




'''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


_SCREAMING_SNAKE_CASE							:   Dict      		= "base_with_context"


def      UpperCamelCase_(	snake_case							:       int							,				snake_case							:       int     ):

							'''simple docstring'''






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

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

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

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

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


def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       Tuple     ):

							'''simple docstring'''






							snake_case_						=      nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T     )     )

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

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

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

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

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

							return model


def      UpperCamelCase_(	snake_case							:       Dict							,				snake_case							:       int     ):

							'''simple docstring'''






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

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

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

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

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

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

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

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

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

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

							return model


def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      checkpoints.load_tax_checkpoint(args.checkpoint_path     )
							snake_case_						=      jnp.tree_util.tree_map(onp.array							,				snake_case     )

							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()",
							]

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

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

							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"							,				)

							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"							,				)

							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							,				)

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

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

							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__":
		_SCREAMING_SNAKE_CASE							:   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.",
		)
		_SCREAMING_SNAKE_CASE							:   Any      		= parser.parse_args()

		main(args)








'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





