

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			Union[str, Any]		,__a      :			int		,__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Optional[Any]         =		s.rsplit(__A		,__A	)
					return new.join(__A	)



def    __UpperCAmelCase (	__a      :			List[Any]	)			->     List[str]:

					"""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      :			str	)			->     int:

					"""simple docstring"""

					_a	:    Tuple         =		{}

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

										if "res_path" in key:
															_a	:    Union[str, Any]         =		key.replace('''res_path.'''		,'''res_path.path.'''	)

										if key.endswith('''.w'''	):
															_a	:    Dict         =		rreplace(__A		,'''.w'''		,'''.weight'''		,1	)
										if key.endswith('''.b'''	):
															_a	:    Optional[Any]         =		rreplace(__A		,'''.b'''		,'''.bias'''		,1	)

										_a	:    Dict         =		value.float()

					return upgrade



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Optional[int]		,__a      :			Optional[int]=None		,__a      :			List[Any]=True	)			->     List[Any]:

					"""simple docstring"""

					from dall_e import Encoder

					_a	:    List[Any]         =		Encoder()
					if os.path.exists(__A	):
										_a	:    Tuple         =		torch.load(__A	)
					else:
										_a	:    List[Any]         =		torch.hub.load_state_dict_from_url(__A	)

					if isinstance(__A		,__A	):
										_a	:    Optional[int]         =		ckpt.state_dict()
					encoder.load_state_dict(__A	)

					if config_path is not None:
										_a	:    Optional[int]         =		FlavaImageCodebookConfig.from_pretrained(__A	)
					else:
										_a	:    Any         =		FlavaImageCodebookConfig()

					_a	:    int         =		FlavaImageCodebook(__A	).eval()
					_a	:    List[str]         =		encoder.state_dict()

					_a	:    Any         =		upgrade_state_dict(__A	)
					hf_model.load_state_dict(__A	)
					_a	:    int         =		hf_model.state_dict()
					_a	:    Optional[Any]         =		count_parameters(__A	)
					_a	:    Tuple         =		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__				=		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__				=		parser.parse_args()

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







def        SCREAMING_SNAKE_CASE_						(			__A :			list[int]  ,    __A :			str				)	->		list[int]:

       """simple docstring"""

       a_					:		Any							=  int(__A				)

       # Initialize Result
       a_					:		Tuple							=  []

       # Traverse through all denomination
       for denomination in reversed(__A				):
              # Find denominations
              while int(__A				) >= int(__A				):
                     total_value -= int(__A				)
                     answer.append(__A				)  # Append the "answers" array

       return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase_      : Union[str, Any]  					=       []
   UpperCAmelCase_      : Union[str, Any]  					=       '0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      UpperCAmelCase_      : List[Any]  					=       int(input('Enter the number of denominations you want to add: ').strip())

      for i in range(0, n):
         denominations.append(int(input(F'Denomination {i}: ').strip()))
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase_      : List[Any]  					=       [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase_      : str  					=       input('Enter the change you want to make: ').strip()

   if int(value) == 0 or int(value) < 0:
      print('The total value cannot be zero or negative.')

   else:
      print(F'Following is minimal change for {value}: ')
      UpperCAmelCase_      : Optional[Any]  					=       find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')


