


"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint






def 		lowercase							(     _snake_case						:      int     )     ->Dict:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				{}

	for key, value in state_dict.items():
		if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
			continue

		__snake_case	:						Union[str, Any]						=				key.replace('''heads.cmd.mim_head.cls.predictions'''     ,   '''mmm_image_head'''     )
		__snake_case	:						List[Any]						=				key.replace('''heads.cmd.mlm_head.cls.predictions'''     ,   '''mmm_text_head'''     )
		__snake_case	:						str						=				key.replace('''heads.cmd.itm_head.cls'''     ,   '''itm_head'''     )
		__snake_case	:						Optional[Any]						=				key.replace('''heads.cmd.itm_head.pooler'''     ,   '''itm_head.pooler'''     )
		__snake_case	:						str						=				key.replace('''heads.cmd.clip_head.logit_scale'''     ,   '''flava.logit_scale'''     )
		__snake_case	:						List[str]						=				key.replace('''heads.fairseq_mlm.cls.predictions'''     ,   '''mlm_head'''     )
		__snake_case	:						Optional[Any]						=				key.replace('''heads.imagenet.mim_head.cls.predictions'''     ,   '''mim_head'''     )
		__snake_case	:						str						=				key.replace('''mm_text_projection'''     ,   '''flava.text_to_mm_projection'''     )
		__snake_case	:						str						=				key.replace('''mm_image_projection'''     ,   '''flava.image_to_mm_projection'''     )
		__snake_case	:						Any						=				key.replace('''image_encoder.module'''     ,   '''flava.image_model'''     )
		__snake_case	:						Tuple						=				key.replace('''text_encoder.module'''     ,   '''flava.text_model'''     )
		__snake_case	:						Any						=				key.replace('''mm_encoder.module.encoder.cls_token'''     ,   '''flava.multimodal_model.cls_token'''     )
		__snake_case	:						List[str]						=				key.replace('''mm_encoder.module'''     ,   '''flava.multimodal_model'''     )
		__snake_case	:						Any						=				key.replace('''text_projection'''     ,   '''flava.text_projection'''     )
		__snake_case	:						Optional[Any]						=				key.replace('''image_projection'''     ,   '''flava.image_projection'''     )

		__snake_case	:						Any						=				value.float()

	for key, value in codebook_state_dict.items():
		__snake_case	:						Union[str, Any]						=				value

	return upgrade






@torch.no_grad()
def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Dict     ,   _snake_case						:      Dict     ,   _snake_case						:      Dict=None     )     ->Union[str, Any]:
	"""simple docstring"""





	if config_path is not None:
		__snake_case	:						Dict						=				FlavaConfig.from_pretrained(_snake_case     )
	else:
		__snake_case	:						Optional[Any]						=				FlavaConfig()

	__snake_case	:						List[Any]						=				FlavaForPreTraining(_snake_case     ).eval()

	__snake_case	:						Union[str, Any]						=				convert_dalle_checkpoint(_snake_case     ,   _snake_case     ,   save_checkpoint=_snake_case     )

	if os.path.exists(_snake_case     ):
		__snake_case	:						List[str]						=				torch.load(_snake_case     ,   map_location='''cpu'''     )
	else:
		__snake_case	:						List[str]						=				torch.hub.load_state_dict_from_url(_snake_case     ,   map_location='''cpu'''     )

	__snake_case	:						Any						=				upgrade_state_dict(_snake_case     ,   _snake_case     )
	hf_model.load_state_dict(_snake_case     )
	__snake_case	:						int						=				hf_model.state_dict()
	__snake_case	:						Union[str, Any]						=				count_parameters(_snake_case     )
	__snake_case	:						List[Any]						=				count_parameters(_snake_case     ) + count_parameters(_snake_case     )

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

	hf_model.save_pretrained(_snake_case     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Optional[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("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
			parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()

			convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)









"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5     ,   int(math.sqrt(_snake_case     ) + 1     )     ,   6     ):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





