"""simple docstring"""






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def 						a_		(      lowerCamelCase      ,    lowerCamelCase=0.999      ,    lowerCamelCase="cosine"      ,    ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase ):
												return math.exp(t * -12.0 )

				else:
								raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

				UpperCAmelCase__    =							[]
				for i in range(lowerCamelCase ):
								UpperCAmelCase__    =							i / num_diffusion_timesteps
								UpperCAmelCase__    =							(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase ) / alpha_bar_fn(lowerCamelCase )      ,    lowerCamelCase ) )
				return torch.tensor(lowerCamelCase      ,    dtype=torch.floataa )







class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[e.name for e in KarrasDiffusionSchedulers]
					snake_case__       					=				2







					@register_to_config
					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			int = 1_000					,lowerCamelCase__     :			float = 0.0_0_0_8_5					,lowerCamelCase__     :			float = 0.0_1_2					,lowerCamelCase__     :			str = "linear"					,lowerCamelCase__     :			Optional[Union[np.ndarray, List[float]]] = None					,lowerCamelCase__     :			str = "epsilon"					,lowerCamelCase__     :			str = "linspace"					,lowerCamelCase__     :			int = 0					,):
									if trained_betas is not None:
													UpperCAmelCase__    =							torch.tensor(lowerCamelCase__					,dtype=torch.floataa    )
									elif beta_schedule == "linear":
													UpperCAmelCase__    =							torch.linspace(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,dtype=torch.floataa    )
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													UpperCAmelCase__    =							(
													    torch.linspace(beta_start**0.5					,beta_end**0.5					,lowerCamelCase__					,dtype=torch.floataa    ) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													UpperCAmelCase__    =							betas_for_alpha_bar(lowerCamelCase__    )
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

									UpperCAmelCase__    =							1.0 - self.betas
									UpperCAmelCase__    =							torch.cumprod(self.alphas					,dim=0    )

									#  set all values
									self.set_timesteps(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]=None    ):
									if schedule_timesteps is None:
													UpperCAmelCase__    =							self.timesteps

									UpperCAmelCase__    =							(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter    ) == 0:
													UpperCAmelCase__    =							1 if len(lowerCamelCase__    ) > 1 else 0
									else:
													UpperCAmelCase__    =							timestep.cpu().item() if torch.is_tensor(lowerCamelCase__    ) else timestep
													UpperCAmelCase__    =							self._index_counter[timestep_int]

									return indices[pos].item()







					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			torch.FloatTensor					,lowerCamelCase__     :			Union[float, torch.FloatTensor]					,):
									UpperCAmelCase__    =							self.index_for_timestep(lowerCamelCase__    )

									if self.state_in_first_order:
													UpperCAmelCase__    =							self.sigmas[step_index]
									else:
													UpperCAmelCase__    =							self.sigmas_interpol[step_index]

									UpperCAmelCase__    =							sample / ((sigma**2 + 1) ** 0.5)
									return sample







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Union[str, torch.device] = None					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							num_inference_steps

									UpperCAmelCase__    =							num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
													UpperCAmelCase__    =							np.linspace(0					,num_train_timesteps - 1					,lowerCamelCase__					,dtype=lowerCamelCase__    )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
													UpperCAmelCase__    =							num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													UpperCAmelCase__    =							(np.arange(0					,lowerCamelCase__    ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__    )
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													UpperCAmelCase__    =							num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													UpperCAmelCase__    =							(np.arange(lowerCamelCase__					,0					,-step_ratio    )).round().copy().astype(lowerCamelCase__    )
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''    )

									UpperCAmelCase__    =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
									UpperCAmelCase__    =							torch.from_numpy(np.log(lowerCamelCase__    )    ).to(lowerCamelCase__    )

									UpperCAmelCase__    =							np.interp(lowerCamelCase__					,np.arange(0					,len(lowerCamelCase__    )    )					,lowerCamelCase__    )
									UpperCAmelCase__    =							np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
									UpperCAmelCase__    =							torch.from_numpy(lowerCamelCase__    ).to(device=lowerCamelCase__    )

									# interpolate sigmas
									UpperCAmelCase__    =							sigmas.log().lerp(sigmas.roll(1    ).log()					,0.5    ).exp()

									UpperCAmelCase__    =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
									UpperCAmelCase__    =							torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

									if str(lowerCamelCase__    ).startswith('mps'    ):
													# mps does not support float64
													UpperCAmelCase__    =							torch.from_numpy(lowerCamelCase__    ).to(lowerCamelCase__					,dtype=torch.floataa    )
									else:
													UpperCAmelCase__    =							torch.from_numpy(lowerCamelCase__    ).to(lowerCamelCase__    )

									# interpolate timesteps
									UpperCAmelCase__    =							self.sigma_to_t(lowerCamelCase__    ).to(lowerCamelCase__					,dtype=timesteps.dtype    )
									UpperCAmelCase__    =							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					,dim=-1    ).flatten()

