


'''simple docstring'''


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowerCamelCase__    	=     0

lowerCamelCase__    	=     [
    [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],
]

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

lowerCamelCase__    	=     tuple[int, int]






class 	lowerCAmelCase__			:




			def __init__(		self  :     Dict	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Node | None	,  )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      pos_x
					_UpperCAmelCase		:       Optional[Any]								=      pos_y
					_UpperCAmelCase		:       Any								=      (pos_y, pos_x)
					_UpperCAmelCase		:       Optional[Any]								=      goal_x
					_UpperCAmelCase		:       Dict								=      goal_y
					_UpperCAmelCase		:       Any								=      g_cost
					_UpperCAmelCase		:       List[Any]								=      parent
					_UpperCAmelCase		:       List[Any]								=      self.calculate_heuristic()
					_UpperCAmelCase		:       Tuple								=      self.g_cost + self.h_cost




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->float:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.pos_x - self.goal_x
					_UpperCAmelCase		:       Optional[Any]								=      self.pos_y - self.goal_y
					if HEURISTIC == 1:
							return abs(lowerCamelCase__ ) + abs(lowerCamelCase__ )
					else:
							return sqrt(dy**2 + dx**2 )






			def __lt__(		self  :     Any	,  lowerCamelCase__  :     Node )   ->bool:



					'''simple docstring'''







					return self.f_cost < other.f_cost






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     TPosition	,  lowerCamelCase__  :     TPosition )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      Node(start[1]	,  start[0]	,  goal[1]	,  goal[0]	,  0	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      Node(goal[1]	,  goal[0]	,  goal[1]	,  goal[0]	,  9_99_99	,  lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      [self.start]
					_UpperCAmelCase		:       list[Node]								=      []

					_UpperCAmelCase		:       Any								=      False




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->list[TPosition]:



					'''simple docstring'''







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

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

							self.closed_nodes.append(lowerCamelCase__ )
							_UpperCAmelCase		:       Union[str, Any]								=      self.get_successors(lowerCamelCase__ )

							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(lowerCamelCase__ )
									else:
											# retrieve the best current path
											_UpperCAmelCase		:       Dict								=      self.open_nodes.pop(self.open_nodes.index(lowerCamelCase__ ) )

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

					return [self.start.pos]




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Node )   ->list[Node]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      []
					for action in delta:
							_UpperCAmelCase		:       List[str]								=      parent.pos_x + action[1]
							_UpperCAmelCase		:       List[str]								=      parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCamelCase__ ) - 1):
									continue

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

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






			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Node | None )   ->list[TPosition]:



					'''simple docstring'''







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






class 	lowerCAmelCase__			:




			def __init__(		self  :     Dict	,  lowerCamelCase__  :     TPosition	,  lowerCamelCase__  :     TPosition )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      AStar(lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      AStar(lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      False




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->list[TPosition]:



					'''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		:       str								=      self.fwd_astar.open_nodes.pop(0 )
							_UpperCAmelCase		:       Tuple								=      self.bwd_astar.open_nodes.pop(0 )

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

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

							_UpperCAmelCase		:       int								=      current_bwd_node
							_UpperCAmelCase		:       Optional[int]								=      current_fwd_node

							_UpperCAmelCase		:       Optional[Any]								=      {
							    self.fwd_astar: self.fwd_astar.get_successors(lowerCamelCase__ ),
							    self.bwd_astar: self.bwd_astar.get_successors(lowerCamelCase__ ),
							}

							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(lowerCamelCase__ )
											else:
													# retrieve the best current path
													_UpperCAmelCase		:       List[str]								=      astar.open_nodes.pop(
													    astar.open_nodes.index(lowerCamelCase__ ) )

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

					return [self.fwd_astar.start.pos]






			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Node	,  lowerCamelCase__  :     Node )   ->list[TPosition]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.fwd_astar.retrace_path(lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      self.bwd_astar.retrace_path(lowerCamelCase__ )
					bwd_path.pop()
					bwd_path.reverse()
					_UpperCAmelCase		:       Union[str, Any]								=      fwd_path + bwd_path
					return path


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

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

						lowerCamelCase__    	=     time.time()
						lowerCamelCase__    	=     BidirectionalAStar(init, goal)
						lowerCamelCase__    	=     time.time() - bd_start_time
						print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')







'''simple docstring'''


import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowerCamelCase__    	=     {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

lowerCamelCase__    	=     {
    '169M': 768,
    '430M': 1_024,
    '1B5': 2_048,
    '3B': 2_560,
    '7B': 4_096,
    '14B': 5_120,
}






