



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 (       __snake_case    )     ->     List[str]:






      '''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 (       __snake_case					,  __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		{}

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

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

            UpperCAmelCase_  :	Optional[Any]				      =		value.float()

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

      return upgrade







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=None    )     ->     Any:






      '''simple docstring'''







      if config_path is not None:
            UpperCAmelCase_  :	Union[str, Any]				      =		FlavaConfig.from_pretrained(__snake_case    )
      else:
            UpperCAmelCase_  :	Any				      =		FlavaConfig()

      UpperCAmelCase_  :	Optional[int]				      =		FlavaForPreTraining(__snake_case    ).eval()

      UpperCAmelCase_  :	List[Any]				      =		convert_dalle_checkpoint(__snake_case					,  __snake_case					,  save_checkpoint=__snake_case    )

      if os.path.exists(__snake_case    ):
            UpperCAmelCase_  :	int				      =		torch.load(__snake_case					,  map_location="cpu"    )
      else:
            UpperCAmelCase_  :	Optional[Any]				      =		torch.hub.load_state_dict_from_url(__snake_case					,  map_location="cpu"    )

      UpperCAmelCase_  :	str				      =		upgrade_state_dict(__snake_case					,  __snake_case    )
      hf_model.load_state_dict(__snake_case    )
      UpperCAmelCase_  :	Optional[int]				      =		hf_model.state_dict()
      UpperCAmelCase_  :	str				      =		count_parameters(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		count_parameters(__snake_case    ) + count_parameters(__snake_case    )

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

      hf_model.save_pretrained(__snake_case    )


if __name__ == "__main__":
   __lowerCamelCase         =			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         =			parser.parse_args()

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






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	list[list[Edge]]				      =		[[] for _ in range(lowercase)]
             UpperCAmelCase_  :	str				      =		size



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             return iter(self._graph[vertex])



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._size



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1.")

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size).")

             self._graph[from_vertex].append(Edge(lowercase     ,lowercase))



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		deque([start_vertex])
             UpperCAmelCase_  :	list[int | None]				      =		[None] * self.size
             UpperCAmelCase_  :	Dict				      =		0

             while queue:
                   UpperCAmelCase_  :	List[Any]				      =		queue.popleft()
                   UpperCAmelCase_  :	Any				      =		distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         UpperCAmelCase_  :	Tuple				      =		current_distance + edge.weight
                         UpperCAmelCase_  :	Optional[Any]				      =		distances[edge.destination_vertex]
                         if (
                             isinstance(lowercase     ,lowercase)
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         UpperCAmelCase_  :	Tuple				      =		new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex)
                         else:
                               queue.append(edge.destination_vertex)

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex.")

             return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
