

"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      str      		=	s.rsplit(__lowerCAmelCase  ,					__lowerCAmelCase							)
      return new.join(__lowerCAmelCase							)




def  _lowercase					(    __lowerCAmelCase							)       ->				Dict:
      # encoder.embeddings are double copied in original FLAVA
      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()							)




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	{}

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""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:
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	key.replace(F'''{group_key}.'''  ,					F'''{group_key}.group.'''							)

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

            if key.endswith(""".w"""							):
                  SCREAMING_SNAKE_CASE__   :      Any      		=	rreplace(__lowerCAmelCase  ,					""".w"""  ,					""".weight"""  ,					1							)
            if key.endswith(""".b"""							):
                  SCREAMING_SNAKE_CASE__   :      List[Any]      		=	rreplace(__lowerCAmelCase  ,					""".b"""  ,					""".bias"""  ,					1							)

            SCREAMING_SNAKE_CASE__   :      Any      		=	value.float()

      return upgrade




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=True							)       ->				List[str]:
      from dall_e import Encoder

      SCREAMING_SNAKE_CASE__   :      int      		=	Encoder()
      if os.path.exists(__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.load(__lowerCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE__   :      int      		=	torch.hub.load_state_dict_from_url(__lowerCAmelCase							)

      if isinstance(__lowerCAmelCase  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	ckpt.state_dict()
      encoder.load_state_dict(__lowerCAmelCase							)

      if config_path is not None:
            SCREAMING_SNAKE_CASE__   :      str      		=	FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	FlavaImageCodebookConfig()

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlavaImageCodebook(__lowerCAmelCase							).eval()
      SCREAMING_SNAKE_CASE__   :      Any      		=	encoder.state_dict()

      SCREAMING_SNAKE_CASE__   :      int      		=	upgrade_state_dict(__lowerCAmelCase							)
      hf_model.load_state_dict(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	hf_model.state_dict()
      SCREAMING_SNAKE_CASE__   :      int      		=	count_parameters(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	count_parameters(__lowerCAmelCase							)

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

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


if __name__ == "__main__":
  a					: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					:List[Any]    = parser.parse_args()

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







"""simple docstring"""


import os

a					:List[str]    = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Any      		=	0

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      while index < len(__lowerCAmelCase							) - 1:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	SYMBOLS[numerals[index]]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	""""""

      SCREAMING_SNAKE_CASE__   :      int      		=	num // 1000
      numerals += m_count * "M"
      num %= 1000

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals




def  _lowercase					(    __lowerCAmelCase = "/p089_roman.txt"							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      int      		=	0

      with open(os.path.dirname(__lowerCAmelCase							) + roman_numerals_filename							) as filea:
            SCREAMING_SNAKE_CASE__   :      str      		=	filea.readlines()

      for line in lines:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	line.strip()
            SCREAMING_SNAKE_CASE__   :      Dict      		=	parse_roman_numerals(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      str      		=	generate_roman_numerals(__lowerCAmelCase							)
            savings += len(__lowerCAmelCase							) - len(__lowerCAmelCase							)

      return savings


if __name__ == "__main__":
  print(f'{solution() = }')




