





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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				Tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				{}

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

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

						UpperCAmelCase   		=				value.float()

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

			return upgrade





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None )  ->				int:







			'''simple docstring'''

			if config_path is not None:
						UpperCAmelCase   		=				FlavaConfig.from_pretrained(UpperCAmelCase_ )
			else:
						UpperCAmelCase   		=				FlavaConfig()

			UpperCAmelCase   		=				FlavaForPreTraining(UpperCAmelCase_ ).eval()

			UpperCAmelCase   		=				convert_dalle_checkpoint(UpperCAmelCase_			,			UpperCAmelCase_			,			save_checkpoint=UpperCAmelCase_ )

			if os.path.exists(UpperCAmelCase_ ):
						UpperCAmelCase   		=				torch.load(UpperCAmelCase_			,			map_location='''cpu''' )
			else:
						UpperCAmelCase   		=				torch.hub.load_state_dict_from_url(UpperCAmelCase_			,			map_location='''cpu''' )

			UpperCAmelCase   		=				upgrade_state_dict(UpperCAmelCase_			,			UpperCAmelCase_ )
			hf_model.load_state_dict(UpperCAmelCase_ )
			UpperCAmelCase   		=				hf_model.state_dict()
			UpperCAmelCase   		=				count_parameters(UpperCAmelCase_ )
			UpperCAmelCase   		=				count_parameters(UpperCAmelCase_ ) + count_parameters(UpperCAmelCase_ )

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

			hf_model.save_pretrained(UpperCAmelCase_ )


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

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







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


if __name__ == "__main__":
     print(f'''{solution() = }''')



