

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 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]))
	SCREAMING_SNAKE_CASE :   int					  =							nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"])				,      requires_grad=_a)
	for lyr_num, lyr in enumerate(model.encoders):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							weights[f"layers_{lyr_num}"]
		SCREAMING_SNAKE_CASE :   Dict					  =							nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]))

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

		SCREAMING_SNAKE_CASE :   Dict					  =							nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))

		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   int					  =							nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))

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

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T))

	SCREAMING_SNAKE_CASE :   Tuple					  =							nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"])				,      requires_grad=_a)

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

		SCREAMING_SNAKE_CASE :   int					  =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   int					  =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Dict					  =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Any					  =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]))

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

	SCREAMING_SNAKE_CASE :   Tuple					  =							nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))

	return model

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
	SCREAMING_SNAKE_CASE :   Dict					  =							nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))

	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"])				,      requires_grad=_a)

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

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

		SCREAMING_SNAKE_CASE :   List[Any]					  =							nn.Parameter(
		    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T))

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

		SCREAMING_SNAKE_CASE :   Dict					  =							ly_weight["MultiHeadDotProductAttention_0"]
		SCREAMING_SNAKE_CASE :   List[str]					  =							nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Dict					  =							nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Tuple					  =							nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]))

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

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

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

	return model

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							checkpoints.load_tax_checkpoint(args.checkpoint_path)
	SCREAMING_SNAKE_CASE :   int					  =							jnp.tree_util.tree_map(onp.array				,      _a)

	SCREAMING_SNAKE_CASE :   Union[str, 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()",
	]

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

	SCREAMING_SNAKE_CASE :   str					  =							DDPMScheduler(beta_schedule="squaredcos_cap_v2"				,      variance_type="fixed_large")

	SCREAMING_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"				,      )

	SCREAMING_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"				,      )

	SCREAMING_SNAKE_CASE :   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				,      )

	SCREAMING_SNAKE_CASE :   int					  =							load_notes_encoder(ta_checkpoint["target"]["token_encoder"]				,      _a)
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]				,      _a)
	SCREAMING_SNAKE_CASE :   List[str]					  =							load_decoder(ta_checkpoint["target"]["decoder"]				,      _a)

	SCREAMING_SNAKE_CASE :   Tuple					  =							OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder")

	SCREAMING_SNAKE_CASE :   List[Any]					  =							SpectrogramDiffusionPipeline(
	    notes_encoder=_a				,      continuous_encoder=_a				,      decoder=_a				,      scheduler=_a				,      melgan=_a				,      )
	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)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['PLBartTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)