






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:							int 				=				logging.get_logger()







@dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  42
     __lowerCamelCase      				=  field(default_factory=__lowercase		)
     __lowerCamelCase      				=  field(default_factory=__lowercase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Tuple				      =			len(list(m.modules()		)		) == 1 or isinstance(_lowerCAmelCase	,						nn.Convad		) or isinstance(_lowerCAmelCase	,						nn.BatchNormad		)
          if has_not_submodules:
               self.traced.append(_lowerCAmelCase		)




     def __call__(				self	,						_lowerCAmelCase		):

          for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook		)		)
          self.module(_lowerCAmelCase		)
          [x.remove() for x in self.handles]
          return self




     @property
     def   __UpperCAmelCase  (				self		):
          return list(filter(lambda _lowerCAmelCase		: len(list(x.state_dict().keys()		)		) > 0	,						self.traced		)		)







@dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  42
     __lowerCamelCase      				=  42
     __lowerCamelCase      				=  1
     __lowerCamelCase      				=  field(default_factory=__lowercase		)
     __lowerCamelCase      				=  field(default_factory=__lowercase		)
     __lowerCamelCase      				=  True




     def __call__(				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			Tracker(self.dest		)(_lowerCAmelCase		).parametrized
          UpperCAmelCase__      :  Dict				      =			Tracker(self.src		)(_lowerCAmelCase		).parametrized

          UpperCAmelCase__      :  List[str]				      =			list(filter(lambda _lowerCAmelCase		: type(_lowerCAmelCase		) not in self.src_skip	,						_lowerCAmelCase		)		)
          UpperCAmelCase__      :  List[str]				      =			list(filter(lambda _lowerCAmelCase		: type(_lowerCAmelCase		) not in self.dest_skip	,						_lowerCAmelCase		)		)

          if len(_lowerCAmelCase		) != len(_lowerCAmelCase		) and self.raise_if_mismatch:
               raise Exception(
                   f"Numbers of operations are different. Source module has {len(_lowerCAmelCase		)} operations while"
                   f" destination module has {len(_lowerCAmelCase		)}."		)

          for dest_m, src_m in zip(_lowerCAmelCase	,						_lowerCAmelCase		):
               dest_m.load_state_dict(src_m.state_dict()		)
               if self.verbose == 1:
                    print(f"Transfered from={src_m} to={dest_m}"		)







class 				UpperCAmelCase_  (		nn.Module		):




     def __init__(				self	,						_lowerCAmelCase		):
          super().__init__()

          UpperCAmelCase__      :  List[Tuple[str, nn.Module]]				      =			[]
          # - get the stem
          feature_blocks.append(("""conv1""", model.stem)		)
          # - get all the feature blocks
          for k, v in model.trunk_output.named_children():
               assert k.startswith("""block"""		), f"Unexpected layer name {k}"
               UpperCAmelCase__      :  Optional[int]				      =			len(_lowerCAmelCase		) + 1
               feature_blocks.append((f"res{block_index}", v)		)

          UpperCAmelCase__      :  str				      =			nn.ModuleDict(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return get_trunk_forward_outputs(
              _lowerCAmelCase	,						out_feat_keys=_lowerCAmelCase	,						feature_blocks=self._feature_blocks	,						)







class 				UpperCAmelCase_  (		__lowercase		):




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  List[Any]				      =			x.split("""-"""		)
          return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)




     def __getitem__(				self	,						_lowerCAmelCase		):

          if x not in self:
               UpperCAmelCase__      :  Tuple				      =			self.convert_name_to_timm(_lowerCAmelCase		)
               UpperCAmelCase__      :  List[str]				      =			partial(lambda: (timm.create_model(_lowerCAmelCase	,						pretrained=_lowerCAmelCase		).eval(), None)		)

          else:
               UpperCAmelCase__      :  Tuple				      =			super().__getitem__(_lowerCAmelCase		)

          return val







class 				UpperCAmelCase_  (		__lowercase		):




     def __getitem__(				self	,						_lowerCAmelCase		):

          if "seer" in x and "in1k" not in x:
               UpperCAmelCase__      :  Any				      =			RegNetModel
          else:
               UpperCAmelCase__      :  Tuple				      =			RegNetForImageClassification
          return val





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			Optional[int]:

     '''simple docstring'''


     for from_key, to_key in keys:
          UpperCAmelCase__      :  int				      =			from_state_dict[from_key].clone()
          print(F"Copied key={from_key} to={to_key}"       )
     return to_state_dict





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase = True ,							) ->			Union[str, Any]:

