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     UpperCamelCase						(	__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			Tuple     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				{}

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

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

				A_		:    Optional[int]         =				value.float()

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

		return upgrade




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			Dict						,__lowercase						:			int						,__lowercase						:			Dict=None     ):
		'''simple docstring'''




		if config_path is not None:
				A_		:    Optional[Any]         =				FlavaConfig.from_pretrained(__lowercase     )
		else:
				A_		:    Tuple         =				FlavaConfig()

		A_		:    Tuple         =				FlavaForPreTraining(__lowercase     ).eval()

		A_		:    Union[str, Any]         =				convert_dalle_checkpoint(__lowercase						,__lowercase						,save_checkpoint=__lowercase     )

		if os.path.exists(__lowercase     ):
				A_		:    str         =				torch.load(__lowercase						,map_location='cpu'     )
		else:
				A_		:    List[str]         =				torch.hub.load_state_dict_from_url(__lowercase						,map_location='cpu'     )

		A_		:    Optional[int]         =				upgrade_state_dict(__lowercase						,__lowercase     )
		hf_model.load_state_dict(__lowercase     )
		A_		:    Dict         =				hf_model.state_dict()
		A_		:    Dict         =				count_parameters(__lowercase     )
		A_		:    Tuple         =				count_parameters(__lowercase     ) + count_parameters(__lowercase     )

		assert torch.allclose(__lowercase						,__lowercase						,atol=1e-3     )

		hf_model.save_pretrained(__lowercase     )


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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase            =		{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		["""BridgeTowerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BridgeTowerForContrastiveLearning""",
	    """BridgeTowerForImageAndTextRetrieval""",
	    """BridgeTowerForMaskedLM""",
	    """BridgeTowerModel""",
	    """BridgeTowerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase            =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






