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_					(     UpperCamelCase__       :						Optional[Any]			):







				'''simple docstring'''







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






def        lowerCamelCase_					(     UpperCamelCase__       :						str,		UpperCamelCase__       :						Dict			):







				'''simple docstring'''







				UpperCamelCase__					=				{}

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

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

								UpperCamelCase__					=				value.float()

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

				return upgrade






@torch.no_grad()
def        lowerCamelCase_					(     UpperCamelCase__       :						Tuple,		UpperCamelCase__       :						Dict,		UpperCamelCase__       :						Any,		UpperCamelCase__       :						Dict=None			):







				'''simple docstring'''







				if config_path is not None:
								UpperCamelCase__					=				FlavaConfig.from_pretrained(UpperCamelCase__			)
				else:
								UpperCamelCase__					=				FlavaConfig()

				UpperCamelCase__					=				FlavaForPreTraining(UpperCamelCase__			).eval()

				UpperCamelCase__					=				convert_dalle_checkpoint(UpperCamelCase__,		UpperCamelCase__,		save_checkpoint=UpperCamelCase__			)

				if os.path.exists(UpperCamelCase__			):
								UpperCamelCase__					=				torch.load(UpperCamelCase__,		map_location='''cpu'''			)
				else:
								UpperCamelCase__					=				torch.hub.load_state_dict_from_url(UpperCamelCase__,		map_location='''cpu'''			)

				UpperCamelCase__					=				upgrade_state_dict(UpperCamelCase__,		UpperCamelCase__			)
				hf_model.load_state_dict(UpperCamelCase__			)
				UpperCamelCase__					=				hf_model.state_dict()
				UpperCamelCase__					=				count_parameters(UpperCamelCase__			)
				UpperCamelCase__					=				count_parameters(UpperCamelCase__			) + count_parameters(UpperCamelCase__			)

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

				hf_model.save_pretrained(UpperCamelCase__			)


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)





import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[Any]     )  ->Any:
										lowercase         =      (0, 0)
										lowercase         =      None
										lowercase         =      0
										lowercase         =      0
										lowercase         =      0





						def __eq__(			self							:Optional[int]		,    lowerCAmelCase__							:Any     )  ->Optional[int]:
										return self.position == cell.position





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[Any]:
										print(self.position     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:Tuple=(5, 5)     )  ->str:
										lowercase         =      np.zeros(lowerCAmelCase__     )
										lowercase         =      world_size[0]
										lowercase         =      world_size[1]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										print(self.w     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Optional[Any]     )  ->Optional[Any]:
										lowercase         =      [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase         =      cell.position[0]
										lowercase         =      cell.position[1]
										lowercase         =      []
										for n in neughbour_cord:
														lowercase         =      current_x + n[0]
														lowercase         =      current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase         =      Cell()
																		lowercase         =      (x, y)
																		lowercase         =      cell
																		neighbours.append(lowerCAmelCase__     )
										return neighbours




def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []
				lowercase         =      []
				_open.append(__magic_name__						)

				while _open:
								lowercase         =      np.argmin([n.f for n in _open]						)
								lowercase         =      _open[min_f]
								_closed.append(_open.pop(__magic_name__						)						)
								if current == goal:
												break
								for n in world.get_neigbours(__magic_name__						):
												for c in _closed:
																if c == n:
																				continue
												lowercase         =      current.g + 1
												lowercase     ,	lowercase         =      n.position
												lowercase     ,	lowercase         =      goal.position
												lowercase         =      (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase         =      n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(__magic_name__						)
				lowercase         =      []
				while current.parent is not None:
								path.append(current.position						)
								lowercase         =      current.parent
				path.append(current.position						)
				return path[::-1]


if __name__ == "__main__":
						_snake_case      :							Optional[int]     	=    Gridworld()
						# Start position and goal
						_snake_case      :							Dict     	=    Cell()
						_snake_case      :							List[str]     	=    (0, 0)
						_snake_case      :							Union[str, Any]     	=    Cell()
						_snake_case      :							List[Any]     	=    (4, 4)
						print(F"path from {start.position} to {goal.position}")
						_snake_case      :							str     	=    astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												_snake_case      :							List[Any]     	=    1
						print(world.w)
