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


_lowercase:	Any       			=		'''base_with_context'''






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']  )  )
							_lowerCAmelCase							=							nn.Parameter(
							    torch.FloatTensor(weights['Embed_0']['embedding']  )     , requires_grad=snake_case  )
							for lyr_num, lyr in enumerate(model.encoders  ):
														_lowerCAmelCase							=							weights[F'layers_{lyr_num}']
														_lowerCAmelCase							=							nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']  )  )

														_lowerCAmelCase							=							ly_weight['attention']
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )

														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )

														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )

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






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T  )  )

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

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

														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']  )  )

														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )

							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']  )  )

							return model






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T  )  )
							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T  )  )

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

							_lowerCAmelCase							=							nn.Parameter(
							    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T  )  )

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

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

														_lowerCAmelCase							=							ly_weight['self_attention']
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )

														_lowerCAmelCase							=							ly_weight['MultiHeadDotProductAttention_0']
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']  )  )

														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']  )  )
														_lowerCAmelCase							=							nn.Parameter(
														    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T  )  )
														_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T  )  )

							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']  )  )

							_lowerCAmelCase							=							nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T  )  )

							return model






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							checkpoints.load_tax_checkpoint(args.checkpoint_path  )
							_lowerCAmelCase							=							jnp.tree_util.tree_map(onp.array     , snake_case  )

							_lowerCAmelCase							=							[
							    '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							=							os.path.join(args.checkpoint_path     , '..'     , 'config.gin'  )
							_lowerCAmelCase							=							inference.parse_training_gin_file(snake_case     , snake_case  )
							_lowerCAmelCase							=							inference.InferenceModel(args.checkpoint_path     , snake_case  )

							_lowerCAmelCase							=							DDPMScheduler(beta_schedule='squaredcos_cap_v2'     , variance_type='fixed_large'  )

							_lowerCAmelCase							=							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							=							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							=							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							=							load_notes_encoder(ta_checkpoint['target']['token_encoder']     , snake_case  )
							_lowerCAmelCase							=							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']     , snake_case  )
							_lowerCAmelCase							=							load_decoder(ta_checkpoint['target']['decoder']     , snake_case  )

							_lowerCAmelCase							=							OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'  )

							_lowerCAmelCase							=							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__":
				_lowercase:	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.''',
				)
				_lowercase:	Union[str, Any]       			=		parser.parse_args()

				main(args)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






