


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
__snake_case   							=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def 		_A		(		_lowercase )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			{}
     with open(_lowercase					,  'r' ) as file:
          for line_number, line in enumerate(_lowercase ):
               __UpperCamelCase =			line.strip()
               if line:
                    __UpperCamelCase =			line.split()
                    __UpperCamelCase =			line_number
                    __UpperCamelCase =			words[0]
                    __UpperCamelCase =			value
     return result






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> List[str]:


     """simple docstring"""


     for attribute in key.split('.' ):
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase )

     __UpperCamelCase =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase ):
               __UpperCamelCase =			PARAM_MAPPING[full_name.split('.' )[-1]]
               __UpperCamelCase =			'param'

     if weight_type is not None and weight_type != "param":
          __UpperCamelCase =			getattr(_lowercase					,  _lowercase ).shape
     elif weight_type is not None and weight_type == "param":
          __UpperCamelCase =			hf_pointer
          for attribute in hf_param_name.split('.' ):
               __UpperCamelCase =			getattr(_lowercase					,  _lowercase )
          __UpperCamelCase =			shape_pointer.shape

          # let's reduce dimension
          __UpperCamelCase =			value[0]
     else:
          __UpperCamelCase =			hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
              f''' {value.shape} for {full_name}''' )

     if weight_type == "weight":
          __UpperCamelCase =			value
     elif weight_type == "weight_g":
          __UpperCamelCase =			value
     elif weight_type == "weight_v":
          __UpperCamelCase =			value
     elif weight_type == "bias":
          __UpperCamelCase =			value
     elif weight_type == "param":
          for attribute in hf_param_name.split('.' ):
               __UpperCamelCase =			getattr(_lowercase					,  _lowercase )
          __UpperCamelCase =			value
     else:
          __UpperCamelCase =			value

     logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_lowercase ):
               __UpperCamelCase =			PARAM_MAPPING[full_name.split('.' )[-1]]
               __UpperCamelCase =			'param'

     if weight_type is not None and weight_type != "param":
          __UpperCamelCase =			'.'.join([key, weight_type] )
     elif weight_type is not None and weight_type == "param":
          __UpperCamelCase =			'.'.join([key, hf_param_name] )
     else:
          __UpperCamelCase =			key

     __UpperCamelCase =			value if 'lm_head' in full_key else value[0]


__snake_case   							=						{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			False
     for key, mapped_key in MAPPING.items():
          __UpperCamelCase =			'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
               __UpperCamelCase =			True
               if "*" in mapped_key:
                    __UpperCamelCase =			name.split(_lowercase )[0].split('.' )[-2]
                    __UpperCamelCase =			mapped_key.replace('*'					,  _lowercase )
               if "weight_g" in name:
                    __UpperCamelCase =			'weight_g'
               elif "weight_v" in name:
                    __UpperCamelCase =			'weight_v'
               elif "bias" in name:
                    __UpperCamelCase =			'bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    __UpperCamelCase =			'weight'
               else:
                    __UpperCamelCase =			None
               if hf_dict is not None:
                    rename_dict(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
               else:
                    set_recursively(_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
               return is_used
     return is_used






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			[]
     __UpperCamelCase =			fairseq_model.state_dict()

     __UpperCamelCase =			hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          __UpperCamelCase =			False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  hf_model.config.feat_extract_norm == 'group'					,  )
               __UpperCamelCase =			True
          else:
               __UpperCamelCase =			load_wavaveca_layer(_lowercase					,  _lowercase					,  _lowercase )
          if not is_used:
               unused_weights.append(_lowercase )

     logger.warning(f'''Unused weights: {unused_weights}''' )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			full_name.split('conv_layers.' )[-1]
     __UpperCamelCase =			name.split('.' )
     __UpperCamelCase =			int(items[0] )
     __UpperCamelCase =			int(items[1] )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'''{full_name} has size {value.shape}, but'''
                        f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
               __UpperCamelCase =			value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
     else:
          unused_weights.append(_lowercase )






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=None					,  _lowercase=None					,  _lowercase=True					,  _lowercase=False )					-> Dict:


