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       __lowerCAmelCase      (						A_   : Tuple					)    ->  Optional[int]:
  return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]					)    ->  Any:
  __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       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]						,	A_   : str						,	A_   : Tuple=None					)    ->  int:





  if config_path is not None:
    __UpperCAmelCase						    =       FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
  else:
    __UpperCAmelCase						    =       FlavaConfig()

  __UpperCAmelCase						    =       FlavaForPreTraining(SCREAMING_SNAKE_CASE__					).eval()

  __UpperCAmelCase						    =       convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	save_checkpoint=SCREAMING_SNAKE_CASE__					)

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

  __UpperCAmelCase						    =       upgrade_state_dict(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__					)
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
  __UpperCAmelCase						    =       hf_model.state_dict()
  __UpperCAmelCase						    =       count_parameters(SCREAMING_SNAKE_CASE__					)
  __UpperCAmelCase						    =       count_parameters(SCREAMING_SNAKE_CASE__					) + count_parameters(SCREAMING_SNAKE_CASE__					)

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

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE__					)


if __name__ == "__main__":
       a_							=      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_							=      parser.parse_args()

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




import inspect
import unittest


class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Dict:




    '''simple docstring'''




    try:
      import diffusers  # noqa: F401
    except ImportError:
      assert False






  def 	_UpperCAmelCase				(				self:			Dict   ) -> Dict:




    '''simple docstring'''




    import diffusers
    from diffusers.dependency_versions_table import deps

    __UpperCAmelCase						    =       inspect.getmembers(__lowerCAmelCase				,				inspect.isclass   )

    for cls_name, cls_module in all_classes:
      if "dummy_" in cls_module.__module__:
        for backend in cls_module._backends:
          if backend == "k_diffusion":
            __UpperCAmelCase						    =       "k-diffusion"
          elif backend == "invisible_watermark":
            __UpperCAmelCase						    =       "invisible-watermark"
          assert backend in deps, F'''{backend} is not in the deps table!'''



