





'''simple docstring'''



import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


_SCREAMING_SNAKE_CASE					=    '''base_with_context'''




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']				)				)
			__lowercase										=							nn.Parameter(
			    torch.FloatTensor(weights['''Embed_0''']['''embedding''']				)		,       requires_grad=lowerCamelCase_				)
			for lyr_num, lyr in enumerate(model.encoders				):
						__lowercase										=							weights[f"layers_{lyr_num}"]
						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']				)				)

						__lowercase										=							ly_weight['''attention''']
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T				)				)

						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']				)				)

						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T				)				)

			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']				)				)
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple				):
			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T				)				)

			__lowercase										=							nn.Parameter(
			    torch.FloatTensor(weights['''Embed_0''']['''embedding''']				)		,       requires_grad=lowerCamelCase_				)

			for lyr_num, lyr in enumerate(model.encoders				):
						__lowercase										=							weights[f"layers_{lyr_num}"]
						__lowercase										=							ly_weight['''attention''']

						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']				)				)

						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']				)				)

			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']				)				)

			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Union[str, Any]				):
			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T				)				)
			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T				)				)

			__lowercase										=							nn.Parameter(
			    torch.FloatTensor(weights['''Embed_0''']['''embedding''']				)		,       requires_grad=lowerCamelCase_				)

			__lowercase										=							nn.Parameter(
			    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T				)				)

			for lyr_num, lyr in enumerate(model.decoders				):
						__lowercase										=							weights[f"layers_{lyr_num}"]
						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']				)				)

						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T				)				)

						__lowercase										=							ly_weight['''self_attention''']
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T				)				)

						__lowercase										=							ly_weight['''MultiHeadDotProductAttention_0''']
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']				)				)

						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']				)				)
						__lowercase										=							nn.Parameter(
						    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T				)				)
						__lowercase										=							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T				)				)

			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']				)				)

			__lowercase										=							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T				)				)

			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							checkpoints.load_tax_checkpoint(args.checkpoint_path				)
			__lowercase										=							jnp.tree_util.tree_map(onp.array		,       lowerCamelCase_				)

			__lowercase										=							[
			    '''from __gin__ import dynamic_registration''',
			    '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
			    '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
			    '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
			]

			__lowercase										=							os.path.join(args.checkpoint_path		,       '''..'''		,       '''config.gin'''				)
			__lowercase										=							inference.parse_training_gin_file(lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							inference.InferenceModel(args.checkpoint_path		,       lowerCamelCase_				)

			__lowercase										=							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		,       variance_type='''fixed_large'''				)

			__lowercase										=							SpectrogramNotesEncoder(
			    max_length=synth_model.sequence_length['''inputs''']		,       vocab_size=synth_model.model.module.config.vocab_size		,       d_model=synth_model.model.module.config.emb_dim		,       dropout_rate=synth_model.model.module.config.dropout_rate		,       num_layers=synth_model.model.module.config.num_encoder_layers		,       num_heads=synth_model.model.module.config.num_heads		,       d_kv=synth_model.model.module.config.head_dim		,       d_ff=synth_model.model.module.config.mlp_dim		,       feed_forward_proj='''gated-gelu'''		,       )

			__lowercase										=							SpectrogramContEncoder(
			    input_dims=synth_model.audio_codec.n_dims		,       targets_context_length=synth_model.sequence_length['''targets_context''']		,       d_model=synth_model.model.module.config.emb_dim		,       dropout_rate=synth_model.model.module.config.dropout_rate		,       num_layers=synth_model.model.module.config.num_encoder_layers		,       num_heads=synth_model.model.module.config.num_heads		,       d_kv=synth_model.model.module.config.head_dim		,       d_ff=synth_model.model.module.config.mlp_dim		,       feed_forward_proj='''gated-gelu'''		,       )

			__lowercase										=							TaFilmDecoder(
			    input_dims=synth_model.audio_codec.n_dims		,       targets_length=synth_model.sequence_length['''targets_context''']		,       max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time		,       d_model=synth_model.model.module.config.emb_dim		,       num_layers=synth_model.model.module.config.num_decoder_layers		,       num_heads=synth_model.model.module.config.num_heads		,       d_kv=synth_model.model.module.config.head_dim		,       d_ff=synth_model.model.module.config.mlp_dim		,       dropout_rate=synth_model.model.module.config.dropout_rate		,       )

			__lowercase										=							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']		,       lowerCamelCase_				)
			__lowercase										=							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']		,       lowerCamelCase_				)
			__lowercase										=							load_decoder(ta_checkpoint['''target''']['''decoder''']		,       lowerCamelCase_				)

			__lowercase										=							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''				)

			__lowercase										=							SpectrogramDiffusionPipeline(
			    notes_encoder=lowerCamelCase_		,       continuous_encoder=lowerCamelCase_		,       decoder=lowerCamelCase_		,       scheduler=lowerCamelCase_		,       melgan=lowerCamelCase_		,       )
			if args.save:
						pipe.save_pretrained(args.output_path				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()

	parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
	parser.add_argument(
	    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
	)
	parser.add_argument(
	    '''--checkpoint_path''',
	    default=f'''{MODEL}/checkpoint_500000''',
	    type=str,
	    required=False,
	    help='''Path to the original jax model checkpoint.''',
	)
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()

	main(args)












'''simple docstring'''



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE					=    0

_SCREAMING_SNAKE_CASE					=    [
    [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],
]

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

_SCREAMING_SNAKE_CASE					=    tuple[int, int]






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							pos_x
				__lowercase										=							pos_y
				__lowercase										=							(pos_y, pos_x)
				__lowercase										=							goal_x
				__lowercase										=							goal_y
				__lowercase										=							g_cost
				__lowercase										=							parent
				__lowercase										=							self.calculate_heuristic()
				__lowercase										=							self.g_cost + self.h_cost


	def 						_UpperCAmelCase     (self )							->							float:







				'''simple docstring'''





				__lowercase										=							self.pos_x - self.goal_x
				__lowercase										=							self.pos_y - self.goal_y
				if HEURISTIC == 1:
							return abs(_lowerCamelCase ) + abs(_lowerCamelCase )
				else:
							return sqrt(dy**2 + dx**2 )


	def __lt__(self     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				return self.f_cost < other.f_cost






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							Node(start[1]     ,start[0]     ,goal[1]     ,goal[0]     ,0     ,_lowerCamelCase )
				__lowercase										=							Node(goal[1]     ,goal[0]     ,goal[1]     ,goal[0]     ,99999     ,_lowerCamelCase )

				__lowercase										=							[self.start]
				__lowercase										=							[]

				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





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

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

							self.closed_nodes.append(_lowerCamelCase )
							__lowercase										=							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
													__lowercase										=							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 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[Node]:







				'''simple docstring'''





				__lowercase										=							[]
				for action in delta:
							__lowercase										=							parent.pos_x + action[1]
							__lowercase										=							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 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





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






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							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()
							__lowercase										=							self.fwd_astar.open_nodes.pop(0 )
							__lowercase										=							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 )

							__lowercase										=							current_bwd_node
							__lowercase										=							current_fwd_node

							__lowercase										=							{
							    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
																__lowercase										=							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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							self.fwd_astar.retrace_path(_lowerCamelCase )
				__lowercase										=							self.bwd_astar.retrace_path(_lowerCamelCase )
				bwd_path.pop()
				bwd_path.reverse()
				__lowercase										=							fwd_path + bwd_path
				return path


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

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

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





