"""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


lowerCAmelCase_:					Union[str, Any]     =					"base_with_context"







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]      )      )
     lowercase__				 =							nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]      )    ,				requires_grad=A      )
     for lyr_num, lyr in enumerate(model.encoders      ):
          lowercase__				 =							weights[f'''layers_{lyr_num}''']
          lowercase__				 =							nn.Parameter(
              torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]      )      )

          lowercase__				 =							ly_weight["attention"]
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T      )      )

          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]      )      )

          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T      )      )

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







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



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

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

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

          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(
              torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]      )      )

          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]      )      )

     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]      )      )

     return model







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T      )      )
     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T      )      )

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

     lowercase__				 =							nn.Parameter(
         torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T      )      )

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

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

          lowercase__				 =							ly_weight["self_attention"]
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T      )      )

          lowercase__				 =							ly_weight["MultiHeadDotProductAttention_0"]
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(
              torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]      )      )

          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]      )      )
          lowercase__				 =							nn.Parameter(
              torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T      )      )
          lowercase__				 =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T      )      )

     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]      )      )

     lowercase__				 =							nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T      )      )

     return model







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							checkpoints.load_tax_checkpoint(args.checkpoint_path      )
     lowercase__				 =							jnp.tree_util.tree_map(onp.array    ,				A      )

     lowercase__				 =							[
         "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()",
     ]

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

     lowercase__				 =							DDPMScheduler(beta_schedule="squaredcos_cap_v2"    ,				variance_type="fixed_large"      )

     lowercase__				 =							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"    ,				)

     lowercase__				 =							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"    ,				)

     lowercase__				 =							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    ,				)

     lowercase__				 =							load_notes_encoder(ta_checkpoint["target"]["token_encoder"]    ,				A      )
     lowercase__				 =							load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]    ,				A      )
     lowercase__				 =							load_decoder(ta_checkpoint["target"]["decoder"]    ,				A      )

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

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


if __name__ == "__main__":
 lowerCAmelCase_:					Optional[int]     =					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.",
 )
 lowerCAmelCase_:					Union[str, Any]     =					parser.parse_args()

 main(args)





"""simple docstring"""


lowerCAmelCase_:					Any     =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ):
          lowercase__				 =							f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
          raise TypeError(A      )

     lowercase__				 =							"".join(bin(A      )[2:].zfill(8      ) for byte in data      )

     lowercase__				 =							len(A      ) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          lowercase__				 =							b"=" * ((6 - len(A      ) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(A      ) % 6)
     else:
          lowercase__				 =							b""

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]    ,				2      )]
             for index in range(0    ,				len(A      )    ,				6      )      ).encode()
         + padding
     )







def 					__a   (		A      ):






     '''simple docstring'''



     if not isinstance(A    ,				A      ) and not isinstance(A    ,				A      ):
          lowercase__				 =							(
              "argument should be a bytes-like object or ASCII string, "
              f'''not \'{encoded_data.__class__.__name__}\''''
          )
          raise TypeError(A      )

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(A    ,				A      ):
          try:
               lowercase__				 =							encoded_data.decode("utf-8"      )
          except UnicodeDecodeError:
               raise ValueError("base64 encoded data should only contain ASCII characters"      )

     lowercase__				 =							encoded_data.count("="      )

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]      ), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data      ), "Invalid base64 character(s) found."

     # Check the padding
     assert len(A      ) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          lowercase__				 =							encoded_data[:-padding]

          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )[: -padding * 2]
     else:
          lowercase__				 =							"".join(
              bin(B64_CHARSET.index(A      )      )[2:].zfill(6      ) for char in encoded_data      )

     lowercase__				 =							[
         int(binary_stream[index : index + 8]    ,				2      )
         for index in range(0    ,				len(A      )    ,				8      )
     ]

     return bytes(A      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