									UpperCAmelCase__    =							torch.cat([timesteps[:1], interleaved_timesteps]    )

									UpperCAmelCase__    =							None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									UpperCAmelCase__    =							defaultdict(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			Optional[Any]    ):
									# get log sigma
									UpperCAmelCase__    =							sigma.log()

									# get distribution
									UpperCAmelCase__    =							log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									UpperCAmelCase__    =							dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
									UpperCAmelCase__    =							low_idx + 1

									UpperCAmelCase__    =							self.log_sigmas[low_idx]
									UpperCAmelCase__    =							self.log_sigmas[high_idx]

									# interpolate sigmas
									UpperCAmelCase__    =							(low - log_sigma) / (low - high)
									UpperCAmelCase__    =							w.clamp(0					,1    )

									# transform interpolation to time range
									UpperCAmelCase__    =							(1 - w) * low_idx + w * high_idx
									UpperCAmelCase__    =							t.view(sigma.shape    )
									return t







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return self.sample is None







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase__     :			Union[float, torch.FloatTensor]					,lowerCamelCase__     :			Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase__     :			bool = True					,):
									UpperCAmelCase__    =							self.index_for_timestep(lowerCamelCase__    )

									# advance index counter by 1
									UpperCAmelCase__    =							timestep.cpu().item() if torch.is_tensor(lowerCamelCase__    ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
													UpperCAmelCase__    =							self.sigmas[step_index]
													UpperCAmelCase__    =							self.sigmas_interpol[step_index + 1]
													UpperCAmelCase__    =							self.sigmas[step_index + 1]
									else:
													# 2nd order / KDPM2's method
													UpperCAmelCase__    =							self.sigmas[step_index - 1]
													UpperCAmelCase__    =							self.sigmas_interpol[step_index]
													UpperCAmelCase__    =							self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									UpperCAmelCase__    =							0
									UpperCAmelCase__    =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
													UpperCAmelCase__    =							sigma_hat if self.state_in_first_order else sigma_interpol
													UpperCAmelCase__    =							sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
													UpperCAmelCase__    =							sigma_hat if self.state_in_first_order else sigma_interpol
													UpperCAmelCase__    =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
									elif self.config.prediction_type == "sample":
													raise NotImplementedError('prediction_type not implemented yet: sample'    )
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''    )

									if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													UpperCAmelCase__    =							(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													UpperCAmelCase__    =							sigma_interpol - sigma_hat

													# store for 2nd order step
													UpperCAmelCase__    =							sample
									else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													UpperCAmelCase__    =							(sample - pred_original_sample) / sigma_interpol

													# 3. delta timestep
													UpperCAmelCase__    =							sigma_next - sigma_hat

													UpperCAmelCase__    =							self.sample
													UpperCAmelCase__    =							None

									UpperCAmelCase__    =							sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			torch.FloatTensor					,lowerCamelCase__     :			torch.FloatTensor					,lowerCamelCase__     :			torch.FloatTensor					,):
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									UpperCAmelCase__    =							self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype    )
									if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__    ):
													# mps does not support float64
													UpperCAmelCase__    =							self.timesteps.to(original_samples.device					,dtype=torch.floataa    )
													UpperCAmelCase__    =							timesteps.to(original_samples.device					,dtype=torch.floataa    )
									else:
													UpperCAmelCase__    =							self.timesteps.to(original_samples.device    )
													UpperCAmelCase__    =							timesteps.to(original_samples.device    )

									UpperCAmelCase__    =							[self.index_for_timestep(lowerCamelCase__					,lowerCamelCase__    ) for t in timesteps]

									UpperCAmelCase__    =							sigmas[step_indices].flatten()
									while len(sigma.shape    ) < len(original_samples.shape    ):
													UpperCAmelCase__    =							sigma.unsqueeze(-1    )

									UpperCAmelCase__    =							original_samples + noise * sigma
									return noisy_samples







					def __len__(       self     :			List[str]    ):
									return self.config.num_train_timesteps




"""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


lowerCAmelCase__						:						str		  =			'base_with_context'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )
				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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

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

				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 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				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=lowerCamelCase )

				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 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							checkpoints.load_tax_checkpoint(args.checkpoint_path )
				UpperCAmelCase__    =							jnp.tree_util.tree_map(onp.array      ,    lowerCamelCase )

				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(lowerCamelCase      ,    lowerCamelCase )
				UpperCAmelCase__    =							inference.InferenceModel(args.checkpoint_path      ,    lowerCamelCase )

				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']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_decoder(ta_checkpoint['target']['decoder']      ,    lowerCamelCase )

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

				UpperCAmelCase__    =							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__":
						lowerCAmelCase__						:						Tuple		  =			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__						:						List[str]		  =			parser.parse_args()

						main(args)



