
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


lowerCamelCase__          =		"""base_with_context"""
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']     )     )
			lowerCAmelCase__      :      str         =		nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )       ,							requires_grad=SCREAMING_SNAKE_CASE_     )
			for lyr_num, lyr in enumerate(model.encoders     ):
						lowerCAmelCase__      :      Tuple         =		weights[F'''layers_{lyr_num}''']
						lowerCAmelCase__      :      Tuple         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']     )     )

						lowerCAmelCase__      :      List[str]         =		ly_weight['attention']
						lowerCAmelCase__      :      Optional[Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						lowerCAmelCase__      :      Dict         =		nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )

						lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )

						lowerCAmelCase__      :      List[Any]         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T     )     )
						lowerCAmelCase__      :      List[Any]         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T     )     )
						lowerCAmelCase__      :      Tuple         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )

			lowerCAmelCase__      :      List[Any]         =		nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']     )     )
			return model
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			lowerCAmelCase__      :      Dict         =		nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T     )     )

			lowerCAmelCase__      :      str         =		nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )       ,							requires_grad=SCREAMING_SNAKE_CASE_     )

			for lyr_num, lyr in enumerate(model.encoders     ):
						lowerCAmelCase__      :      Dict         =		weights[F'''layers_{lyr_num}''']
						lowerCAmelCase__      :      Dict         =		ly_weight['attention']

						lowerCAmelCase__      :      Tuple         =		nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						lowerCAmelCase__      :      Any         =		nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )
						lowerCAmelCase__      :      List[str]         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']     )     )

						lowerCAmelCase__      :      Tuple         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T     )     )
						lowerCAmelCase__      :      int         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T     )     )
						lowerCAmelCase__      :      int         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )
						lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )

			lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']     )     )

			return model
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T     )     )
			lowerCAmelCase__      :      Tuple         =		nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T     )     )

			lowerCAmelCase__      :      Dict         =		nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )       ,							requires_grad=SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T     )     )

			for lyr_num, lyr in enumerate(model.decoders     ):
						lowerCAmelCase__      :      Union[str, Any]         =		weights[F'''layers_{lyr_num}''']
						lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']     )     )

						lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T     )     )

						lowerCAmelCase__      :      int         =		ly_weight['self_attention']
						lowerCAmelCase__      :      List[Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						lowerCAmelCase__      :      int         =		nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						lowerCAmelCase__      :      List[str]         =		nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						lowerCAmelCase__      :      List[Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )

						lowerCAmelCase__      :      str         =		ly_weight['MultiHeadDotProductAttention_0']
						lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						lowerCAmelCase__      :      Dict         =		nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )
						lowerCAmelCase__      :      int         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']     )     )

						lowerCAmelCase__      :      Any         =		nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )
						lowerCAmelCase__      :      Any         =		nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T     )     )
						lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T     )     )
						lowerCAmelCase__      :      Dict         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T     )     )
						lowerCAmelCase__      :      str         =		nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )

			lowerCAmelCase__      :      Union[str, Any]         =		nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']     )     )

			lowerCAmelCase__      :      Optional[int]         =		nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T     )     )

			return model
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      int         =		checkpoints.load_tax_checkpoint(args.checkpoint_path     )
			lowerCAmelCase__      :      Tuple         =		jnp.tree_util.tree_map(onp.array       ,							SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      int         =		[
			    '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()',
			]

			lowerCAmelCase__      :      Optional[int]         =		os.path.join(args.checkpoint_path       ,							'..'       ,							'config.gin'     )
			lowerCAmelCase__      :      Tuple         =		inference.parse_training_gin_file(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		inference.InferenceModel(args.checkpoint_path       ,							SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      Dict         =		DDPMScheduler(beta_schedule='squaredcos_cap_v2'       ,							variance_type='fixed_large'     )

			lowerCAmelCase__      :      Optional[Any]         =		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'       ,							)

			lowerCAmelCase__      :      Tuple         =		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'       ,							)

			lowerCAmelCase__      :      int         =		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       ,							)

			lowerCAmelCase__      :      Any         =		load_notes_encoder(ta_checkpoint['target']['token_encoder']       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		load_decoder(ta_checkpoint['target']['decoder']       ,							SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      int         =		OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'     )

			lowerCAmelCase__      :      int         =		SpectrogramDiffusionPipeline(
			    notes_encoder=SCREAMING_SNAKE_CASE_       ,							continuous_encoder=SCREAMING_SNAKE_CASE_       ,							decoder=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							melgan=SCREAMING_SNAKE_CASE_       ,							)
			if args.save:
						pipe.save_pretrained(args.output_path     )


if __name__ == "__main__":
					lowerCamelCase__          =		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.""",
					)
					lowerCamelCase__          =		parser.parse_args()

					main(args)

from __future__ import annotations

lowerCamelCase__          =		list[list[int]]

# assigning initial values to the grid
lowerCamelCase__          =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__          =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		bool:
			for i in range(9     ):
						if grid[row][i] == n or grid[i][column] == n:
									return False

			for i in range(3     ):
						for j in range(3     ):
									if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
												return False

			return True
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		tuple[int, int] | None:
			for i in range(9     ):
						for j in range(9     ):
									if grid[i][j] == 0:
												return i, j
			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Matrix | None:
			if location := find_empty_location(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      ,				lowerCAmelCase__      :      Any         =		location
			else:
						# If the location is ``None``, then the grid is solved.
						return grid

			for digit in range(1       ,							10     ):
						if is_safe(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
									lowerCAmelCase__      :      Optional[Any]         =		digit

									if sudoku(SCREAMING_SNAKE_CASE_     ) is not None:
												return grid

									lowerCAmelCase__      :      List[Any]         =		0

			return None
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			for row in grid:
						for cell in row:
									print(SCREAMING_SNAKE_CASE_       ,							end=' '     )
						print()


if __name__ == "__main__":
					# make a copy of grid so that you can compare with the unmodified grid
					for example_grid in (initial_grid, no_solution):
										print("""\nExample grid:\n""" + """=""" * 20)
										print_solution(example_grid)
										print("""\nExample grid solution:""")
										lowerCamelCase__          =		sudoku(example_grid)
										if solution is not None:
															print_solution(solution)
										else:
															print("""Cannot find a solution.""")