





"""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


__magic_name__       :					int						=			"""base_with_context"""

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

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

												UpperCamelCase				:		List[str]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]     )     )

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

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

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

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

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

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

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

						UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]     )     )

						return model

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

						UpperCamelCase				:		Optional[Any]				   =			nn.Parameter(
						    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]     )							,     requires_grad=SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(
						    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T     )     )

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

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

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

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

												UpperCamelCase				:		Optional[Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]     )     )
												UpperCamelCase				:		Dict				   =			nn.Parameter(
												    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T     )     )

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

						UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T     )     )

						return model

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			checkpoints.load_tax_checkpoint(args.checkpoint_path     )
						UpperCamelCase				:		int				   =			jnp.tree_util.tree_map(onp.array							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Tuple				   =			[
						    """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()""",
						]

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

						UpperCamelCase				:		Optional[int]				   =			DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							,     variance_type="""fixed_large"""     )

						UpperCamelCase				:		List[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"""							,     )

						UpperCamelCase				:		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"""							,     )

						UpperCamelCase				:		Optional[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							,     )

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

						UpperCamelCase				:		List[Any]				   =			OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""     )

						UpperCamelCase				:		str				   =			SpectrogramDiffusionPipeline(
						    notes_encoder=SCREAMING_SNAKE_CASE							,     continuous_encoder=SCREAMING_SNAKE_CASE							,     decoder=SCREAMING_SNAKE_CASE							,     scheduler=SCREAMING_SNAKE_CASE							,     melgan=SCREAMING_SNAKE_CASE							,     )
						if args.save:
												pipe.save_pretrained(args.output_path     )


if __name__ == "__main__":
		__magic_name__       :					List[Any]						=			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.""",
		)
		__magic_name__       :					int						=			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
#
########################################################################


__a   :							int						= 16
__a   :							str						= 32



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Accelerator      ,					__lowercase :      int = 1_6     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
    __A       =						load_dataset("""glue"""      ,					"""mrpc"""     )

    def tokenize_function(__lowercase :      Tuple     ):
        # max_length=None => use the model max length (it's actually the default)
        __A       =						tokenizer(examples["""sentence1"""]      ,					examples["""sentence2"""]      ,					truncation=__lowercase      ,					max_length=__lowercase     )
        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(
            __lowercase      ,					batched=__lowercase      ,					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(__lowercase :      List[Any]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __A       =						1_2_8 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       =						1_6
        elif accelerator.mixed_precision != "no":
            __A       =						8
        else:
            __A       =						None

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

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

    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

  __a   :							int						= mocked_dataloaders  # noqa: F811



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      List[Any]     )    ->   Tuple:





    """simple docstring"""

    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,					__lowercase     ) == "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=__lowercase     )
    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(__lowercase     )
    __A     ,						__A       =						get_dataloaders(__lowercase      ,					__lowercase     )
    # 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=__lowercase     )

    # 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=__lowercase     )

    # Instantiate scheduler
    __A       =						get_linear_schedule_with_warmup(
        optimizer=__lowercase      ,					num_warmup_steps=1_0_0      ,					num_training_steps=(len(__lowercase     ) * 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(
        __lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )

    # Now we train the model
    for epoch in range(__lowercase     ):
        model.train()
        with LocalSGD(
            accelerator=__lowercase      ,					model=__lowercase      ,					local_sgd_steps=__lowercase      ,					enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(__lowercase     ):
                # 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(__lowercase     ):
                    __A       =						model(**__lowercase     )
                    __A       =						output.loss
                    accelerator.backward(__lowercase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

        __A       =						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    __A       =						argparse.ArgumentParser(description="""Simple example of training script."""     )
    parser.add_argument(
        """--mixed_precision"""      ,					type=__lowercase      ,					default=__lowercase      ,					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=__lowercase      ,					default=1      ,					help="""The number of minibatches to be ran before gradients are accumulated."""      ,					)
    parser.add_argument(
        """--local_sgd_steps"""      ,					type=__lowercase      ,					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""": 4_2, """batch_size""": 1_6}
    training_function(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()




