




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


SCREAMING_SNAKE_CASE__       :      Dict  =   'base_with_context'

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

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

              lowerCamelCase   :      Tuple			 =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]    )    )

              lowerCamelCase   :      Union[str, Any]			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T    )    )
              lowerCamelCase   :      Any			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T    )    )
              lowerCamelCase   :      Dict			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T    )    )

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> List[str]:
       lowerCamelCase   :      Union[str, Any]			 =					nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T    )    )

       lowerCamelCase   :      Optional[Any]			 =					nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]    ) ,requires_grad=_SCREAMING_SNAKE_CASE    )

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

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

              lowerCamelCase   :      List[str]			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T    )    )
              lowerCamelCase   :      Tuple			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T    )    )
              lowerCamelCase   :      str			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T    )    )
              lowerCamelCase   :      Dict			 =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]    )    )

       lowerCamelCase   :      List[Any]			 =					nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]    )    )

       return model

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Tuple:
       lowerCamelCase   :      List[Any]			 =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T    )    )
       lowerCamelCase   :      Tuple			 =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T    )    )

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

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

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

              lowerCamelCase   :      Any			 =					nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T    )    )

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

              lowerCamelCase   :      str			 =					ly_weight["MultiHeadDotProductAttention_0"]
              lowerCamelCase   :      Dict			 =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T    )    )
              lowerCamelCase   :      int			 =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T    )    )
              lowerCamelCase   :      Union[str, Any]			 =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T    )    )
              lowerCamelCase   :      List[Any]			 =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T    )    )
              lowerCamelCase   :      Any			 =					nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]    )    )

              lowerCamelCase   :      str			 =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]    )    )
              lowerCamelCase   :      str			 =					nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T    )    )
              lowerCamelCase   :      Dict			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T    )    )
              lowerCamelCase   :      str			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T    )    )
              lowerCamelCase   :      List[Any]			 =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T    )    )

       lowerCamelCase   :      Any			 =					nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]    )    )

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

       return model

def      A      (      _SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      str			 =					checkpoints.load_tax_checkpoint(args.checkpoint_path    )
       lowerCamelCase   :      Union[str, Any]			 =					jnp.tree_util.tree_map(onp.array ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Union[str, Any]			 =					[
           "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   :      Tuple			 =					os.path.join(args.checkpoint_path ,".." ,"config.gin"    )
       lowerCamelCase   :      Optional[Any]			 =					inference.parse_training_gin_file(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					inference.InferenceModel(args.checkpoint_path ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[int]			 =					DDPMScheduler(beta_schedule="squaredcos_cap_v2" ,variance_type="fixed_large"    )

       lowerCamelCase   :      Dict			 =					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   :      List[str]			 =					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   :      Optional[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 ,)

       lowerCamelCase   :      List[str]			 =					load_notes_encoder(ta_checkpoint["target"]["token_encoder"] ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[Any]			 =					load_decoder(ta_checkpoint["target"]["decoder"] ,_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Any			 =					OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"    )

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


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      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.',
       )
       SCREAMING_SNAKE_CASE__       :      int  =   parser.parse_args()

       main(args)












def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE ,x % y    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return (x * y) // greatest_common_divisor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE = 20    )	-> int:
       lowerCamelCase   :      List[Any]			 =					1
       for i in range(1 ,n + 1    ):
              lowerCamelCase   :      List[str]			 =					lcm(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       return g


if __name__ == "__main__":
       print(f'''{solution() = }''')






