




"""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


lowercase__			=		"""base_with_context"""







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

								_lowerCamelCase	:     int       =       ly_weight['attention']
								_lowerCamelCase	:     Any       =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
								_lowerCamelCase	:     Dict       =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
								_lowerCamelCase	:     Any       =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
								_lowerCamelCase	:     List[Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)

								_lowerCamelCase	:     Optional[int]       =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)

								_lowerCamelCase	:     List[str]       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T						)						)
								_lowerCamelCase	:     Optional[int]       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T						)						)
								_lowerCamelCase	:     int       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T						)						)

				_lowerCamelCase	:     Union[str, Any]       =       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)
				return model







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

				_lowerCamelCase	:     Optional[int]       =       nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding']						)				,       requires_grad=lowercase__						)

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

								_lowerCamelCase	:     Union[str, Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
								_lowerCamelCase	:     List[str]       =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
								_lowerCamelCase	:     Union[str, Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
								_lowerCamelCase	:     Tuple       =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)
								_lowerCamelCase	:     Optional[int]       =       nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']						)						)

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

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

				return model







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_lowerCamelCase	:     List[str]       =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T						)						)
				_lowerCamelCase	:     Dict       =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T						)						)

				_lowerCamelCase	:     List[Any]       =       nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding']						)				,       requires_grad=lowercase__						)

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

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

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

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

								_lowerCamelCase	:     int       =       ly_weight['MultiHeadDotProductAttention_0']
								_lowerCamelCase	:     Optional[Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
								_lowerCamelCase	:     Optional[Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
								_lowerCamelCase	:     List[str]       =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
								_lowerCamelCase	:     Union[str, Any]       =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)
								_lowerCamelCase	:     str       =       nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']						)						)

								_lowerCamelCase	:     int       =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)
								_lowerCamelCase	:     Dict       =       nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T						)						)
								_lowerCamelCase	:     Optional[Any]       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T						)						)
								_lowerCamelCase	:     Tuple       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T						)						)
								_lowerCamelCase	:     Any       =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T						)						)

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

				_lowerCamelCase	:     List[Any]       =       nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T						)						)

				return model







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       checkpoints.load_tax_checkpoint(args.checkpoint_path						)
				_lowerCamelCase	:     Optional[int]       =       jnp.tree_util.tree_map(onp.array				,       lowercase__						)

				_lowerCamelCase	:     int       =       [
				    '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	:     Tuple       =       os.path.join(args.checkpoint_path				,       '..'				,       'config.gin'						)
				_lowerCamelCase	:     Any       =       inference.parse_training_gin_file(lowercase__				,       lowercase__						)
				_lowerCamelCase	:     str       =       inference.InferenceModel(args.checkpoint_path				,       lowercase__						)

				_lowerCamelCase	:     Optional[int]       =       DDPMScheduler(beta_schedule='squaredcos_cap_v2'				,       variance_type='fixed_large'						)

				_lowerCamelCase	:     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'				,       )

				_lowerCamelCase	:     List[str]       =       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	:     int       =       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	:     Optional[Any]       =       load_notes_encoder(ta_checkpoint['target']['token_encoder']				,       lowercase__						)
				_lowerCamelCase	:     Any       =       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']				,       lowercase__						)
				_lowerCamelCase	:     Optional[int]       =       load_decoder(ta_checkpoint['target']['decoder']				,       lowercase__						)

				_lowerCamelCase	:     Any       =       OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'						)

				_lowerCamelCase	:     Tuple       =       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__":
			lowercase__			=		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__			=		parser.parse_args()

			main(args)





"""simple docstring"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}