

"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def   _A					(	_a	:						str     ,   _a	:						Union[str, Any]     ,   _a	:						int     ,   _a	:						Union[str, Any]				):



      """simple docstring"""






      A				  =		s.rsplit(_a     ,   _a				)
      return new.join(_a				)



def   _A					(	_a	:						Optional[int]				):



      """simple docstring"""






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



def   _A					(	_a	:						Optional[int]				):



      """simple docstring"""






      A				  =		{}

      A				  =		["""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:
                        A				  =		key.replace(f'{group_key}.'     ,   f'{group_key}.group.'				)

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

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

            A				  =		value.float()

      return upgrade



@torch.no_grad()
def   _A					(	_a	:						List[Any]     ,   _a	:						Any     ,   _a	:						Union[str, Any]=None     ,   _a	:						List[Any]=True				):



      """simple docstring"""






      from dall_e import Encoder

      A				  =		Encoder()
      if os.path.exists(_a				):
            A				  =		torch.load(_a				)
      else:
            A				  =		torch.hub.load_state_dict_from_url(_a				)

      if isinstance(_a     ,   _a				):
            A				  =		ckpt.state_dict()
      encoder.load_state_dict(_a				)

      if config_path is not None:
            A				  =		FlavaImageCodebookConfig.from_pretrained(_a				)
      else:
            A				  =		FlavaImageCodebookConfig()

      A				  =		FlavaImageCodebook(_a				).eval()
      A				  =		encoder.state_dict()

      A				  =		upgrade_state_dict(_a				)
      hf_model.load_state_dict(_a				)
      A				  =		hf_model.state_dict()
      A				  =		count_parameters(_a				)
      A				  =		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__":
  UpperCAmelCase													=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")
  UpperCAmelCase													=parser.parse_args()

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







"""simple docstring"""


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




