

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       __UpperCamelCase     (lowerCAmelCase       :	List[str],     lowerCAmelCase       :	str  )  ->	Optional[Any]:
      A 			=					nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']  )  )
      A 			=					nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']  ),     requires_grad=lowerCamelCase_  )
      for lyr_num, lyr in enumerate(model.encoders  ):
            A 			=					weights[f'''layers_{lyr_num}''']
            A 			=					nn.Parameter(
                torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']  )  )

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

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

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

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


def       __UpperCamelCase     (lowerCAmelCase       :	str,     lowerCAmelCase       :	Optional[int]  )  ->	int:
      A 			=					nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T  )  )

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

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

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

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

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

      return model


def       __UpperCamelCase     (lowerCAmelCase       :	int,     lowerCAmelCase       :	int  )  ->	Optional[Any]:
      A 			=					nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T  )  )
      A 			=					nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T  )  )

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

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

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

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

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

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

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

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

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

      return model


def       __UpperCamelCase     (lowerCAmelCase       :	List[Any]  )  ->	int:
      A 			=					checkpoints.load_tax_checkpoint(args.checkpoint_path  )
      A 			=					jnp.tree_util.tree_map(onp.array,     lowerCamelCase_  )

      A 			=					[
          "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()",
      ]

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

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

      A 			=					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',     )

      A 			=					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',     )

      A 			=					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,     )

      A 			=					load_notes_encoder(ta_checkpoint['target']['token_encoder'],     lowerCamelCase_  )
      A 			=					load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'],     lowerCamelCase_  )
      A 			=					load_decoder(ta_checkpoint['target']['decoder'],     lowerCamelCase_  )

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

      A 			=					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__":
  _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 torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case									=    logging.get_logger(__name__)  # pylint: disable=invalid-name




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

  # uncondition for scaling
  lowerCAmelCase__     :   Dict     		=	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





