



"""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    A_  (			_lowercase	):







						'''simple docstring'''


						return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			{}

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

												snake_case_ :str						=			value.float()

						for key, value in codebook_state_dict.items():
												snake_case_ :Optional[int]						=			value

						return upgrade
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase=None	):







						'''simple docstring'''


						if config_path is not None:
												snake_case_ :int						=			FlavaConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :int						=			FlavaConfig()

						snake_case_ :Optional[Any]						=			FlavaForPreTraining(_lowercase	).eval()

						snake_case_ :Any						=			convert_dalle_checkpoint(_lowercase,					_lowercase,					save_checkpoint=_lowercase	)

						if os.path.exists(_lowercase	):
												snake_case_ :List[str]						=			torch.load(_lowercase,					map_location="""cpu"""	)
						else:
												snake_case_ :Optional[int]						=			torch.hub.load_state_dict_from_url(_lowercase,					map_location="""cpu"""	)

						snake_case_ :List[Any]						=			upgrade_state_dict(_lowercase,					_lowercase	)
						hf_model.load_state_dict(_lowercase	)
						snake_case_ :Optional[int]						=			hf_model.state_dict()
						snake_case_ :int						=			count_parameters(_lowercase	)
						snake_case_ :Optional[Any]						=			count_parameters(_lowercase	) + count_parameters(_lowercase	)

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

						hf_model.save_pretrained(_lowercase	)


if __name__ == "__main__":
						__a      =				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")
						__a      =				parser.parse_args()

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



from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()