     '''simple docstring'''
     print(F"Converting {name}..."       )
     with torch.no_grad():
          UpperCAmelCase__      :  str				      =			from_model_func()
          UpperCAmelCase__      :  Optional[Any]				      =			our_model_func(_lowerCamelCase       ).eval()
          UpperCAmelCase__      :  Dict				      =			ModuleTransfer(src=_lowerCamelCase ,							dest=_lowerCamelCase ,							raise_if_mismatch=_lowerCamelCase       )
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.randn((1, 3, 224, 224)       )
          module_transfer(_lowerCamelCase       )

     if from_state_dict is not None:
          UpperCAmelCase__      :  Optional[Any]				      =			[]
          # for seer - in1k finetuned we have to manually copy the head
          if "seer" in name and "in1k" in name:
               UpperCAmelCase__      :  Any				      =			[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
          UpperCAmelCase__      :  List[str]				      =			manually_copy_vissl_head(_lowerCamelCase ,							our_model.state_dict() ,							_lowerCamelCase       )
          our_model.load_state_dict(_lowerCamelCase       )

     UpperCAmelCase__      :  str				      =			our_model(_lowerCamelCase ,							output_hidden_states=_lowerCamelCase       )
     UpperCAmelCase__      :  Any				      =			(
         our_outputs.logits if isinstance(_lowerCamelCase ,							_lowerCamelCase       ) else our_outputs.last_hidden_state
     )

     UpperCAmelCase__      :  List[str]				      =			from_model(_lowerCamelCase       )
     UpperCAmelCase__      :  List[Any]				      =			from_output[-1] if type(_lowerCamelCase       ) is list else from_output

     # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
     if "seer" in name and "in1k" in name:
          UpperCAmelCase__      :  Union[str, Any]				      =			our_outputs.hidden_states[-1]

     assert torch.allclose(_lowerCamelCase ,							_lowerCamelCase       ), "The model logits don't match the original one."

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / name ,							commit_message="""Add model""" ,							use_temp_dir=_lowerCamelCase ,							)

          UpperCAmelCase__      :  Optional[int]				      =			224 if """seer""" not in name else 384
          # we can use the convnext one
          UpperCAmelCase__      :  List[Any]				      =			AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" ,							size=_lowerCamelCase       )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name ,							commit_message="""Add image processor""" ,							use_temp_dir=_lowerCamelCase ,							)

          print(F"Pushed {name}"       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase = None ,							__lowerCamelCase = True       ) ->			List[str]:

     '''simple docstring'''
     UpperCAmelCase__      :  int				      =			"""imagenet-1k-id2label.json"""
     UpperCAmelCase__      :  int				      =			1000
     UpperCAmelCase__      :  Any				      =			(1, num_labels)

     UpperCAmelCase__      :  Union[str, Any]				      =			"""huggingface/label-files"""
     UpperCAmelCase__      :  List[str]				      =			num_labels
     UpperCAmelCase__      :  int				      =			json.load(open(cached_download(hf_hub_url(_lowerCamelCase ,							_lowerCamelCase ,							repo_type="""dataset"""       )       ) ,							"""r"""       )       )
     UpperCAmelCase__      :  Optional[int]				      =			{int(_lowerCamelCase       ): v for k, v in idalabel.items()}

     UpperCAmelCase__      :  Optional[Any]				      =			idalabel
     UpperCAmelCase__      :  Any				      =			{v: k for k, v in idalabel.items()}

     UpperCAmelCase__      :  int				      =			partial(_lowerCamelCase ,							num_labels=_lowerCamelCase ,							idalabel=_lowerCamelCase ,							labelaid=_lowerCamelCase       )

