
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  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''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  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__=None						):







				'''simple docstring'''


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

				lowercase         =      FlavaForPreTraining(__magic_name__						).eval()

				lowercase         =      convert_dalle_checkpoint(__magic_name__		,    __magic_name__		,    save_checkpoint=__magic_name__						)

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

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

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

				hf_model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
						_snake_case      :							List[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")
						_snake_case      :							Union[str, Any]     	=    parser.parse_args()

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


def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      set()
				# To detect a back edge, keep track of vertices currently in the recursion stack
				lowercase         =      set()
				return any(
				    node not in visited and depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						)
				    for node in graph						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				visited.add(__magic_name__						)
				rec_stk.add(__magic_name__						)

				for node in graph[vertex]:
								if node not in visited:
												if depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):
																return True
								elif node in rec_stk:
												return True

    # The node needs to be removed from recursion stack before function ends
				rec_stk.remove(__magic_name__						)
				return False


if __name__ == "__main__":
						from doctest import testmod

						testmod()
