
'''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__      :		List[str]       							=  """base_with_context"""
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(
       torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)    ,      requires_grad=snake_case_						)
   for lyr_num, lyr in enumerate(model.encoders						):
      _SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

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

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

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

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)
   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

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

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

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

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

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

   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




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

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

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

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

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

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

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

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

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

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

   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(args.checkpoint_path						)
   _SCREAMING_SNAKE_CASE      =				jnp.tree_util.tree_map(onp.array    ,      snake_case_						)

   _SCREAMING_SNAKE_CASE      =				[
       "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()",
   ]

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

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

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				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    ,      )

   _SCREAMING_SNAKE_CASE      =				load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,      snake_case_						)
   _SCREAMING_SNAKE_CASE      =				load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,      snake_case_						)
   _SCREAMING_SNAKE_CASE      =				load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,      snake_case_						)

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

   _SCREAMING_SNAKE_CASE      =				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__":
     UpperCamelCase__      :		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.",
     )
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()

     main(args)








'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """encoder.version""",
       """decoder.version""",
       """model.encoder.version""",
       """model.decoder.version""",
       """decoder.output_projection.weight""",
       """_float_tensor""",
       """encoder.embed_positions._float_tensor""",
       """decoder.embed_positions._float_tensor""",
   ]
   for k in ignore_keys:
      state_dict.pop(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""model"""]
   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				state_dict["""encoder.embed_tokens.weight"""].shape[0]

   _SCREAMING_SNAKE_CASE      =				MaMaaaConfig(
       vocab_size=SCREAMING_SNAKE_CASE_    ,      max_position_embeddings=10_24    ,      encoder_layers=args.encoder_layers    ,      decoder_layers=args.decoder_layers    ,      encoder_attention_heads=args.encoder_attention_heads    ,      decoder_attention_heads=args.decoder_attention_heads    ,      encoder_ffn_dim=args.encoder_ffn_embed_dim    ,      decoder_ffn_dim=args.decoder_ffn_embed_dim    ,      d_model=args.encoder_embed_dim    ,      encoder_layerdrop=args.encoder_layerdrop    ,      decoder_layerdrop=args.decoder_layerdrop    ,      dropout=args.dropout    ,      attention_dropout=args.attention_dropout    ,      activation_dropout=args.activation_dropout    ,      activation_function="""relu"""    ,      )

   _SCREAMING_SNAKE_CASE      =				state_dict["""decoder.embed_tokens.weight"""]
   _SCREAMING_SNAKE_CASE      =				MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
     parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






