



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        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple						)	->    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        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Union[str, Any]						)	->    List[str]:
					__lowerCAmelCase						 ={}

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

										__lowerCAmelCase						 =key.replace("""heads.cmd.mim_head.cls.predictions"""					, """mmm_image_head"""						)
										__lowerCAmelCase						 =key.replace("""heads.cmd.mlm_head.cls.predictions"""					, """mmm_text_head"""						)
										__lowerCAmelCase						 =key.replace("""heads.cmd.itm_head.cls"""					, """itm_head"""						)
										__lowerCAmelCase						 =key.replace("""heads.cmd.itm_head.pooler"""					, """itm_head.pooler"""						)
										__lowerCAmelCase						 =key.replace("""heads.cmd.clip_head.logit_scale"""					, """flava.logit_scale"""						)
										__lowerCAmelCase						 =key.replace("""heads.fairseq_mlm.cls.predictions"""					, """mlm_head"""						)
										__lowerCAmelCase						 =key.replace("""heads.imagenet.mim_head.cls.predictions"""					, """mim_head"""						)
										__lowerCAmelCase						 =key.replace("""mm_text_projection"""					, """flava.text_to_mm_projection"""						)
										__lowerCAmelCase						 =key.replace("""mm_image_projection"""					, """flava.image_to_mm_projection"""						)
										__lowerCAmelCase						 =key.replace("""image_encoder.module"""					, """flava.image_model"""						)
										__lowerCAmelCase						 =key.replace("""text_encoder.module"""					, """flava.text_model"""						)
										__lowerCAmelCase						 =key.replace("""mm_encoder.module.encoder.cls_token"""					, """flava.multimodal_model.cls_token"""						)
										__lowerCAmelCase						 =key.replace("""mm_encoder.module"""					, """flava.multimodal_model"""						)
										__lowerCAmelCase						 =key.replace("""text_projection"""					, """flava.text_projection"""						)
										__lowerCAmelCase						 =key.replace("""image_projection"""					, """flava.image_projection"""						)

										__lowerCAmelCase						 =value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase						 =value

					return upgrade






@torch.no_grad()
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     int=None						)	->    int:
					if config_path is not None:
										__lowerCAmelCase						 =FlavaConfig.from_pretrained(__lowerCamelCase						)
					else:
										__lowerCAmelCase						 =FlavaConfig()

					__lowerCAmelCase						 =FlavaForPreTraining(__lowerCamelCase						).eval()

					__lowerCAmelCase						 =convert_dalle_checkpoint(__lowerCamelCase					, __lowerCamelCase					, save_checkpoint=__lowerCamelCase						)

					if os.path.exists(__lowerCamelCase						):
										__lowerCAmelCase						 =torch.load(__lowerCamelCase					, map_location="""cpu"""						)
					else:
										__lowerCAmelCase						 =torch.hub.load_state_dict_from_url(__lowerCamelCase					, map_location="""cpu"""						)

					__lowerCAmelCase						 =upgrade_state_dict(__lowerCamelCase					, __lowerCamelCase						)
					hf_model.load_state_dict(__lowerCamelCase						)
					__lowerCAmelCase						 =hf_model.state_dict()
					__lowerCAmelCase						 =count_parameters(__lowerCamelCase						)
					__lowerCAmelCase						 =count_parameters(__lowerCamelCase						) + count_parameters(__lowerCamelCase						)

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

					hf_model.save_pretrained(__lowerCamelCase						)


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('''--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''')
				lowercase_													=      parser.parse_args()

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









from __future__ import annotations






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int = 0)->				List[str]:
						__lowerCAmelCase						 =key



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""encrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.encrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""decrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.decrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




