optional[Any]     =args.func(__snake_case	)
						service.run()


if __name__ == "__main__":
		main()

