



"""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


lowercase__    :  str							    =	'''base_with_context'''




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

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

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

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

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




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     Optional[Any]				=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

    snake_case_ :     Any				=	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )		,			requires_grad=_a    )

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

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

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

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

    return model




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     int				=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
    snake_case_ :     Optional[int]				=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

    snake_case_ :     Tuple				=	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )		,			requires_grad=_a    )

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

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

        snake_case_ :     List[Any]				=	nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T    )    )

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

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

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

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

    snake_case_ :     Dict				=	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

    return model




def __lowercase			(							_a    ):
    snake_case_ :     List[str]				=	checkpoints.load_tax_checkpoint(args.checkpoint_path    )
    snake_case_ :     Tuple				=	jnp.tree_util.tree_map(onp.array		,			_a    )

    snake_case_ :     str				=	[
        '''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()''',
    ]

    snake_case_ :     Any				=	os.path.join(args.checkpoint_path		,			'''..'''		,			'''config.gin'''    )
    snake_case_ :     List[Any]				=	inference.parse_training_gin_file(_a		,			_a    )
    snake_case_ :     Any				=	inference.InferenceModel(args.checkpoint_path		,			_a    )

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

    snake_case_ :     Union[str, 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'''		,			)

    snake_case_ :     int				=	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'''		,			)

    snake_case_ :     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		,			)

    snake_case_ :     Any				=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']		,			_a    )
    snake_case_ :     List[str]				=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']		,			_a    )
    snake_case_ :     List[str]				=	load_decoder(ta_checkpoint['''target''']['''decoder''']		,			_a    )

    snake_case_ :     Union[str, Any]				=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

    snake_case_ :     List[Any]				=	SpectrogramDiffusionPipeline(
        notes_encoder=_a		,			continuous_encoder=_a		,			decoder=_a		,			scheduler=_a		,			melgan=_a		,			)
    if args.save:
        pipe.save_pretrained(args.output_path    )


if __name__ == "__main__":
     lowercase__    :  List[str]							    =	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.''',
     )
     lowercase__    :  Any							    =	parser.parse_args()

     main(args)










"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     for l in label:
         if l not in self.labels:
             raise ValueError(
                 f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

     return [self.labels[l] for l in label]






 @torch.no_grad()
 def __call__(     self		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

     snake_case_ :     str				=	randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)  ,     generator=lowercase_  ,     device=self.device  ,     dtype=self.transformer.dtype  ,     )
     snake_case_ :     Optional[Any]				=	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(lowercase_				)

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     if guidance_scale > 1:
         snake_case_,	snake_case_ :     Optional[Any]				=	latent_model_input.chunk(2  ,     dim=0				)
     else:
         snake_case_ :     Dict				=	latent_model_input

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





