"""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


UpperCAmelCase       =  """base_with_context"""






def      lowercase		(						a__	:		str ,  a__	:		int     )						->   Union[str, Any]:
					_UpperCamelCase      						=	nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']     )     )
					_UpperCamelCase      						=	nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']     ) ,  requires_grad=_lowercase     )
					for lyr_num, lyr in enumerate(model.encoders     ):
										_UpperCamelCase      						=	weights[F'''layers_{lyr_num}''']
										_UpperCamelCase      						=	nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']     )     )

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

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

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

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






def      lowercase		(						a__	:		str ,  a__	:		Dict     )						->   Any:
					_UpperCamelCase      						=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T     )     )

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

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

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

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

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

					return model






def      lowercase		(						a__	:		Dict ,  a__	:		Tuple     )						->   List[str]:
					_UpperCamelCase      						=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T     )     )
					_UpperCamelCase      						=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T     )     )

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

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

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

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

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

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

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

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

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

					return model






def      lowercase		(						a__	:		Tuple     )						->   List[str]:
					_UpperCamelCase      						=	checkpoints.load_tax_checkpoint(args.checkpoint_path     )
					_UpperCamelCase      						=	jnp.tree_util.tree_map(onp.array ,  _lowercase     )

					_UpperCamelCase      						=	[
					    """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()""",
					]

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

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

					_UpperCamelCase      						=	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''' ,  )

					_UpperCamelCase      						=	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''' ,  )

					_UpperCamelCase      						=	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 ,  )

					_UpperCamelCase      						=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] ,  _lowercase     )
					_UpperCamelCase      						=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] ,  _lowercase     )
					_UpperCamelCase      						=	load_decoder(ta_checkpoint['''target''']['''decoder'''] ,  _lowercase     )

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

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


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

		main(args)





'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

