'''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


__UpperCAmelCase     							="base_with_context"






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    List[Any]:
    __lowerCamelCase		=  nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']				)				)
    __lowerCamelCase		=  nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']				)				,  requires_grad=UpperCamelCase__				)
    for lyr_num, lyr in enumerate(model.encoders				):
        __lowerCamelCase		=  weights[f"""layers_{lyr_num}"""]
        __lowerCamelCase		=  nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']				)				)

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

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

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

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






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Union[str, Any]:
    __lowerCamelCase		=  nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T				)				)

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

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

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

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

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

    return model






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Optional[int]:
    __lowerCamelCase		=  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T				)				)
    __lowerCamelCase		=  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T				)				)

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

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

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

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

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

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

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

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

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

    return model






def __lowerCAmelCase (							UpperCamelCase__				)	->    Optional[int]:
    __lowerCamelCase		=  checkpoints.load_tax_checkpoint(args.checkpoint_path				)
    __lowerCamelCase		=  jnp.tree_util.tree_map(onp.array				,  UpperCamelCase__				)

    __lowerCamelCase		=  [
        '''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()''',
    ]

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

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

    __lowerCamelCase		=  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'''				,  )

    __lowerCamelCase		=  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'''				,  )

    __lowerCamelCase		=  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				,  )

    __lowerCamelCase		=  load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,  UpperCamelCase__				)
    __lowerCamelCase		=  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,  UpperCamelCase__				)
    __lowerCamelCase		=  load_decoder(ta_checkpoint['''target''']['''decoder''']				,  UpperCamelCase__				)

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

    __lowerCamelCase		=  SpectrogramDiffusionPipeline(
        notes_encoder=UpperCamelCase__				,  continuous_encoder=UpperCamelCase__				,  decoder=UpperCamelCase__				,  scheduler=UpperCamelCase__				,  melgan=UpperCamelCase__				,  )
    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 os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
				from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if is_torch_version('<'				,			'2.0.0' ) or not hasattr(UpperCamelCase__				,			'_dynamo' ):
														return False
							return isinstance(UpperCamelCase__				,			torch._dynamo.eval_frame.OptimizedModule )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ = True ):






							"""simple docstring"""





							A__		   =							(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

							A__		   =							is_compiled_module(UpperCamelCase__ )
							if is_compiled:
														A__		   =							model
														A__		   =							model._orig_mod

							if is_deepspeed_available():
														options += (DeepSpeedEngine,)

							while isinstance(UpperCamelCase__				,			UpperCamelCase__ ):
														A__		   =							model.module

							if not keep_fpaa_wrapper:
														A__		   =							getattr(UpperCamelCase__				,			'forward' )
														A__		   =							model.__dict__.pop('_original_forward'				,			UpperCamelCase__ )
														if original_forward is not None:
																					while hasattr(UpperCamelCase__				,			'__wrapped__' ):
																												A__		   =							forward.__wrapped__
																												if forward == original_forward:
																																			break
																					A__		   =							forward
														if getattr(UpperCamelCase__				,			'_converted_to_transformer_engine'				,			UpperCamelCase__ ):
																					convert_model(UpperCamelCase__				,			to_transformer_engine=UpperCamelCase__ )

							if is_compiled:
														A__		   =							model
														A__		   =							compiled_model

							return model






def      UpperCAmelCase    (		):






							"""simple docstring"""





							PartialState().wait_for_everyone()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							if PartialState().distributed_type == DistributedType.TPU:
														xm.save(UpperCamelCase__				,			UpperCamelCase__ )
							elif PartialState().local_process_index == 0:
														torch.save(UpperCamelCase__				,			UpperCamelCase__ )






@contextmanager
def      UpperCAmelCase    (		**UpperCamelCase__ ):






							"""simple docstring"""





							for key, value in kwargs.items():
														A__		   =							str(UpperCamelCase__ )

							yield

							for key in kwargs:
														if key.upper() in os.environ:
																					del os.environ[key.upper()]






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if not hasattr(UpperCamelCase__				,			'__qualname__' ) and not hasattr(UpperCamelCase__				,			'__name__' ):
														A__		   =							getattr(UpperCamelCase__				,			'__class__'				,			UpperCamelCase__ )
							if hasattr(UpperCamelCase__				,			'__qualname__' ):
														return obj.__qualname__
							if hasattr(UpperCamelCase__				,			'__name__' ):
														return obj.__name__
							return str(UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							for key, value in source.items():
														if isinstance(UpperCamelCase__				,			UpperCamelCase__ ):
																					A__		   =							destination.setdefault(UpperCamelCase__				,			{} )
																					merge_dicts(UpperCamelCase__				,			UpperCamelCase__ )
														else:
																					A__		   =							value

							return destination






def      UpperCAmelCase    (		UpperCamelCase__ = None ):






							"""simple docstring"""





							if port is None:
														A__		   =							29_500
							with socket.socket(socket.AF_INET				,			socket.SOCK_STREAM ) as s:
														return s.connect_ex(('localhost', port) ) == 0





