






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase      :       List[Any]    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       torch.load(__lowerCAmelCase       ,			map_location="cpu"							)
     if "model" in sd.keys():
          __lowerCAmelCase             =       torch.load(__lowerCAmelCase       ,			map_location="cpu"							)["model"]

     # pop unnecessary weights
     __lowerCAmelCase             =       [
         "decoder.version",
         "decoder.output_projection.weight",
     ]
     for key in keys_to_delete:
          if key in sd:
               sd.pop(__lowerCAmelCase							)

     __lowerCAmelCase             =       {
         "decoder.project_in_dim.weight": "decoder.project_in.weight",
         "decoder.project_out_dim.weight": "decoder.project_out.weight",
         "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
         "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
     }
     for old_key, new_key in keys_to_rename.items():
          if old_key in sd:
               __lowerCAmelCase             =       sd.pop(__lowerCAmelCase							)

     __lowerCAmelCase             =       list(sd.keys()							)
     for key in keys:
          if ".qkv_proj." in key:
               __lowerCAmelCase             =       sd[key]
               # We split QKV in separate Q,K,V

               __lowerCAmelCase             =       key.replace(".qkv_proj."       ,			".q_proj."							)
               __lowerCAmelCase             =       key.replace(".qkv_proj."       ,			".k_proj."							)
               __lowerCAmelCase             =       key.replace(".qkv_proj."       ,			".v_proj."							)

               __lowerCAmelCase             =       value.shape[0]
               assert depth % 3 == 0
               # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
               # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
               __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       torch.split(__lowerCAmelCase       ,			depth // 3       ,			dim=0							)

               __lowerCAmelCase             =       q
               __lowerCAmelCase             =       k
               __lowerCAmelCase             =       v
               del sd[key]

     return sd





@torch.no_grad()
def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=None							):
     __lowerCAmelCase             =       load_checkpoint(__lowerCAmelCase							)

     if config is not None:
          __lowerCAmelCase             =       OPTConfig.from_pretrained(__lowerCAmelCase							)
     else:
          __lowerCAmelCase             =       OPTConfig()

     __lowerCAmelCase             =       OPTModel(__lowerCAmelCase							).half().eval()
     model.load_state_dict(__lowerCAmelCase							)

     # Check results
     Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
     model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
 lowerCamelCase      :       Any    =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--fairseq_path''',
     type=str,
     help=(
         '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
         ''' https://huggingface.co/models?other=opt_metasq'''
     ),
 )
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
 lowerCamelCase      :       Any    =						parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








from __future__ import annotations

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      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()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      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:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [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],
			]

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

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

			_A, _A  =	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])



