



'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__  :     List[Any]     							=     logging.get_logger(__name__)
class     UpperCAmelCase__					(      UpperCAmelCase_):



 def __init__(					self						,		*lowercase						,		**lowercase     )					->							None:
  warnings.warn(
      """The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
      """ Please use PerceiverImageProcessor instead."""						,		lowercase						,		)
  super().__init__(*lowercase						,		**lowercase     )










'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def _lowercase				(					__A							,__A							,__A							,__A    ):



 '''simple docstring'''



 __UpperCamelCase			 =					s.rsplit(__A							,__A    )
 return new.join(__A    )







def _lowercase				(					__A    ):



 '''simple docstring'''



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







def _lowercase				(					__A    ):



 '''simple docstring'''



 __UpperCamelCase			 =					{}

 __UpperCamelCase			 =					["""group_1""", """group_2""", """group_3""", """group_4"""]
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    __UpperCamelCase			 =					key.replace(f"{group_key}."							,f"{group_key}.group."    )

  if "res_path" in key:
   __UpperCamelCase			 =					key.replace("""res_path."""							,"""res_path.path."""    )

  if key.endswith(""".w"""    ):
   __UpperCamelCase			 =					rreplace(__A							,""".w"""							,""".weight"""							,1    )
  if key.endswith(""".b"""    ):
   __UpperCamelCase			 =					rreplace(__A							,""".b"""							,""".bias"""							,1    )

  __UpperCamelCase			 =					value.float()

 return upgrade







@torch.no_grad()
def _lowercase				(					__A							,__A							,__A=None							,__A=True    ):



 '''simple docstring'''



 from dall_e import Encoder

 __UpperCamelCase			 =					Encoder()
 if os.path.exists(__A    ):
  __UpperCamelCase			 =					torch.load(__A    )
 else:
  __UpperCamelCase			 =					torch.hub.load_state_dict_from_url(__A    )

 if isinstance(__A							,__A    ):
  __UpperCamelCase			 =					ckpt.state_dict()
 encoder.load_state_dict(__A    )

 if config_path is not None:
  __UpperCamelCase			 =					FlavaImageCodebookConfig.from_pretrained(__A    )
 else:
  __UpperCamelCase			 =					FlavaImageCodebookConfig()

 __UpperCamelCase			 =					FlavaImageCodebook(__A    ).eval()
 __UpperCamelCase			 =					encoder.state_dict()

 __UpperCamelCase			 =					upgrade_state_dict(__A    )
 hf_model.load_state_dict(__A    )
 __UpperCamelCase			 =					hf_model.state_dict()
 __UpperCamelCase			 =					count_parameters(__A    )
 __UpperCamelCase			 =					count_parameters(__A    )

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

 if save_checkpoint:
  hf_model.save_pretrained(__A    )
 else:
  return hf_state_dict


if __name__ == "__main__":
 a__  :     Optional[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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 a__  :     Dict     							=     parser.parse_args()

 convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





