
"""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


_a														=		"""base_with_context"""



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					_UpperCamelCase							 =  nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']	)	)
					_UpperCamelCase							 =  nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),     requires_grad=__snake_case	)
					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       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[Any]:


					"""simple docstring"""





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

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

					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       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[int]:


					"""simple docstring"""





					_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=__snake_case	)

					_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       lowerCamelCase__		(			__snake_case	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  checkpoints.load_tax_checkpoint(args.checkpoint_path	)
					_UpperCamelCase							 =  jnp.tree_util.tree_map(onp.array,     __snake_case	)

					_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(__snake_case,     __snake_case	)
					_UpperCamelCase							 =  inference.InferenceModel(args.checkpoint_path,     __snake_case	)

					_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'''],     __snake_case	)
					_UpperCamelCase							 =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],     __snake_case	)
					_UpperCamelCase							 =  load_decoder(ta_checkpoint['''target''']['''decoder'''],     __snake_case	)

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

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


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

				main(args)








"""simple docstring"""





import torch
from torch import nn






class     _UpperCAmelCase(    nn.Module	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a=1				,		__a=False)       ->       Tuple:






							'''simple docstring'''
							super().__init__()

							_UpperCamelCase							 =  n_token
							_UpperCamelCase							 =  d_embed
							_UpperCamelCase							 =  d_proj

							_UpperCamelCase							 =  cutoffs + [n_token]
							_UpperCamelCase							 =  [0] + self.cutoffs
							_UpperCamelCase							 =  div_val

							_UpperCamelCase							 =  self.cutoffs[0]
							_UpperCamelCase							 =  len(self.cutoffs) - 1
							_UpperCamelCase							 =  self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												_UpperCamelCase							 =  nn.Parameter(torch.zeros(self.n_clusters				,		self.d_embed))
												_UpperCamelCase							 =  nn.Parameter(torch.zeros(self.n_clusters))

							_UpperCamelCase							 =  nn.ModuleList()
							_UpperCamelCase							 =  nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs)):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(__a				,		__a)))
																	else:
																						self.out_projs.append(__a)

												self.out_layers.append(nn.Linear(__a				,		__a))
							else:
												for i in range(len(self.cutoffs)):
																	_UpperCamelCase ,  _UpperCamelCase							 =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	_UpperCamelCase							 =  d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(__a				,		__a)))

																	self.out_layers.append(nn.Linear(__a				,		r_idx - l_idx))

							_UpperCamelCase							 =  keep_order



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       Any:






							'''simple docstring'''
							if proj is None:
												_UpperCamelCase							 =  nn.functional.linear(__a				,		__a				,		bias=__a)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												_UpperCamelCase							 =  nn.functional.linear(__a				,		proj.t().contiguous())
												_UpperCamelCase							 =  nn.functional.linear(__a				,		__a				,		bias=__a)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit



		def 						UpperCAmelCase							(					self				,		__a				,		__a=None				,		__a=False)       ->       Dict:






							'''simple docstring'''

							if labels is not None:
												# Shift so that tokens < n predict n
												_UpperCamelCase							 =  hidden[..., :-1, :].contiguous()
												_UpperCamelCase							 =  labels[..., 1:].contiguous()
												_UpperCamelCase							 =  hidden.view(-1				,		hidden.size(-1))
												_UpperCamelCase							 =  labels.view(-1)
												if hidden.size(0) != labels.size(0):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.''')
							else:
												_UpperCamelCase							 =  hidden.view(-1				,		hidden.size(-1))

							if self.n_clusters == 0:
												_UpperCamelCase							 =  self._compute_logit(__a				,		self.out_layers[0].weight				,		self.out_layers[0].bias				,		self.out_projs[0])
												if labels is not None:
																	_UpperCamelCase							 =  labels != -1_00
																	_UpperCamelCase							 =  torch.zeros_like(__a				,		dtype=hidden.dtype				,		device=hidden.device)
																	_UpperCamelCase							 =  (
																	    -nn.functional.log_softmax(__a				,		dim=-1)[mask].gather(1				,		labels[mask].unsqueeze(1)).squeeze(1)
																	)
												else:
																	_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=-1)
							else:
												# construct weights and biases
												_UpperCamelCase ,  _UpperCamelCase							 =  [], []
												for i in range(len(self.cutoffs)):
																	if self.div_val == 1:
																						_UpperCamelCase ,  _UpperCamelCase							 =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						_UpperCamelCase							 =  self.out_layers[0].weight[l_idx:r_idx]
																						_UpperCamelCase							 =  self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						_UpperCamelCase							 =  self.out_layers[i].weight
																						_UpperCamelCase							 =  self.out_layers[i].bias

																	if i == 0:
																						_UpperCamelCase							 =  torch.cat([weight_i, self.cluster_weight]				,		dim=0)
																						_UpperCamelCase							 =  torch.cat([bias_i, self.cluster_bias]				,		dim=0)

																	weights.append(__a)
																	biases.append(__a)

