


"""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


__snake_case             =   """base_with_context"""







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Union[str, Any]			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				List[str]				 =   nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]			)			)
    snake_case   :				Optional[Any]				 =   nn.Parameter(
        torch.FloatTensor(weights["Embed_0"]["embedding"]			)						,							requires_grad=lowercase			)
    for lyr_num, lyr in enumerate(model.encoders			):
        snake_case   :				Tuple				 =   weights[F'layers_{lyr_num}']
        snake_case   :				Optional[Any]				 =   nn.Parameter(
            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]			)			)

        snake_case   :				Optional[int]				 =   ly_weight["attention"]
        snake_case   :				Any				 =   nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T			)			)
        snake_case   :				Optional[Any]				 =   nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T			)			)
        snake_case   :				Union[str, Any]				 =   nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T			)			)
        snake_case   :				int				 =   nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T			)			)

        snake_case   :				str				 =   nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]			)			)

        snake_case   :				Optional[int]				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T			)			)
        snake_case   :				Optional[Any]				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T			)			)
        snake_case   :				List[Any]				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T			)			)

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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Any			)     ->						List[str]:


    """simple docstring"""





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

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

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

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

        snake_case   :				int				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T			)			)
        snake_case   :				Optional[Any]				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T			)			)
        snake_case   :				List[str]				 =   nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T			)			)
        snake_case   :				Optional[Any]				 =   nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]			)			)

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

    return model







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Tuple			)     ->						int:


    """simple docstring"""





    snake_case   :				Any				 =   nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T			)			)
    snake_case   :				List[str]				 =   nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T			)			)

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

    snake_case   :				Tuple				 =   nn.Parameter(
        torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T			)			)

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

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

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

        snake_case   :				Tuple				 =   ly_weight["MultiHeadDotProductAttention_0"]
        snake_case   :				int				 =   nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T			)			)
        snake_case   :				Optional[Any]				 =   nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T			)			)
        snake_case   :				Optional[int]				 =   nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T			)			)
        snake_case   :				str				 =   nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T			)			)
        snake_case   :				Tuple				 =   nn.Parameter(
            torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]			)			)

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

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

    snake_case   :				List[str]				 =   nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T			)			)

    return model







def   __lowerCAmelCase						(						lowercase  :  Dict			)     ->						str:


    """simple docstring"""





    snake_case   :				Dict				 =   checkpoints.load_tax_checkpoint(args.checkpoint_path			)
    snake_case   :				Any				 =   jnp.tree_util.tree_map(onp.array						,							lowercase			)

    snake_case   :				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()",
    ]

    snake_case   :				List[str]				 =   os.path.join(args.checkpoint_path						,							".."						,							"config.gin"			)
    snake_case   :				Optional[Any]				 =   inference.parse_training_gin_file(lowercase						,							lowercase			)
    snake_case   :				int				 =   inference.InferenceModel(args.checkpoint_path						,							lowercase			)

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

    snake_case   :				str				 =   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"						,							)

    snake_case   :				Any				 =   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"						,							)

    snake_case   :				str				 =   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						,							)

    snake_case   :				Any				 =   load_notes_encoder(ta_checkpoint["target"]["token_encoder"]						,							lowercase			)
    snake_case   :				Any				 =   load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]						,							lowercase			)
    snake_case   :				str				 =   load_decoder(ta_checkpoint["target"]["decoder"]						,							lowercase			)

    snake_case   :				Optional[int]				 =   OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"			)

    snake_case   :				Optional[int]				 =   SpectrogramDiffusionPipeline(
        notes_encoder=lowercase						,							continuous_encoder=lowercase						,							decoder=lowercase						,							scheduler=lowercase						,							melgan=lowercase						,							)
    if args.save:
        pipe.save_pretrained(args.output_path			)


if __name__ == "__main__":
  __snake_case             =   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.""",
  )
  __snake_case             =   parser.parse_args()

  main(args)









"""simple docstring"""



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__snake_case             =   logging.getLogger()







@unittest.skip('''Temporarily disable the doc tests.'''     )
@require_torch
@require_tf
@slow
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     )     ->		Tuple:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   [file for file in os.listdir(UpperCamelCase__						) if os.path.isfile(os.path.join(UpperCamelCase__						,     UpperCamelCase__						)						)]

       if identifier is not None:
           snake_case   :				Optional[Any]				 =   [file for file in files if identifier in file]

       if n_identifier is not None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               for n_ in n_identifier:
                   snake_case   :				str				 =   [file for file in files if n_ not in file]
           else:
               snake_case   :				str				 =   [file for file in files if n_identifier not in file]

       snake_case   :				Tuple				 =   ignore_files or []
       ignore_files.append("__init__.py"						)
       snake_case   :				Optional[Any]				 =   [file for file in files if file not in ignore_files]

       for file in files:
           # Open all files
           print("Testing"						,     UpperCamelCase__						)

           if only_modules:
               snake_case   :				str				 =   file.split("."						)[0]
               try:
                   snake_case   :				Optional[int]				 =   getattr(UpperCamelCase__						,     UpperCamelCase__						)
                   snake_case   :				str				 =   doctest.DocTestSuite(UpperCamelCase__						)
                   snake_case   :				Optional[Any]				 =   unittest.TextTestRunner().run(UpperCamelCase__						)
                   self.assertIs(len(result.failures						)						,     0						)
               except AttributeError:
                   logger.info(F'{module_identifier} is not a module.'						)
           else:
               snake_case   :				Tuple				 =   doctest.testfile(str(".." / directory / file						)						,     optionflags=doctest.ELLIPSIS						)
               self.assertIs(result.failed						,     0						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Tuple				 =   Path("src/transformers"						)
       snake_case   :				List[Any]				 =   "modeling"
       snake_case   :				Dict				 =   [
           "modeling_ctrl.py",
           "modeling_tf_ctrl.py",
       ]
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						,     ignore_files=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "tokenization"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "configuration"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   Path("src/transformers"						)
       snake_case   :				List[str]				 =   ["configuration", "modeling", "tokenization"]
       self.analyze_directory(UpperCamelCase__						,     n_identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Any				 =   Path("docs/source"						)
       snake_case   :				Tuple				 =   ["favicon.ico"]
       self.analyze_directory(UpperCamelCase__						,     ignore_files=UpperCamelCase__						,     only_modules=UpperCamelCase__						)