     """simple docstring"""


     if config_path is not None:
          __UpperCamelCase =			WavaVecaConfig.from_pretrained(_lowercase )
     else:
          __UpperCamelCase =			WavaVecaConfig()

     if is_seq_class:
          __UpperCamelCase =			read_txt_into_dict(_lowercase )
          __UpperCamelCase =			idalabel
          __UpperCamelCase =			WavaVecaForSequenceClassification(_lowercase )
          __UpperCamelCase =			WavaVecaFeatureExtractor(
              feature_size=1					,  sampling_rate=1_60_00					,  padding_value=0					,  do_normalize=_lowercase					,  return_attention_mask=_lowercase					,  )
          feature_extractor.save_pretrained(_lowercase )

     elif is_finetuned:
          if dict_path:
               __UpperCamelCase =			Dictionary.load(_lowercase )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __UpperCamelCase =			target_dict.pad_index
               __UpperCamelCase =			target_dict.bos_index
               __UpperCamelCase =			target_dict.eos_index
               __UpperCamelCase =			len(target_dict.symbols )
               __UpperCamelCase =			os.path.join(_lowercase					,  'vocab.json' )
               if not os.path.isdir(_lowercase ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_lowercase ) )
                    return
               os.makedirs(_lowercase					,  exist_ok=_lowercase )
               __UpperCamelCase =			target_dict.indices

               # fairseq has the <pad> and <s> switched
               __UpperCamelCase =			0
               __UpperCamelCase =			1
               with open(_lowercase					,  'w'					,  encoding='utf-8' ) as vocab_handle:
                    json.dump(_lowercase					,  _lowercase )
               __UpperCamelCase =			WavaVecaCTCTokenizer(
                   _lowercase					,  unk_token=target_dict.unk_word					,  pad_token=target_dict.pad_word					,  bos_token=target_dict.bos_word					,  eos_token=target_dict.eos_word					,  word_delimiter_token='|'					,  do_lower_case=_lowercase					,  )
               __UpperCamelCase =			True if config.feat_extract_norm == 'layer' else False
               __UpperCamelCase =			WavaVecaFeatureExtractor(
                   feature_size=1					,  sampling_rate=1_60_00					,  padding_value=0					,  do_normalize=_lowercase					,  return_attention_mask=_lowercase					,  )
               __UpperCamelCase =			WavaVecaProcessor(feature_extractor=_lowercase					,  tokenizer=_lowercase )
               processor.save_pretrained(_lowercase )

          __UpperCamelCase =			WavaVecaForCTC(_lowercase )
     else:
          __UpperCamelCase =			WavaVecaForPreTraining(_lowercase )

     if is_finetuned or is_seq_class:
          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]					,  arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
     else:
          __UpperCamelCase =			argparse.Namespace(task='audio_pretraining' )
          __UpperCamelCase =			fairseq.tasks.setup_task(_lowercase )

          __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,  task=_lowercase )

     __UpperCamelCase =			model[0].eval()

     recursively_load_weights(_lowercase					,  _lowercase					,  not is_finetuned )

     hf_wavavec.save_pretrained(_lowercase )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    parser.add_argument(
        '''--is_seq_class''',
        action='''store_true''',
        help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
    )
    __snake_case   							=						parser.parse_args()

    __snake_case   							=						not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )







'''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


a							   =				'base_with_context'







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







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

						snake_case:    Any      							=ly_weight['attention']
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=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_mlp_layer_norm']['scale']					)					)

						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

			snake_case:    Dict      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

			for lyr_num, lyr in enumerate(model.encoders					):
						snake_case:    List[Any]      							=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:    Optional[int]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Tuple      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

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

			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
			snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

			snake_case:    Tuple      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

			snake_case:    Any      							=nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

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

						snake_case:    int      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T					)					)

						snake_case:    str      							=ly_weight['self_attention']
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

						snake_case:    Optional[Any]      							=ly_weight['MultiHeadDotProductAttention_0']
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Any      							=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:    Union[str, Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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

			snake_case:    int      							=nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T					)					)

			return model







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=checkpoints.load_tax_checkpoint(args.checkpoint_path					)
			snake_case:    Tuple      							=jnp.tree_util.tree_map(onp.array				,							__UpperCAmelCase					)

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

			snake_case:    List[Any]      							=DDPMScheduler(beta_schedule='squaredcos_cap_v2'				,							variance_type='fixed_large'					)

			snake_case:    Optional[Any]      							=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:    Optional[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:    List[Any]      							=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:    Optional[Any]      							=load_notes_encoder(ta_checkpoint['target']['token_encoder']				,							__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']				,							__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=load_decoder(ta_checkpoint['target']['decoder']				,							__UpperCAmelCase					)

			snake_case:    int      							=OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

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


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

	main(args)




