






from __future__ import annotations

import time

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    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Dict			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







											snake_case__	  =       pos_x
											snake_case__	  =       pos_y
											snake_case__	  =       (pos_y, pos_x)
											snake_case__	  =       goal_x
											snake_case__	  =       goal_y
											snake_case__	  =       parent


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    tuple[int, int]			,		UpperCamelCase__    :    tuple[int, int]):






											'''simple docstring'''







											snake_case__	  =       Node(start[1]			,		start[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)
											snake_case__	  =       Node(goal[1]			,		goal[0]			,		goal[1]			,		goal[0]			,		UpperCamelCase__)

											snake_case__	  =       [self.start]
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											while self.node_queue:
																		snake_case__	  =       self.node_queue.pop(0)

																		if current_node.pos == self.target.pos:
																									snake_case__	  =       True
																									return self.retrace_path(UpperCamelCase__)

																		snake_case__	  =       self.get_successors(UpperCamelCase__)

																		for node in successors:
																									self.node_queue.append(UpperCamelCase__)

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



				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







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

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

																		successors.append(
																		    Node(UpperCamelCase__			,		UpperCamelCase__			,		self.target.pos_y			,		self.target.pos_x			,		UpperCamelCase__))
											return successors



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Node | None):






											'''simple docstring'''







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


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    str			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       BreadthFirstSearch(UpperCamelCase__			,		UpperCamelCase__)
											snake_case__	  =       False



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
																		snake_case__	  =       self.fwd_bfs.node_queue.pop(0)
																		snake_case__	  =       self.bwd_bfs.node_queue.pop(0)

																		if current_bwd_node.pos == current_fwd_node.pos:
																									snake_case__	  =       True
																									return self.retrace_bidirectional_path(
																									    UpperCamelCase__			,		UpperCamelCase__)

																		snake_case__	  =       current_bwd_node
																		snake_case__	  =       current_fwd_node

																		snake_case__	  =       {
																		    self.fwd_bfs: self.fwd_bfs.get_successors(UpperCamelCase__),
																		    self.bwd_bfs: self.bwd_bfs.get_successors(UpperCamelCase__),
																		}

																		for bfs in [self.fwd_bfs, self.bwd_bfs]:
																									for node in successors[bfs]:
																																bfs.node_queue.append(UpperCamelCase__)

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



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Node			,		UpperCamelCase__    :    Node):






											'''simple docstring'''







											snake_case__	  =       self.fwd_bfs.retrace_path(UpperCamelCase__)
											snake_case__	  =       self.bwd_bfs.retrace_path(UpperCamelCase__)
											bwd_path.pop()
											bwd_path.reverse()
											snake_case__	  =       fwd_path + bwd_path
											return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	import doctest

	doctest.testmod()
	a__       =     (0, 0)
	a__       =     (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	a__       =     time.time()
	a__       =     BreadthFirstSearch(init, goal)
	a__       =     bfs.search()
	a__       =     time.time() - start_bfs_time

	print("""Unidirectional BFS computation time : """, bfs_time)

	a__       =     time.time()
	a__       =     BidirectionalBreadthFirstSearch(init, goal)
	a__       =     bd_bfs.search()
	a__       =     time.time() - start_bd_bfs_time

	print("""Bidirectional BFS computation time : """, bd_bfs_time)








import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def _UpperCAmelCase   (	a						:	Optional[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any]=None ):
							# set parameter of one layer
							assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
							snake_case__	  =       nn.Parameter(a )
							if bias is not None:
														assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
														snake_case__	  =       nn.Parameter(a )

