




'''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__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						{}

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

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

            SCREAMING_SNAKE_CASE   :      Dict    					=						value.float()

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

      return upgrade




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=None    ):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      int    					=						FlavaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlavaConfig()

      SCREAMING_SNAKE_CASE   :      str    					=						FlavaForPreTraining(__UpperCamelCase    ).eval()

      SCREAMING_SNAKE_CASE   :      int    					=						convert_dalle_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,save_checkpoint=__UpperCamelCase    )

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

      SCREAMING_SNAKE_CASE   :      int    					=						upgrade_state_dict(__UpperCamelCase      ,__UpperCamelCase    )
      hf_model.load_state_dict(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hf_model.state_dict()
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						count_parameters(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						count_parameters(__UpperCamelCase    ) + count_parameters(__UpperCamelCase    )

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

      hf_model.save_pretrained(__UpperCamelCase    )


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

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






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__snake_case   							=						0

__snake_case   							=						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__snake_case   							=						[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__snake_case   							=						tuple[int, int]



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: int,A_: int,A_: int,A_: int,A_: int,A_: Node | None,):






            '''simple docstring'''




            __UpperCamelCase =			pos_x
            __UpperCamelCase =			pos_y
            __UpperCamelCase =			(pos_y, pos_x)
            __UpperCamelCase =			goal_x
            __UpperCamelCase =			goal_y
            __UpperCamelCase =			g_cost
            __UpperCamelCase =			parent
            __UpperCamelCase =			self.calculate_heuristic()
            __UpperCamelCase =			self.g_cost + self.h_cost







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.pos_x - self.goal_x
            __UpperCamelCase =			self.pos_y - self.goal_y
            if HEURISTIC == 1:
                 return abs(A_					) + abs(A_					)
            else:
                 return sqrt(dy**2 + dx**2					)







       def __lt__(			self: int,A_: Node					):






            '''simple docstring'''




            return self.f_cost < other.f_cost



class 	__lowerCamelCase			:







       def __init__(			self: Any,A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			Node(start[1],start[0],goal[1],goal[0],0,A_					)
            __UpperCamelCase =			Node(goal[1],goal[0],goal[1],goal[0],9_9999,A_					)

            __UpperCamelCase =			[self.start]
            __UpperCamelCase =			[]

            __UpperCamelCase =			False







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 __UpperCamelCase =			self.open_nodes.pop(0					)

                 if current_node.pos == self.target.pos:
                      return self.retrace_path(A_					)

                 self.closed_nodes.append(A_					)
                 __UpperCamelCase =			self.get_successors(A_					)

                 for child_node in successors:
                      if child_node in self.closed_nodes:
                           continue

                      if child_node not in self.open_nodes:
                           self.open_nodes.append(A_					)
                      else:
                           # retrieve the best current path
                           __UpperCamelCase =			self.open_nodes.pop(self.open_nodes.index(A_					)					)

                           if child_node.g_cost < better_node.g_cost:
                                self.open_nodes.append(A_					)
                           else:
                                self.open_nodes.append(A_					)

            return [self.start.pos]







       def 		snake_case_	(			self: int,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            for action in delta:
                 __UpperCamelCase =			parent.pos_x + action[1]
                 __UpperCamelCase =			parent.pos_y + action[0]
                 if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(A_					) - 1):
                      continue

                 if grid[pos_y][pos_x] != 0:
                      continue

                 successors.append(
                     Node(
                         A_,A_,self.target.pos_y,self.target.pos_x,parent.g_cost + 1,A_,)					)
            return successors







       def 		snake_case_	(			self: Any,A_: Node | None					):






            '''simple docstring'''




            __UpperCamelCase =			node
            __UpperCamelCase =			[]
            while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)					)
                 __UpperCamelCase =			current_node.parent
            path.reverse()
            return path



class 	__lowerCamelCase			:







       def __init__(			self: List[Any],A_: TPosition,A_: TPosition					):






            '''simple docstring'''




            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			AStar(A_,A_					)
            __UpperCamelCase =			False







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 __UpperCamelCase =			self.fwd_astar.open_nodes.pop(0					)
                 __UpperCamelCase =			self.bwd_astar.open_nodes.pop(0					)

                 if current_bwd_node.pos == current_fwd_node.pos:
                      return self.retrace_bidirectional_path(
                          A_,A_					)

                 self.fwd_astar.closed_nodes.append(A_					)
                 self.bwd_astar.closed_nodes.append(A_					)

                 __UpperCamelCase =			current_bwd_node
                 __UpperCamelCase =			current_fwd_node

                 __UpperCamelCase =			{
                     self.fwd_astar: self.fwd_astar.get_successors(A_					),
                     self.bwd_astar: self.bwd_astar.get_successors(A_					),
                 }

                 for astar in [self.fwd_astar, self.bwd_astar]:
                      for child_node in successors[astar]:
                           if child_node in astar.closed_nodes:
                                continue

                           if child_node not in astar.open_nodes:
                                astar.open_nodes.append(A_					)
                           else:
                                # retrieve the best current path
                                __UpperCamelCase =			astar.open_nodes.pop(
                                    astar.open_nodes.index(A_					)					)

                                if child_node.g_cost < better_node.g_cost:
                                     astar.open_nodes.append(A_					)
                                else:
                                     astar.open_nodes.append(A_					)

            return [self.fwd_astar.start.pos]







       def 		snake_case_	(			self: List[str],A_: Node,A_: Node					):






            '''simple docstring'''




            __UpperCamelCase =			self.fwd_astar.retrace_path(A_					)
            __UpperCamelCase =			self.bwd_astar.retrace_path(A_					)
            bwd_path.pop()
            bwd_path.reverse()
            __UpperCamelCase =			fwd_path + bwd_path
            return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    __snake_case   							=						(0, 0)
    __snake_case   							=						(len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    __snake_case   							=						time.time()
    __snake_case   							=						AStar(init, goal)
    __snake_case   							=						a_star.search()
    __snake_case   							=						time.time() - start_time
    print(f"""AStar execution time = {end_time:f} seconds""")

    __snake_case   							=						time.time()
    __snake_case   							=						BidirectionalAStar(init, goal)
    __snake_case   							=						time.time() - bd_start_time
    print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")


