'''simple docstring'''

from __future__ import annotations

import time

lowercase_     		= list[tuple[int, int]]

lowercase_     		= [
    [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],
]

lowercase_     		= [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=pos_x
								_lowerCAmelCase										=pos_y
								_lowerCAmelCase										=(pos_y, pos_x)
								_lowerCAmelCase										=goal_x
								_lowerCAmelCase										=goal_y
								_lowerCAmelCase										=parent

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[int]:
								_lowerCAmelCase										=Node(start[1]		,						start[0]		,						goal[1]		,						goal[0]		,						_A				)
								_lowerCAmelCase										=Node(goal[1]		,						goal[0]		,						goal[1]		,						goal[0]		,						_A				)

								_lowerCAmelCase										=[self.start]
								_lowerCAmelCase										=False






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								while self.node_queue:
														_lowerCAmelCase										=self.node_queue.pop(0				)

														if current_node.pos == self.target.pos:
																				_lowerCAmelCase										=True
																				return self.retrace_path(_A				)

														_lowerCAmelCase										=self.get_successors(_A				)

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

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






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								_lowerCAmelCase										=[]
								for action in delta:
														_lowerCAmelCase										=parent.pos_x + action[1]
														_lowerCAmelCase										=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		,						_A				)				)
								return successors






		def 		UpperCamelCase__       (   self		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										=node
								_lowerCAmelCase										=[]
								while current_node is not None:
														path.append((current_node.pos_y, current_node.pos_x)				)
														_lowerCAmelCase										=current_node.parent
								path.reverse()
								return path

class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										=BreadthFirstSearch(_A		,						_A				)
								_lowerCAmelCase										=BreadthFirstSearch(_A		,						_A				)
								_lowerCAmelCase										=False






		def 		UpperCamelCase__       (   self				) ->     str:
								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
														_lowerCAmelCase										=self.fwd_bfs.node_queue.pop(0				)
														_lowerCAmelCase										=self.bwd_bfs.node_queue.pop(0				)

														if current_bwd_node.pos == current_fwd_node.pos:
																				_lowerCAmelCase										=True
																				return self.retrace_bidirectional_path(
																				    _A		,						_A				)

														_lowerCAmelCase										=current_bwd_node
														_lowerCAmelCase										=current_fwd_node

														_lowerCAmelCase										={
														    self.fwd_bfs: self.fwd_bfs.get_successors(_A				),
														    self.bwd_bfs: self.bwd_bfs.get_successors(_A				),
														}

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Dict:
								_lowerCAmelCase										=self.fwd_bfs.retrace_path(_A				)
								_lowerCAmelCase										=self.bwd_bfs.retrace_path(_A				)
								bwd_path.pop()
								bwd_path.reverse()
								_lowerCAmelCase										=fwd_path + bwd_path
								return path


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

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

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

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

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

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






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).read()      )
						if not params:
												raise ValueError(
												    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
						if not args.output.endswith('.pt'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).astype(np.floataa      )
																		if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
																								continue
																		if key_name.startswith('pasts/'      ):
																								if key_name.startswith('pasts/mlp'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser(
							    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
							parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





