





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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		# 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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Optional[int] 						=      {}

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

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

				lowerCamelCase_	:     str 						=      value.float()

		for key, value in codebook_state_dict.items():
				lowerCamelCase_	:     Optional[Any] 						=      value

		return upgrade


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=None						):
		if config_path is not None:
				lowerCamelCase_	:     List[str] 						=      FlavaConfig.from_pretrained(lowerCAmelCase__						)
		else:
				lowerCamelCase_	:     Optional[int] 						=      FlavaConfig()

		lowerCamelCase_	:     Union[str, Any] 						=      FlavaForPreTraining(lowerCAmelCase__						).eval()

		lowerCamelCase_	:     List[Any] 						=      convert_dalle_checkpoint(lowerCAmelCase__							,lowerCAmelCase__							,save_checkpoint=lowerCAmelCase__						)

		if os.path.exists(lowerCAmelCase__						):
				lowerCamelCase_	:     Any 						=      torch.load(lowerCAmelCase__							,map_location='cpu'						)
		else:
				lowerCamelCase_	:     Optional[Any] 						=      torch.hub.load_state_dict_from_url(lowerCAmelCase__							,map_location='cpu'						)

		lowerCamelCase_	:     List[str] 						=      upgrade_state_dict(lowerCAmelCase__							,lowerCAmelCase__						)
		hf_model.load_state_dict(lowerCAmelCase__						)
		lowerCamelCase_	:     Union[str, Any] 						=      hf_model.state_dict()
		lowerCamelCase_	:     Dict 						=      count_parameters(lowerCAmelCase__						)
		lowerCamelCase_	:     Dict 						=      count_parameters(lowerCAmelCase__						) + count_parameters(lowerCAmelCase__						)

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

		hf_model.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
		_lowercase		:  Dict			       =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		:  Optional[Any]			       =parser.parse_args()

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







def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      []
		lowerCamelCase_	:     Tuple 						=      []
		lowerCamelCase_	:     Dict 						=      {
		    '^': 3,
		    '*': 2,
		    '/': 2,
		    '%': 2,
		    '+': 1,
		    '-': 1,
		}  # Priority of each operator
		lowerCamelCase_	:     Optional[int] 						=      len(lowerCAmelCase__						) if (len(lowerCAmelCase__						) > 7) else 7

		# Print table header for output
		print(
		    'Symbol'.center(8						)							,'Stack'.center(lowerCAmelCase__						)							,'Postfix'.center(lowerCAmelCase__						)							,sep=' | '							,)
		print('-' * (print_width * 3 + 7)						)

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(lowerCAmelCase__						)  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(lowerCAmelCase__						)  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(lowerCAmelCase__						) == 0:
								stack.append(lowerCAmelCase__						)  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(lowerCAmelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								stack.append(lowerCAmelCase__						)  # push x to stack

				print(
				    x.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		while len(lowerCAmelCase__						) > 0:  # while stack is not empty
				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
				print(
				    ' '.center(8						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,(''.join(lowerCAmelCase__						)).ljust(lowerCAmelCase__						)							,sep=' | '							,)  # Output in tabular format

		return "".join(lowerCAmelCase__						)  # return Postfix as str


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      list(infix[::-1]						)  # reverse the infix equation

		for i in range(len(lowerCAmelCase__						)						):
				if infix[i] == "(":
						lowerCamelCase_	:     str 						=      ')'  # change "(" to ")"
				elif infix[i] == ")":
						lowerCamelCase_	:     Optional[Any] 						=      '('  # change ")" to "("

		return (infix_2_postfix(''.join(lowerCAmelCase__						)						))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		_lowercase		:  int			       =input("""\nEnter an Infix Equation = """)  # Input an Infix equation
		_lowercase		:  Optional[Any]			       ="""""".join(Infix.split())  # Remove spaces from the input
		print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")
