'''simple docstring'''




import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 				snake_case__							(			_A:					Optional[int]      ,   _A:					Union[str, Any]      ,   _A:					Dict      ,   _A:					Optional[Any] )    ->	List[str]:

		'''simple docstring'''


		lowerCAmelCase   					=  s.rsplit(_A      ,   _A )
		return new.join(_A )






def 				snake_case__							(			_A:					List[Any] )    ->	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 				snake_case__							(			_A:					str )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  {}

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

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

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

				lowerCAmelCase   					=  value.float()

		return upgrade






@torch.no_grad()
def 				snake_case__							(			_A:					Dict      ,   _A:					List[str]      ,   _A:					str=None      ,   _A:					Union[str, Any]=True )    ->	Any:

		'''simple docstring'''


		from dall_e import Encoder

		lowerCAmelCase   					=  Encoder()
		if os.path.exists(_A ):
				lowerCAmelCase   					=  torch.load(_A )
		else:
				lowerCAmelCase   					=  torch.hub.load_state_dict_from_url(_A )

		if isinstance(_A      ,   _A ):
				lowerCAmelCase   					=  ckpt.state_dict()
		encoder.load_state_dict(_A )

		if config_path is not None:
				lowerCAmelCase   					=  FlavaImageCodebookConfig.from_pretrained(_A )
		else:
				lowerCAmelCase   					=  FlavaImageCodebookConfig()

		lowerCAmelCase   					=  FlavaImageCodebook(_A ).eval()
		lowerCAmelCase   					=  encoder.state_dict()

		lowerCAmelCase   					=  upgrade_state_dict(_A )
		hf_model.load_state_dict(_A )
		lowerCAmelCase   					=  hf_model.state_dict()
		lowerCAmelCase   					=  count_parameters(_A )
		lowerCAmelCase   					=  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__":
						__lowercase          =  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''')
						__lowercase          =  parser.parse_args()

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





'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase






def 				snake_case__							(			_A:					str )    ->	str:

		'''simple docstring'''


		if not sentence:
				return ""
		lowerCAmelCase   					=  dict(zip(_A      ,   _A ) )
		return lower_to_upper.get(sentence[0]      ,   sentence[0] ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