     UpperCAmelCase__      :  Optional[Any]				      =			{
         """regnet-x-002""": ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7] ,							hidden_sizes=[24, 56, 152, 368] ,							groups_width=8 ,							layer_type="""x"""       ),
         """regnet-x-004""": ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 12] ,							hidden_sizes=[32, 64, 160, 384] ,							groups_width=16 ,							layer_type="""x"""       ),
         """regnet-x-006""": ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7] ,							hidden_sizes=[48, 96, 240, 528] ,							groups_width=24 ,							layer_type="""x"""       ),
         """regnet-x-008""": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5] ,							hidden_sizes=[64, 128, 288, 672] ,							groups_width=16 ,							layer_type="""x"""       ),
         """regnet-x-016""": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 2] ,							hidden_sizes=[72, 168, 408, 912] ,							groups_width=24 ,							layer_type="""x"""       ),
         """regnet-x-032""": ImageNetPreTrainedConfig(
             depths=[2, 6, 15, 2] ,							hidden_sizes=[96, 192, 432, 1008] ,							groups_width=48 ,							layer_type="""x"""       ),
         """regnet-x-040""": ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2] ,							hidden_sizes=[80, 240, 560, 1360] ,							groups_width=40 ,							layer_type="""x"""       ),
         """regnet-x-064""": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1] ,							hidden_sizes=[168, 392, 784, 1624] ,							groups_width=56 ,							layer_type="""x"""       ),
         """regnet-x-080""": ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1] ,							hidden_sizes=[80, 240, 720, 1920] ,							groups_width=120 ,							layer_type="""x"""       ),
         """regnet-x-120""": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1] ,							hidden_sizes=[224, 448, 896, 2240] ,							groups_width=112 ,							layer_type="""x"""       ),
         """regnet-x-160""": ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1] ,							hidden_sizes=[256, 512, 896, 2048] ,							groups_width=128 ,							layer_type="""x"""       ),
         """regnet-x-320""": ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1] ,							hidden_sizes=[336, 672, 1344, 2520] ,							groups_width=168 ,							layer_type="""x"""       ),
         # y variant
         """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] ,							hidden_sizes=[24, 56, 152, 368] ,							groups_width=8       ),
         """regnet-y-004""": ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6] ,							hidden_sizes=[48, 104, 208, 440] ,							groups_width=8       ),
         """regnet-y-006""": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4] ,							hidden_sizes=[48, 112, 256, 608] ,							groups_width=16       ),
         """regnet-y-008""": ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2] ,							hidden_sizes=[64, 128, 320, 768] ,							groups_width=16       ),
         """regnet-y-016""": ImageNetPreTrainedConfig(
             depths=[2, 6, 17, 2] ,							hidden_sizes=[48, 120, 336, 888] ,							groups_width=24       ),
         """regnet-y-032""": ImageNetPreTrainedConfig(
             depths=[2, 5, 13, 1] ,							hidden_sizes=[72, 216, 576, 1512] ,							groups_width=24       ),
         """regnet-y-040""": ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2] ,							hidden_sizes=[128, 192, 512, 1088] ,							groups_width=64       ),
         """regnet-y-064""": ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2] ,							hidden_sizes=[144, 288, 576, 1296] ,							groups_width=72       ),
         """regnet-y-080""": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1] ,							hidden_sizes=[168, 448, 896, 2016] ,							groups_width=56       ),
         """regnet-y-120""": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1] ,							hidden_sizes=[224, 448, 896, 2240] ,							groups_width=112       ),
         """regnet-y-160""": ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1] ,							hidden_sizes=[224, 448, 1232, 3024] ,							groups_width=112       ),
         """regnet-y-320""": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1] ,							hidden_sizes=[232, 696, 1392, 3712] ,							groups_width=232       ),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,							hidden_sizes=[232, 696, 1392, 3712] ,							groups_width=232       ),
         """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,							hidden_sizes=[328, 984, 1968, 4920] ,							groups_width=328       ),
         """regnet-y-1280-seer""": RegNetConfig(
             depths=[2, 7, 17, 1] ,							hidden_sizes=[528, 1056, 2904, 7392] ,							groups_width=264       ),
         """regnet-y-2560-seer""": RegNetConfig(
             depths=[3, 7, 16, 1] ,							hidden_sizes=[640, 1696, 2544, 5088] ,							groups_width=640       ),
         """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1] ,							hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,							groups_width=1010       ),
         # finetuned on imagenet
         """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1] ,							hidden_sizes=[232, 696, 1392, 3712] ,							groups_width=232       ),
         """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1] ,							hidden_sizes=[328, 984, 1968, 4920] ,							groups_width=328       ),
         """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1] ,							hidden_sizes=[528, 1056, 2904, 7392] ,							groups_width=264       ),
         """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1] ,							hidden_sizes=[640, 1696, 2544, 5088] ,							groups_width=640       ),
         """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1] ,							hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,							groups_width=1010       ),
     }

     UpperCAmelCase__      :  List[str]				      =			NameToOurModelFuncMap()
     UpperCAmelCase__      :  List[str]				      =			NameToFromModelFuncMap()
     # add seer weights logic

     def load_using_classy_vision(__lowerCamelCase ,							__lowerCamelCase       ) -> Tuple[nn.Module, Dict]:
          UpperCAmelCase__      :  List[str]				      =			torch.hub.load_state_dict_from_url(_lowerCamelCase ,							model_dir=str(_lowerCamelCase       ) ,							map_location="""cpu"""       )
          UpperCAmelCase__      :  Optional[Any]				      =			model_func()
          # check if we have a head, if yes add it
          UpperCAmelCase__      :  str				      =			files["""classy_state_dict"""]["""base_model"""]["""model"""]
          UpperCAmelCase__      :  Any				      =			model_state_dict["""trunk"""]
          model.load_state_dict(_lowerCamelCase       )
          return model.eval(), model_state_dict["heads"]