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	List[str]  ,					a						:	List[Any] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )

							set_param(
							    torch_layer.self_attention.query_key  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict  ,					a						:	Optional[int] ):
							# set torch weights for 1-to-1 comparison
							snake_case__	  =       np.asarray(weights[0] )
							snake_case__	  =       np.asarray(weights[1] )
							snake_case__	  =       np.asarray(weights[2] )
							snake_case__	  =       np.asarray(weights[3] )

							set_param(
							    torch_layer.self_attention.query  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.key  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.self_attention.value  ,					torch.tensor(a ).transpose(1  ,					2 ).contiguous().view(-1  ,					a )  ,					)
							set_param(
							    torch_layer.output.dense  ,					torch.tensor(a ).view(-1  ,					a ).contiguous().transpose(0  ,					1 )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Tuple  ,					a						:	List[Any] ):
							# layernorm 1
							snake_case__	  =       weights[0][0][0]
							snake_case__	  =       np.asarray(layer_norm_a[0] )
							snake_case__	  =       np.asarray(layer_norm_a[1] )
							set_param(
							    torch_block.attention.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# lsh weights + output
							snake_case__	  =       weights[0][1]
							if len(a ) < 4:
														set_layer_weights_in_torch_lsh(a  ,					torch_block.attention  ,					a )
							else:
														set_layer_weights_in_torch_local(a  ,					torch_block.attention  ,					a )

							# intermediate weighs
							snake_case__	  =       weights[2][0][1][2]

							# Chunked Feed Forward
							if len(a ) == 4:
														snake_case__	  =       intermediate_weights[2]

							# layernorm 2
							snake_case__	  =       np.asarray(intermediate_weights[0][0] )
							snake_case__	  =       np.asarray(intermediate_weights[0][1] )
							set_param(
							    torch_block.feed_forward.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# intermediate dense
							snake_case__	  =       np.asarray(intermediate_weights[1][0] )
							snake_case__	  =       np.asarray(intermediate_weights[1][1] )
							set_param(
							    torch_block.feed_forward.dense.dense  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

							# intermediate out
							snake_case__	  =       np.asarray(intermediate_weights[4][0] )
							snake_case__	  =       np.asarray(intermediate_weights[4][1] )
							set_param(
							    torch_block.feed_forward.output.dense  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Dict  ,					a						:	Union[str, Any] ):
							# reformer model
							snake_case__	  =       torch_model.reformer

							# word embeds
							snake_case__	  =       np.asarray(weights[1] )
							set_param(
							    torch_model_reformer.embeddings.word_embeddings  ,					torch.tensor(a )  ,					)

							if isinstance(weights[3]  ,					a ):
														snake_case__	  =       torch_model_reformer.embeddings.position_embeddings
														for emb_idx in range(len(position_embeddings.weights ) ):
																					snake_case__	  =       np.asarray(weights[3][emb_idx][0] )
																					assert (
																					    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																					), F'''{position_embeddings[emb_idx]} emb does not match'''
																					snake_case__	  =       nn.Parameter(torch.tensor(a ) )

							snake_case__	  =       weights[5]
							assert len(torch_model_reformer.encoder.layers ) * 4 == len(
							    a ), "HF and trax model do not have the same number of layers"
							for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
														snake_case__	  =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
														set_block_weights_in_torch(a  ,					a  ,					a )

							# output layer norm
							snake_case__	  =       np.asarray(weights[7][0] )
							snake_case__	  =       np.asarray(weights[7][1] )
							set_param(
							    torch_model_reformer.encoder.layer_norm  ,					torch.tensor(a )  ,					torch.tensor(a )  ,					)

							# output embeddings
							snake_case__	  =       np.asarray(weights[9][0] )
							snake_case__	  =       np.asarray(weights[9][1] )
							set_param(
							    torch_model.lm_head.decoder  ,					torch.tensor(a ).transpose(0  ,					1 ).contiguous()  ,					torch.tensor(a )  ,					)

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	str ):
							# Initialise PyTorch model
							snake_case__	  =       ReformerConfig.from_json_file(a )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case__	  =       ReformerModelWithLMHead(a )

							with open(a  ,					"""rb""" ) as f:
														snake_case__	  =       pickle.load(a )["""weights"""]

							set_model_weights_in_torch(a  ,					a  ,					config.hidden_size )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,					a )


if __name__ == "__main__":
	a__       =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained Reformer model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	a__       =     parser.parse_args()
	convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
