
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       s.rsplit(_UpperCamelCase					,				_UpperCamelCase				)
	return new.join(_UpperCamelCase				)






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	# 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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       {}

	lowercase	    =       ['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:
				lowercase	    =       key.replace(F'''{group_key}.'''					,				F'''{group_key}.group.'''				)

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

		if key.endswith('.w'				):
			lowercase	    =       rreplace(_UpperCamelCase					,				'.w'					,				'.weight'					,				1				)
		if key.endswith('.b'				):
			lowercase	    =       rreplace(_UpperCamelCase					,				'.b'					,				'.bias'					,				1				)

		lowercase	    =       value.float()

	return upgrade






@torch.no_grad()
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=True				):
	from dall_e import Encoder

	lowercase	    =       Encoder()
	if os.path.exists(_UpperCamelCase				):
		lowercase	    =       torch.load(_UpperCamelCase				)
	else:
		lowercase	    =       torch.hub.load_state_dict_from_url(_UpperCamelCase				)

	if isinstance(_UpperCamelCase					,				_UpperCamelCase				):
		lowercase	    =       ckpt.state_dict()
	encoder.load_state_dict(_UpperCamelCase				)

	if config_path is not None:
		lowercase	    =       FlavaImageCodebookConfig.from_pretrained(_UpperCamelCase				)
	else:
		lowercase	    =       FlavaImageCodebookConfig()

	lowercase	    =       FlavaImageCodebook(_UpperCamelCase				).eval()
	lowercase	    =       encoder.state_dict()

	lowercase	    =       upgrade_state_dict(_UpperCamelCase				)
	hf_model.load_state_dict(_UpperCamelCase				)
	lowercase	    =       hf_model.state_dict()
	lowercase	    =       count_parameters(_UpperCamelCase				)
	lowercase	    =       count_parameters(_UpperCamelCase				)

	assert torch.allclose(_UpperCamelCase					,				_UpperCamelCase					,				atol=1e-3				)

	if save_checkpoint:
		hf_model.save_pretrained(_UpperCamelCase				)
	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)






def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






