



'''simple docstring'''





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						42
    lowercase   =						None





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=0.999	,  _lowerCAmelCase="cosine"	,  )						->					Optional[int]:
      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}"						)

      snake_case__    :       Tuple       		= []
      for i in range(_lowerCAmelCase						):
            snake_case__    :       Union[str, Any]       		= i / num_diffusion_timesteps
            snake_case__    :       str       		= (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        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						1




    @register_to_config
    def __init__(       self   :	Optional[int]					,	snake_case_   :	int = 1_000					,	snake_case_   :	float = 0.0001					,	snake_case_   :	float = 0.02					,	snake_case_   :	str = "linear"					,	snake_case_   :	Optional[Union[np.ndarray, List[float]]] = None					,	snake_case_   :	bool = True					,	snake_case_   :	bool = True					,	snake_case_   :	int = 0					,	snake_case_   :	str = "epsilon"					,	snake_case_   :	float = 1.0					,	**snake_case_   :	List[Any]					,	):
          if kwargs.get("""set_alpha_to_one"""					,	snake_case_		) is not None:
                snake_case__    :       Union[str, Any]       		= (
                    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
                )
                deprecate("""set_alpha_to_one"""					,	"""1.0.0"""					,	snake_case_					,	standard_warn=snake_case_		)
                snake_case__    :       List[Any]       		= kwargs["""set_alpha_to_one"""]
          if trained_betas is not None:
                snake_case__    :       List[str]       		= torch.tensor(snake_case_					,	dtype=torch.floataa		)
          elif beta_schedule == "linear":
                snake_case__    :       Union[str, Any]       		= torch.linspace(snake_case_					,	snake_case_					,	snake_case_					,	dtype=torch.floataa		)
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                snake_case__    :       Any       		= (
                    torch.linspace(beta_start**0.5					,	beta_end**0.5					,	snake_case_					,	dtype=torch.floataa		) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                snake_case__    :       List[str]       		= betas_for_alpha_bar(snake_case_		)
          else:
                raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

          snake_case__    :       Dict       		= 1.0 - self.betas
          snake_case__    :       List[str]       		= torch.cumprod(self.alphas					,	dim=0		)

          # At every step in inverted ddim, we are looking into the next alphas_cumprod
          # For the final step, there is no next alphas_cumprod, and the index is out of bounds
          # `set_alpha_to_zero` decides whether we set this parameter simply to zero
          # in this case, self.step() just output the predicted noise
          # or whether we use the final alpha of the "non-previous" one.
          snake_case__    :       Optional[Any]       		= torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

          # standard deviation of the initial noise distribution
          snake_case__    :       Any       		= 1.0

          # setable values
          snake_case__    :       Any       		= None
          snake_case__    :       Tuple       		= torch.from_numpy(np.arange(0					,	snake_case_		).copy().astype(np.intaa		)		)




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	torch.FloatTensor					,	snake_case_   :	Optional[int] = None		):
          return sample




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	int					,	snake_case_   :	Union[str, torch.device] = None		):

          if num_inference_steps > self.config.num_train_timesteps:
                raise ValueError(
                    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                    f" maximal {self.config.num_train_timesteps} timesteps."		)

          snake_case__    :       List[str]       		= num_inference_steps
          snake_case__    :       Tuple       		= self.config.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
          snake_case__    :       List[Any]       		= (np.arange(0					,	snake_case_		) * step_ratio).round().copy().astype(np.intaa		)
          snake_case__    :       Tuple       		= torch.from_numpy(snake_case_		).to(snake_case_		)
          self.timesteps += self.config.steps_offset




    def lowerCamelCase			(       self   :	Tuple					,	snake_case_   :	torch.FloatTensor					,	snake_case_   :	int					,	snake_case_   :	torch.FloatTensor					,	snake_case_   :	float = 0.0					,	snake_case_   :	bool = False					,	snake_case_   :	Optional[torch.FloatTensor] = None					,	snake_case_   :	bool = True					,	):
          # 1. get previous step value (=t+1)
          snake_case__    :       Any       		= timestep + self.config.num_train_timesteps // self.num_inference_steps

          # 2. compute alphas, betas
          # change original implementation to exactly match noise levels for analogous forward process
          snake_case__    :       Optional[int]       		= self.alphas_cumprod[timestep]
          snake_case__    :       Union[str, Any]       		= (
              self.alphas_cumprod[prev_timestep]
              if prev_timestep < self.config.num_train_timesteps
              else self.final_alpha_cumprod
          )

          snake_case__    :       int       		= 1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
                snake_case__    :       Any       		= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                snake_case__    :       Optional[int]       		= model_output
          elif self.config.prediction_type == "sample":
                snake_case__    :       Union[str, Any]       		= model_output
                snake_case__    :       Optional[int]       		= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
                snake_case__    :       str       		= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                snake_case__    :       Any       		= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
                raise ValueError(
                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                    """ `v_prediction`"""		)

          # 4. Clip or threshold "predicted x_0"
          if self.config.clip_sample:
                snake_case__    :       Tuple       		= pred_original_sample.clamp(
                    -self.config.clip_sample_range					,	self.config.clip_sample_range		)

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          snake_case__    :       Any       		= (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          snake_case__    :       Optional[int]       		= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
                return (prev_sample, pred_original_sample)
          return DDIMSchedulerOutput(prev_sample=snake_case_					,	pred_original_sample=snake_case_		)




    def __len__(       self   :	Tuple		):
          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


__a  			=						"base_with_context"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
      snake_case__    :       Dict       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)
      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[str]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       Dict       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       List[str]       		= ly_weight["""attention"""]
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= 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__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)
      return model





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

      snake_case__    :       str       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

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

            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= 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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
      snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

      snake_case__    :       List[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

      snake_case__    :       Optional[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T						)						)

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

            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

            snake_case__    :       Any       		= ly_weight["""self_attention"""]
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= ly_weight["""MultiHeadDotProductAttention_0"""]
            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       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__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
            snake_case__    :       str       		= 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__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

      return model





def 		__snake_case(							_lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
      snake_case__    :       List[str]       		= jnp.tree_util.tree_map(onp.array	,  _lowerCAmelCase						)

      snake_case__    :       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()""",
      ]

      snake_case__    :       Any       		= os.path.join(args.checkpoint_path	,  """.."""	,  """config.gin"""						)
      snake_case__    :       Dict       		= inference.parse_training_gin_file(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       int       		= inference.InferenceModel(args.checkpoint_path	,  _lowerCAmelCase						)

      snake_case__    :       Optional[int]       		= 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__    :       Union[str, Any]       		= 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__    :       Any       		= 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__    :       Optional[int]       		= load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Union[str, Any]       		= load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Optional[Any]       		= load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,  _lowerCAmelCase						)

      snake_case__    :       str       		= OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

      snake_case__    :       Dict       		= 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__":
       __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)
