



'''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							       =			"""base_with_context"""






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]       )       )
					a_       		=nn.Parameter(
					    torch.FloatTensor(weights["Embed_0"]["embedding"]       )       ,	requires_grad=snake_case_       )
					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_       (		lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T       )       )

					a_       		=nn.Parameter(
					    torch.FloatTensor(weights["Embed_0"]["embedding"]       )       ,	requires_grad=snake_case_       )

					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_       (		lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					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=snake_case_       )

					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_       (		lowercase__       ):


					'''simple docstring'''







					a_       		=checkpoints.load_tax_checkpoint(args.checkpoint_path       )
					a_       		=jnp.tree_util.tree_map(onp.array       ,	snake_case_       )

					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(snake_case_       ,	snake_case_       )
					a_       		=inference.InferenceModel(args.checkpoint_path       ,	snake_case_       )

					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"]       ,	snake_case_       )
					a_       		=load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]       ,	snake_case_       )
					a_       		=load_decoder(ta_checkpoint["target"]["decoder"]       ,	snake_case_       )

					a_       		=OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"       )

					a_       		=SpectrogramDiffusionPipeline(
					    notes_encoder=snake_case_       ,	continuous_encoder=snake_case_       ,	decoder=snake_case_       ,	scheduler=snake_case_       ,	melgan=snake_case_       ,	)
					if args.save:
										pipe.save_pretrained(args.output_path       )


if __name__ == "__main__":
		lowercase							       =			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							       =			parser.parse_args()

		main(args)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase							       =			{
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''VisionEncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''TFVisionEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


