






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							( __lowerCAmelCase      )			->     Dict:
     # 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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       Dict	    =  {}

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

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

          UpperCamelCase__      :       Optional[int]	    =  value.float()

     for key, value in codebook_state_dict.items():
          UpperCamelCase__      :       Optional[Any]	    =  value

     return upgrade







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=None      )			->     List[str]:
     if config_path is not None:
          UpperCamelCase__      :       Any	    =  FlavaConfig.from_pretrained(__lowerCAmelCase      )
     else:
          UpperCamelCase__      :       Optional[Any]	    =  FlavaConfig()

     UpperCamelCase__      :       Any	    =  FlavaForPreTraining(__lowerCAmelCase      ).eval()

     UpperCamelCase__      :       Tuple	    =  convert_dalle_checkpoint(__lowerCAmelCase ,   __lowerCAmelCase ,   save_checkpoint=__lowerCAmelCase      )

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

     UpperCamelCase__      :       Tuple	    =  upgrade_state_dict(__lowerCAmelCase ,   __lowerCAmelCase      )
     hf_model.load_state_dict(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[Any]	    =  hf_model.state_dict()
     UpperCamelCase__      :       Union[str, Any]	    =  count_parameters(__lowerCAmelCase      )
     UpperCamelCase__      :       Union[str, Any]	    =  count_parameters(__lowerCAmelCase      ) + count_parameters(__lowerCAmelCase      )

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

     hf_model.save_pretrained(__lowerCAmelCase      )


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

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







lowerCamelCase     :							Optional[int]    ={
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     list[str]:
     UpperCamelCase__      :       Optional[Any]	    =  set()
     # keep track of all the paths to be checked
     UpperCamelCase__      :       Optional[Any]	    =  [[start]]

     # return path if start is goal
     if start == goal:
          return [start]

     # keeps looping until all possible paths have been checked
     while queue:
          # pop the first path from the queue
          UpperCamelCase__      :       int	    =  queue.pop(0      )
          # get the last node from the path
          UpperCamelCase__      :       Dict	    =  path[-1]
          if node not in explored:
               UpperCamelCase__      :       Tuple	    =  graph[node]
               # go through all neighbour nodes, construct a new path and
               # push it into the queue
               for neighbour in neighbours:
                    UpperCamelCase__      :       List[str]	    =  list(__lowerCAmelCase      )
                    new_path.append(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    # return path if neighbour is goal
                    if neighbour == goal:
                         return new_path

            # mark node as explored
               explored.add(__lowerCAmelCase      )

    # in case there's no path between the 2 nodes
     return []







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     int:
     if not graph or start not in graph or target not in graph:
          return -1
     if start == target:
          return 0
     UpperCamelCase__      :       Tuple	    =  [start]
     UpperCamelCase__      :       Optional[int]	    =  set(__lowerCAmelCase      )
     # Keep tab on distances from `start` node.
     UpperCamelCase__      :       str	    =  {start: 0, target: -1}
     while queue:
          UpperCamelCase__      :       Any	    =  queue.pop(0      )
          if node == target:
               UpperCamelCase__      :       Union[str, Any]	    =  (
                   dist[node] if dist[target] == -1 else min(dist[target] ,   dist[node]      )
               )
          for adjacent in graph[node]:
               if adjacent not in visited:
                    visited.add(__lowerCAmelCase      )
                    queue.append(__lowerCAmelCase      )
                    UpperCamelCase__      :       List[Any]	    =  dist[node] + 1
     return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4