


'''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_     (       A__   :  Union[str, Any]   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  {}

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

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

    lowerCAmelCase_     : Tuple     =  value.float()

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

  return upgrade







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  List[str]				,       A__   :  Dict				,       A__   :  str=None   ):
  '''simple docstring'''





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

  lowerCAmelCase_     : Dict     =  FlavaForPreTraining(__lowerCAmelCase   ).eval()

  lowerCAmelCase_     : int     =  convert_dalle_checkpoint(__lowerCAmelCase				,       __lowerCAmelCase				,       save_checkpoint=__lowerCAmelCase   )

  if os.path.exists(__lowerCAmelCase   ):
    lowerCAmelCase_     : Tuple     =  torch.load(__lowerCAmelCase				,       map_location="""cpu"""   )
  else:
    lowerCAmelCase_     : str     =  torch.hub.load_state_dict_from_url(__lowerCAmelCase				,       map_location="""cpu"""   )

  lowerCAmelCase_     : List[str]     =  upgrade_state_dict(__lowerCAmelCase				,       __lowerCAmelCase   )
  hf_model.load_state_dict(__lowerCAmelCase   )
  lowerCAmelCase_     : Dict     =  hf_model.state_dict()
  lowerCAmelCase_     : Any     =  count_parameters(__lowerCAmelCase   )
  lowerCAmelCase_     : Tuple     =  count_parameters(__lowerCAmelCase   ) + count_parameters(__lowerCAmelCase   )

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

  hf_model.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
  __A   :    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")
  __A   :    Any											= parser.parse_args()

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





import os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

    for issue in open_issues:
        snake_case__    =					sorted([comment for comment in issue.get_comments()]     ,		key=lambda __lowerCAmelCase				: i.created_at     ,		reverse=__lowerCAmelCase				)
        snake_case__    =					comments[0] if len(__lowerCAmelCase				) > 0 else None
        snake_case__    =					dt.utcnow()
        snake_case__    =					(current_time - issue.updated_at).days
        snake_case__    =					(current_time - issue.created_at).days
        if (
            last_comment is not None
            and last_comment.user.login == "github-actions[bot]"
            and days_since_updated > 7
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Close issue since it has been 7 days of inactivity since bot mention.
            issue.edit(state="closed"				)
        elif (
            days_since_updated > 23
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Add stale comment
            issue.create_comment(
                "This issue has been automatically marked as stale because it has not had "
                "recent activity. If you think this still needs to be addressed "
                "please comment on this thread.\n\nPlease note that issues that do not follow the "
                "[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
                "are likely to be ignored."				)


if __name__ == "__main__":
     main()