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       List[str]								=      list(state_dict.keys()   )
		for name in state_dict_keys:
				_UpperCAmelCase		:       Optional[int]								=      state_dict.pop(__lowerCAmelCase   )
				# emb -> embedding
				if name.startswith("emb."   ):
						_UpperCAmelCase		:       Tuple								=      name.replace("emb."      , "embeddings."   )
				# ln_0 -> pre_ln (only present at block 0)
				if name.startswith("blocks.0.ln0"   ):
						_UpperCAmelCase		:       Optional[int]								=      name.replace("blocks.0.ln0"      , "blocks.0.pre_ln"   )
				# att -> attention
				_UpperCAmelCase		:       Union[str, Any]								=      re.sub(R"blocks\.(\d+)\.att"      , R"blocks.\1.attention"      , __lowerCAmelCase   )
				# ffn -> feed_forward
				_UpperCAmelCase		:       Dict								=      re.sub(R"blocks\.(\d+)\.ffn"      , R"blocks.\1.feed_forward"      , __lowerCAmelCase   )
				# time_mix_k -> time_mix_key and reshape
				if name.endswith(".time_mix_k"   ):
						_UpperCAmelCase		:       int								=      name.replace(".time_mix_k"      , ".time_mix_key"   )
				# time_mix_v -> time_mix_value and reshape
				if name.endswith(".time_mix_v"   ):
						_UpperCAmelCase		:       Union[str, Any]								=      name.replace(".time_mix_v"      , ".time_mix_value"   )
				# time_mix_r -> time_mix_key and reshape
				if name.endswith(".time_mix_r"   ):
						_UpperCAmelCase		:       int								=      name.replace(".time_mix_r"      , ".time_mix_receptance"   )

				if name != "head.weight":
						_UpperCAmelCase		:       List[str]								=      "rwkv." + name

				_UpperCAmelCase		:       Optional[Any]								=      weight
		return state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase=False      , __lowerCAmelCase=None   ):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
				_UpperCAmelCase		:       str								=      50_277
				_UpperCAmelCase		:       Optional[Any]								=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
		else:
				_UpperCAmelCase		:       Tuple								=      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      len(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )

		# 2. Build the config
		_UpperCAmelCase		:       Optional[int]								=      list(NUM_HIDDEN_LAYERS_MAPPING.keys()   )
		if size is None:
				# Try to infer size from the checkpoint name
				for candidate in possible_sizes:
						if candidate in checkpoint_file:
								_UpperCAmelCase		:       Optional[Any]								=      candidate
								break
				if size is None:
						raise ValueError("Could not infer the size, please provide it with the `--size` argument."   )
		if size not in possible_sizes:
				raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""   )

		_UpperCAmelCase		:       Any								=      RwkvConfig(
		    vocab_size=__lowerCAmelCase      , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]      , hidden_size=HIDEN_SIZE_MAPPING[size]      , )
		config.save_pretrained(__lowerCAmelCase   )

		# 3. Download model file then convert state_dict
		_UpperCAmelCase		:       str								=      hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Any								=      convert_state_dict(__lowerCAmelCase   )

		# 4. Split in shards and save
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      shard_checkpoint(__lowerCAmelCase   )
		for shard_file, shard in shards.items():
				torch.save(__lowerCAmelCase      , os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )

		if index is not None:
				_UpperCAmelCase		:       int								=      os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )
				# Save the index as well
				with open(__lowerCAmelCase      , "w"      , encoding="utf-8"   ) as f:
						_UpperCAmelCase		:       int								=      json.dumps(__lowerCAmelCase      , indent=2      , sort_keys=__lowerCAmelCase   ) + "\n"
						f.write(__lowerCAmelCase   )

				# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
				print(
				    "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."   )
				_UpperCAmelCase		:       Union[str, Any]								=      list(shards.keys()   )

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						_UpperCAmelCase		:       Union[str, Any]								=      torch.load(os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )
						torch.save({k: v.cpu().clone() for k, v in state_dict.items()}      , os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )

		del state_dict
		gc.collect()

		if push_to_hub:
				if model_name is None:
						raise ValueError("Please provide a `model_name` to push the model to the Hub."   )
				_UpperCAmelCase		:       Optional[Any]								=      AutoModelForCausalLM.from_pretrained(__lowerCAmelCase   )
				model.push_to_hub(__lowerCAmelCase      , max_shard_size="2GB"   )
				tokenizer.push_to_hub(__lowerCAmelCase   )


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
						)
						parser.add_argument(
						    '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
						)
						parser.add_argument(
						    '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
						)
						parser.add_argument(
						    '--tokenizer_file',
						    default=None,
						    type=str,
						    help='Path to the tokenizer file to use (if not provided, only the model is converted).',
						)
						parser.add_argument(
						    '--size',
						    default=None,
						    type=str,
						    help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Push to the Hub the converted model.',
						)
						parser.add_argument(
						    '--model_name',
						    default=None,
						    type=str,
						    help='Name of the pushed model on the Hub, including the username / organization.',
						)

						lowerCamelCase__    	=     parser.parse_args()
						convert_rmkv_checkpoint_to_hf_format(
						    args.repo_id,
						    args.checkpoint_file,
						    args.output_dir,
						    size=args.size,
						    tokenizer_file=args.tokenizer_file,
						    push_to_hub=args.push_to_hub,
						    model_name=args.model_name,
						)



