



'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a							   =				logging.get_logger(__name__)







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[int]      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			if "model" in sd.keys():
						snake_case:    Tuple      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)['model']

			# pop unnecessary weights
			snake_case:    str      							=[
			    'decoder.version',
			    'decoder.output_projection.weight',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__UpperCAmelCase					)

			snake_case:    Any      							={
			    '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:
									snake_case:    str      							=sd.pop(__UpperCAmelCase					)

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

									snake_case:    List[Any]      							=key.replace('.qkv_proj.'				,							'.q_proj.'					)
									snake_case:    List[Any]      							=key.replace('.qkv_proj.'				,							'.k_proj.'					)
									snake_case:    List[str]      							=key.replace('.qkv_proj.'				,							'.v_proj.'					)

									snake_case:    Optional[Any]      							=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
									snake_case							,  snake_case							,  snake_case:    int      							=torch.split(__UpperCAmelCase				,							depth // 3				,							dim=0					)

									snake_case:    List[Any]      							=q
									snake_case:    Optional[Any]      							=k
									snake_case:    Optional[Any]      							=v
									del sd[key]

			return sd







@torch.no_grad()
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=None					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Tuple      							=load_checkpoint(__UpperCAmelCase					)

			if config is not None:
						snake_case:    Tuple      							=OPTConfig.from_pretrained(__UpperCAmelCase					)
			else:
						snake_case:    Tuple      							=OPTConfig()

			snake_case:    List[str]      							=OPTModel(__UpperCAmelCase					).half().eval()
			model.load_state_dict(__UpperCAmelCase					)

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


if __name__ == "__main__":
	a							   =				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.')
	a							   =				parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [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],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=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
																			snake_case:    List[Any]      							=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_			)

										if not self.reached:
													return [self.start.pos]
										return None


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=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    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	a							   =				(0, 0)
	a							   =				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