												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[0], biases[0], self.out_projs[0]

												_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)
												_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

												if labels is None:
																	_UpperCamelCase							 =  hidden.new_empty((head_logit.size(0), self.n_token))
												else:
																	_UpperCamelCase							 =  torch.zeros_like(__a				,		dtype=hidden.dtype				,		device=hidden.device)

												_UpperCamelCase							 =  0
												_UpperCamelCase							 =  [0] + self.cutoffs
												for i in range(len(__a) - 1):
																	_UpperCamelCase ,  _UpperCamelCase							 =  cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						_UpperCamelCase							 =  (labels >= l_idx) & (labels < r_idx)
																						_UpperCamelCase							 =  mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						_UpperCamelCase							 =  labels.index_select(0				,		__a) - l_idx
																						_UpperCamelCase							 =  head_logprob.index_select(0				,		__a)
																						_UpperCamelCase							 =  hidden.index_select(0				,		__a)
																	else:
																						_UpperCamelCase							 =  hidden

																	if i == 0:
																						if labels is not None:
																											_UpperCamelCase							 =  head_logprob_i.gather(1				,		target_i[:, None]).squeeze(1)
																						else:
																											_UpperCamelCase							 =  head_logprob[:, : self.cutoffs[0]]
																	else:
																						_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[i], biases[i], self.out_projs[i]

																						_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)
																						_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)
																						_UpperCamelCase							 =  self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											_UpperCamelCase							 =  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1				,		target_i[:, None]).squeeze(1)
																						else:
																											_UpperCamelCase							 =  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											_UpperCamelCase							 =  logprob_i

																	if labels is not None:
																						if (hasattr(self				,		'''keep_order''') and self.keep_order) or keep_order:
																											out.index_copy_(0				,		__a				,		-logprob_i)
																						else:
																											out[offset : offset + logprob_i.size(0)].copy_(-logprob_i)
																						offset += logprob_i.size(0)

							return out




		def 						UpperCAmelCase							(					self				,		__a)       ->       List[str]:






							'''simple docstring'''
							if self.n_clusters == 0:
												_UpperCamelCase							 =  self._compute_logit(__a				,		self.out_layers[0].weight				,		self.out_layers[0].bias				,		self.out_projs[0])
												return nn.functional.log_softmax(__a				,		dim=-1)
							else:
												# construct weights and biases
												_UpperCamelCase ,  _UpperCamelCase							 =  [], []
												for i in range(len(self.cutoffs)):
																	if self.div_val == 1:
																						_UpperCamelCase ,  _UpperCamelCase							 =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						_UpperCamelCase							 =  self.out_layers[0].weight[l_idx:r_idx]
																						_UpperCamelCase							 =  self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						_UpperCamelCase							 =  self.out_layers[i].weight
																						_UpperCamelCase							 =  self.out_layers[i].bias

																	if i == 0:
																						_UpperCamelCase							 =  torch.cat([weight_i, self.cluster_weight]				,		dim=0)
																						_UpperCamelCase							 =  torch.cat([bias_i, self.cluster_bias]				,		dim=0)

																	weights.append(__a)
																	biases.append(__a)

												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[0], biases[0], self.out_projs[0]
												_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)

												_UpperCamelCase							 =  hidden.new_empty((head_logit.size(0), self.n_token))
												_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

												_UpperCamelCase							 =  [0] + self.cutoffs
												for i in range(len(__a) - 1):
																	_UpperCamelCase ,  _UpperCamelCase							 =  cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						_UpperCamelCase							 =  head_logprob[:, : self.cutoffs[0]]
																	else:
																						_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  weights[i], biases[i], self.out_projs[i]

																						_UpperCamelCase							 =  self._compute_logit(__a				,		__a				,		__a				,		__a)
																						_UpperCamelCase							 =  nn.functional.log_softmax(__a				,		dim=1)

																						_UpperCamelCase							 =  head_logprob[:, -i] + tail_logprob_i
																						_UpperCamelCase							 =  logprob_i

												return out






