

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


lowerCAmelCase__		      =       'base_with_context'



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Union[str, Any]:




				'''simple docstring'''





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

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

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

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

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Dict					)	->					Union[str, Any]:




				'''simple docstring'''





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

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

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

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

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

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

				return model



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					str:




				'''simple docstring'''





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

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

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

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

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

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

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

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

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

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

				return model



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]					)	->					int:




				'''simple docstring'''





				A__						=	checkpoints.load_tax_checkpoint(args.checkpoint_path					)
				A__						=	jnp.tree_util.tree_map(onp.array		,				lowercase__					)

				A__						=	[
				    '''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()''',
				]

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

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

				A__						=	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"		,				)

				A__						=	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"		,				)

				A__						=	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		,				)

				A__						=	load_notes_encoder(ta_checkpoint["target"]["token_encoder"]		,				lowercase__					)
				A__						=	load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]		,				lowercase__					)
				A__						=	load_decoder(ta_checkpoint["target"]["decoder"]		,				lowercase__					)

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

				A__						=	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__":
	lowerCAmelCase__		      =       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.""",
	)
	lowerCAmelCase__		      =       parser.parse_args()

	main(args)





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__lowerCAmelCase	: List[str]  				='true'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=16      ):
						set_seed(42      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		RegressionModel()
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		deepcopy(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		RegressionDataset(length=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		DataLoader(lowercase__    ,       batch_size=lowercase__      )
						model.to(accelerator.device      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return model, ddp_model, dataloader


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		load_dataset('''glue'''    ,       '''mrpc'''    ,       split='''validation'''      )

						def tokenize_function(lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		dataset.map(
												    lowercase__    ,       batched=lowercase__    ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,       )

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												if use_longest:
																		return tokenizer.pad(lowercase__    ,       padding='''longest'''    ,       return_tensors='''pt'''      )
												return tokenizer.pad(lowercase__    ,       padding='''max_length'''    ,       max_length=128    ,       return_tensors='''pt'''      )

						return DataLoader(lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=16      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     str					=		Accelerator(dispatch_batches=lowercase__    ,       split_batches=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		get_dataloader(lowercase__    ,       not dispatch_batches      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		AutoModelForSequenceClassification.from_pretrained(
						    '''hf-internal-testing/mrpc-bert-base-cased'''    ,       return_dict=lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		accelerator.prepare(lowercase__    ,       lowercase__      )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		[]
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowercase__      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		accelerator.gather_for_metrics((logit, target)      )
																		logits_and_targets.append((logit, target)      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		[], []
						for logit, targ in logits_and_targets:
												logits.append(lowercase__      )
												targs.append(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		torch.cat(lowercase__      ), torch.cat(lowercase__      )
						return logits, targs


def        _UpperCamelCase							(      lowercase__    ,       lowercase__=82    ,       lowercase__=False    ,       lowercase__=False    ,       lowercase__=16      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_basic_setup(lowercase__    ,       lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		generate_predictions(lowercase__    ,       lowercase__    ,       lowercase__      )
						assert (
						    len(lowercase__      ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__      )}'''


def        _UpperCamelCase							(      lowercase__ = False    ,       lowercase__ = False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		evaluate.load('''glue'''    ,       '''mrpc'''      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[Any]					=		get_mrpc_setup(lowercase__    ,       lowercase__      )
						# First do baseline
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		setup['''no''']
						model.to(lowercase__      )
						model.eval()
						for batch in dataloader:
												batch.to(lowercase__      )
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		outputs.logits.argmax(dim=-1      )
												metric.add_batch(predictions=lowercase__    ,       references=batch['''labels''']      )
						__SCREAMING_SNAKE_CASE						:     int					=		metric.compute()

						# Then do distributed
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		setup['''ddp''']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE						:     int					=		model(**lowercase__      )
												__SCREAMING_SNAKE_CASE						:     str					=		outputs.logits.argmax(dim=-1      )
												__SCREAMING_SNAKE_CASE						:     Any					=		batch['''labels''']
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		accelerator.gather_for_metrics((preds, references)      )
												metric.add_batch(predictions=lowercase__    ,       references=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]    ,       distributed[key]      ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('''**Testing gather_for_metrics**'''      )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''      )
																								test_mrpc(lowercase__    ,       lowercase__      )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test torch metrics**'''      )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		Accelerator(split_batches=lowercase__    ,       dispatch_batches=lowercase__      )
																		if accelerator.is_local_main_process:
																								print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
																		test_torch_metrics(lowercase__    ,       99      )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		Accelerator()
						test_torch_metrics(lowercase__    ,       512      )
						accelerator.state._reset_state()


def        _UpperCamelCase							(      lowercase__      ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()
