

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__			(       _a):
	# 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 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							{}

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

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

		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							value.float()

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

	return upgrade

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a=None):
	if config_path is not None:
		SCREAMING_SNAKE_CASE :   Any					  =							FlavaConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE :   Tuple					  =							FlavaConfig()

	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FlavaForPreTraining(_a).eval()

	SCREAMING_SNAKE_CASE :   int					  =							convert_dalle_checkpoint(_a				,      _a				,      save_checkpoint=_a)

	if os.path.exists(_a):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.load(_a				,      map_location="cpu")
	else:
		SCREAMING_SNAKE_CASE :   str					  =							torch.hub.load_state_dict_from_url(_a				,      map_location="cpu")

	SCREAMING_SNAKE_CASE :   Tuple					  =							upgrade_state_dict(_a				,      _a)
	hf_model.load_state_dict(_a)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							hf_model.state_dict()
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							count_parameters(_a)
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							count_parameters(_a) + count_parameters(_a)

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

	hf_model.save_pretrained(_a)


if __name__ == "__main__":
			a_      =     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')
			a_      =     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


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)