



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


UpperCAmelCase							 =					"""base_with_context"""

def 			A_      (	__a     :				Dict ,		__a     :				str		):

				"""simple docstring"""
				a__				      =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]		)		)
				a__				      =		nn.Parameter(
				    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		) ,		requires_grad=__a		)
				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 			A_      (	__a     :				str ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T		)		)

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

				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 			A_      (	__a     :				Optional[Any] ,		__a     :				Tuple		):

				"""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=__a		)

				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 			A_      (	__a     :				Any		):

				"""simple docstring"""
				a__				      =		checkpoints.load_tax_checkpoint(args.checkpoint_path		)
				a__				      =		jnp.tree_util.tree_map(onp.array ,		__a		)

				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(__a ,		__a		)
				a__				      =		inference.InferenceModel(args.checkpoint_path ,		__a		)

				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"""] ,		__a		)
				a__				      =		load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] ,		__a		)
				a__				      =		load_decoder(ta_checkpoint["""target"""]["""decoder"""] ,		__a		)

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

				a__				      =		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__":
	UpperCAmelCase							 =					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.""",
	)
	UpperCAmelCase							 =					parser.parse_args()

	main(args)






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase							 =					16
UpperCAmelCase							 =					32

def 			A_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

				def tokenize_function(__a     :				int		):
								# max_length=None => use the model max length (it's actually the default)
								a__				      =		tokenizer(examples["""sentence1"""] ,		examples["""sentence2"""] ,		truncation=__a ,		max_length=__a		)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								a__				      =		datasets.map(
								    __a ,		batched=__a ,		remove_columns=["""idx""", """sentence1""", """sentence2"""] ,		)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				a__				      =		tokenized_datasets.rename_column("""label""" ,		"""labels"""		)

				def collate_fn(__a     :				Optional[int]		):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								a__				      =		128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												a__				      =		16
								elif accelerator.mixed_precision != "no":
												a__				      =		8
								else:
												a__				      =		None

								return tokenizer.pad(
								    __a ,		padding="""longest""" ,		max_length=__a ,		pad_to_multiple_of=__a ,		return_tensors="""pt""" ,		)

				# Instantiate dataloaders.
				a__				      =		DataLoader(
				    tokenized_datasets["""train"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)
				a__				      =		DataLoader(
				    tokenized_datasets["""validation"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	UpperCAmelCase							 =					mocked_dataloaders  # noqa: F811

def 			A_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "1":
								a__				      =		2
				# New Code #
				a__				      =		int(args.gradient_accumulation_steps		)
				a__				      =		int(args.local_sgd_steps		)
				# Initialize accelerator
				a__				      =		Accelerator(
				    cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		gradient_accumulation_steps=__a		)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				a__				      =		config["""lr"""]
				a__				      =		int(config["""num_epochs"""]		)
				a__				      =		int(config["""seed"""]		)
				a__				      =		int(config["""batch_size"""]		)

				a__				      =		evaluate.load("""glue""" ,		"""mrpc"""		)

				set_seed(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a__				      =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,		return_dict=__a		)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				a__				      =		model.to(accelerator.device		)

				# Instantiate optimizer
				a__				      =		AdamW(params=model.parameters() ,		lr=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * num_epochs) ,		)

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# Now we train the model
				for epoch in range(__a		):
								model.train()
								with LocalSGD(
								    accelerator=__a ,		model=__a ,		local_sgd_steps=__a ,		enabled=local_sgd_steps is not None		) as local_sgd:
												for step, batch in enumerate(__a		):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device		)
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__a		):
																				a__				      =		model(**__a		)
																				a__				      =		output.loss
																				accelerator.backward(__a		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(__a		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																a__				      =		model(**__a		)
												a__				      =		outputs.logits.argmax(dim=-1		)
												a__      ,			a__				      =		accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
												metric.add_batch(
												    predictions=__a ,		references=__a ,		)

								a__				      =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:''' ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		choices=["""no""", """fp16""", """bf16""", """fp8"""] ,		help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU.""" ,		)
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps""" ,		type=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		default=8 ,		help="""Number of local SGD steps or None to disable local SGD"""		)
				parser.add_argument("""--cpu""" ,		action="""store_true""" ,		help="""If passed, will train on the CPU."""		)
				a__				      =		parser.parse_args()
				a__				      =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(__a ,		__a		)


if __name__ == "__main__":
	main()

