"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)

__lowerCAmelCase					: int        ={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[int]													=			'deta'
      snake_case__    :     List[str]													=			{
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=900	, __lowerCAmelCase=2048	, __lowerCAmelCase=6	, __lowerCAmelCase=2048	, __lowerCAmelCase=8	, __lowerCAmelCase=6	, __lowerCAmelCase=1024	, __lowerCAmelCase=8	, __lowerCAmelCase=0.0	, __lowerCAmelCase=True	, __lowerCAmelCase="relu"	, __lowerCAmelCase=256	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1.0	, __lowerCAmelCase=True	, __lowerCAmelCase=False	, __lowerCAmelCase="sine"	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=4	, __lowerCAmelCase=True	, __lowerCAmelCase=300	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=1	, __lowerCAmelCase=5	, __lowerCAmelCase=2	, __lowerCAmelCase=1	, __lowerCAmelCase=1	, __lowerCAmelCase=5	, __lowerCAmelCase=2	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.2_5	, **__lowerCAmelCase	, ):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase														=			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase	, __lowerCAmelCase		):
                        lowercase														=			backbone_config.pop("""model_type"""		)
                        lowercase														=			CONFIG_MAPPING[backbone_model_type]
                        lowercase														=			config_class.from_dict(__lowerCAmelCase		)

            lowercase														=			backbone_config
            lowercase														=			num_queries
            lowercase														=			max_position_embeddings
            lowercase														=			d_model
            lowercase														=			encoder_ffn_dim
            lowercase														=			encoder_layers
            lowercase														=			encoder_attention_heads
            lowercase														=			decoder_ffn_dim
            lowercase														=			decoder_layers
            lowercase														=			decoder_attention_heads
            lowercase														=			dropout
            lowercase														=			attention_dropout
            lowercase														=			activation_dropout
            lowercase														=			activation_function
            lowercase														=			init_std
            lowercase														=			init_xavier_std
            lowercase														=			encoder_layerdrop
            lowercase														=			auxiliary_loss
            lowercase														=			position_embedding_type
            # deformable attributes
            lowercase														=			num_feature_levels
            lowercase														=			encoder_n_points
            lowercase														=			decoder_n_points
            lowercase														=			two_stage
            lowercase														=			two_stage_num_proposals
            lowercase														=			with_box_refine
            lowercase														=			assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase														=			class_cost
            lowercase														=			bbox_cost
            lowercase														=			giou_cost
            # Loss coefficients
            lowercase														=			mask_loss_coefficient
            lowercase														=			dice_loss_coefficient
            lowercase														=			bbox_loss_coefficient
            lowercase														=			giou_loss_coefficient
            lowercase														=			eos_coefficient
            lowercase														=			focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase	, **__lowerCAmelCase		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self.d_model







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			copy.deepcopy(self.__dict__		)
            lowercase														=			self.backbone_config.to_dict()
            lowercase														=			self.__class__.model_type
            return output




"""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     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					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     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

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

            lowercase														=			key.replace("""heads.cmd.mim_head.cls.predictions"""      ,	"""mmm_image_head"""	)
            lowercase														=			key.replace("""heads.cmd.mlm_head.cls.predictions"""      ,	"""mmm_text_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.cls"""      ,	"""itm_head"""	)
            lowercase														=			key.replace("""heads.cmd.itm_head.pooler"""      ,	"""itm_head.pooler"""	)
            lowercase														=			key.replace("""heads.cmd.clip_head.logit_scale"""      ,	"""flava.logit_scale"""	)
            lowercase														=			key.replace("""heads.fairseq_mlm.cls.predictions"""      ,	"""mlm_head"""	)
            lowercase														=			key.replace("""heads.imagenet.mim_head.cls.predictions"""      ,	"""mim_head"""	)
            lowercase														=			key.replace("""mm_text_projection"""      ,	"""flava.text_to_mm_projection"""	)
            lowercase														=			key.replace("""mm_image_projection"""      ,	"""flava.image_to_mm_projection"""	)
            lowercase														=			key.replace("""image_encoder.module"""      ,	"""flava.image_model"""	)
            lowercase														=			key.replace("""text_encoder.module"""      ,	"""flava.text_model"""	)
            lowercase														=			key.replace("""mm_encoder.module.encoder.cls_token"""      ,	"""flava.multimodal_model.cls_token"""	)
            lowercase														=			key.replace("""mm_encoder.module"""      ,	"""flava.multimodal_model"""	)
            lowercase														=			key.replace("""text_projection"""      ,	"""flava.text_projection"""	)
            lowercase														=			key.replace("""image_projection"""      ,	"""flava.image_projection"""	)

            lowercase														=			value.float()

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

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

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

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

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

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =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					: List[str]        =parser.parse_args()

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