     # pretrained
     UpperCAmelCase__      :  List[str]				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,							)

     UpperCAmelCase__      :  List[str]				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,							)

     UpperCAmelCase__      :  Tuple				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()       ) ,							)

     UpperCAmelCase__      :  str				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,							lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27 ,							group_width=1010 ,							w_a=1744 ,							w_a=620.83 ,							w_m=2.52       )       )       ) ,							)

     # IN1K finetuned
     UpperCAmelCase__      :  Dict				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,							)

     UpperCAmelCase__      :  List[Any]				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()       ) ,							)

     UpperCAmelCase__      :  Union[str, Any]				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()       ) ,							)

     UpperCAmelCase__      :  str				      =			partial(
         _lowerCamelCase ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" ,							lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27 ,							group_width=1010 ,							w_a=1744 ,							w_a=620.83 ,							w_m=2.52       )       )       ) ,							)

     if model_name:
          convert_weight_and_push(
              _lowerCamelCase ,							names_to_from_model_map[model_name] ,							names_to_ours_model_map[model_name] ,							names_to_config[model_name] ,							_lowerCamelCase ,							_lowerCamelCase ,							)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   _lowerCamelCase ,							names_to_from_model_map[model_name] ,							names_to_ours_model_map[model_name] ,							_lowerCamelCase ,							_lowerCamelCase ,							_lowerCamelCase ,							)
     return config, expected_shape


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							Tuple 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
              """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      SCREAMING_SNAKE_CASE__		:							str 				=				parser.parse_args()

      SCREAMING_SNAKE_CASE__		:							str 				=				args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)













'''simple docstring'''






from __future__ import annotations

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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				tuple[list[list[int]], list[list[int]]]:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the reference grid
			__snake_case					:      Tuple							      =     1
			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the action grid

			__snake_case					:      List[str]							      =     init[0]
			__snake_case					:      str							      =     init[1]
			__snake_case					:      int							      =     0
			__snake_case					:      int							      =     g + heuristic[x][y]  # cost from starting cell to destination cell
			__snake_case					:      List[str]							      =     [[f, g, x, y]]

			__snake_case					:      Any							      =     False  # flag that is set when search is complete
			__snake_case					:      int							      =     False  # flag set if we can't find expand

			while not found and not resign:
						if len(_lowerCamelCase ) == 0:
									raise ValueError("""Algorithm is unable to find solution""" )
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									__snake_case					:      Tuple							      =     cell.pop()
									__snake_case					:      Optional[int]							      =     next_cell[2]
									__snake_case					:      List[Any]							      =     next_cell[3]
									__snake_case					:      int							      =     next_cell[1]

									if x == goal[0] and y == goal[1]:
												__snake_case					:      Optional[Any]							      =     True
									else:
												for i in range(len(_lowerCamelCase ) ):  # to try out different valid actions
															__snake_case					:      Union[str, Any]							      =     x + DIRECTIONS[i][0]
															__snake_case					:      str							      =     y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(_lowerCamelCase ) and ya >= 0 and ya < len(grid[0] ):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					__snake_case					:      str							      =     g + cost
																					__snake_case					:      Tuple							      =     ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya] )
																					__snake_case					:      List[str]							      =     1
																					__snake_case					:      Optional[int]							      =     i
			__snake_case					:      List[str]							      =     []
			__snake_case					:      Optional[int]							      =     goal[0]
			__snake_case					:      List[Any]							      =     goal[1]
			invpath.append([x, y] )  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						__snake_case					:      Dict							      =     x - DIRECTIONS[action[x][y]][0]
						__snake_case					:      int							      =     y - DIRECTIONS[action[x][y]][1]
						__snake_case					:      Optional[int]							      =     xa
						__snake_case					:      int							      =     ya
						invpath.append([x, y] )

			__snake_case					:      Optional[int]							      =     []
			for i in range(len(_lowerCamelCase ) ):
						path.append(invpath[len(_lowerCamelCase ) - 1 - i] )
			return path, action


if __name__ == "__main__":
	__UpperCamelCase					    =	[
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	__UpperCamelCase					    =	[0, 0]
	# all coordinates are given in format [y,x]
	__UpperCamelCase					    =	[len(grid) - 1, len(grid[0]) - 1]
	__UpperCamelCase					    =	1

	# the cost map which pushes the path closer to the goal
	__UpperCamelCase					    =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			__UpperCamelCase					    =	abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				__UpperCamelCase					    =	99

	__UpperCamelCase    ,		__UpperCamelCase					    =	search(grid, init, goal, cost, heuristic)

	print("ACTION MAP")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])


