"""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__     ):





		"""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__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   {}

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

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

				lowerCAmelCase__        =   value.float()

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

		return upgrade







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




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

		lowerCAmelCase__        =   FlavaForPreTraining(lowerCamelCase__     ).eval()

		lowerCAmelCase__        =   convert_dalle_checkpoint(lowerCamelCase__			,  lowerCamelCase__			,  save_checkpoint=lowerCamelCase__     )

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

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

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

		hf_model.save_pretrained(lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Optional[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")
			__lowerCAmelCase    :							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 os







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   os.path.dirname(os.path.realpath(lowerCamelCase__     )     )
		lowerCAmelCase__        =   os.path.join(lowerCamelCase__			,  """triangle.txt"""     )

		with open(lowerCamelCase__     ) as f:
				lowerCAmelCase__        =   f.readlines()

		lowerCAmelCase__        =   []
		for line in triangle:
				lowerCAmelCase__        =   []
				for number in line.strip().split(""" """     ):
						numbers_from_line.append(int(lowerCamelCase__     )     )
				a.append(lowerCamelCase__     )

		for i in range(1			,  len(lowerCamelCase__     )     ):
				for j in range(len(a[i]     )     ):
						lowerCAmelCase__        =   a[i - 1][j] if j != len(a[i - 1]     ) else 0
						lowerCAmelCase__        =   a[i - 1][j - 1] if j > 0 else 0
						a[i][j] += max(lowerCamelCase__			,  lowerCamelCase__     )
		return max(a[-1]     )


if __name__ == "__main__":
			print(solution())




