



'''simple docstring'''





import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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  			=						16
__a  			=						32





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Union[str, Any]:
      snake_case__    :       List[str]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Tuple       		= load_dataset("""glue"""	,  """mrpc"""						)

      def tokenize_function(_lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__    :       Optional[Any]       		= tokenizer(examples["""sentence1"""]	,  examples["""sentence2"""]	,  truncation=_lowerCAmelCase	,  max_length=_lowerCAmelCase						)
            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():
            snake_case__    :       List[Any]       		= datasets.map(
                _lowerCAmelCase	,  batched=_lowerCAmelCase	,  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
      snake_case__    :       Union[str, Any]       		= tokenized_datasets.rename_column("""label"""	,  """labels"""						)

      def collate_fn(_lowerCAmelCase						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case__    :       List[str]       		= 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":
                  snake_case__    :       Dict       		= 16
            elif accelerator.mixed_precision != "no":
                  snake_case__    :       Optional[Any]       		= 8
            else:
                  snake_case__    :       Dict       		= None

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

      # Instantiate dataloaders.
      snake_case__    :       int       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Dict       		= DataLoader(
          tokenized_datasets["""validation"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)

      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  			=						mocked_dataloaders  # noqa: F811





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[int]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,  _lowerCAmelCase						) == "1":
            snake_case__    :       Tuple       		= 2
      # Initialize accelerator
      snake_case__    :       int       		= Accelerator(cpu=args.cpu	,  mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__    :       List[str]       		= config["""lr"""]
      snake_case__    :       Optional[int]       		= int(config["""num_epochs"""]						)
      snake_case__    :       int       		= int(config["""seed"""]						)
      snake_case__    :       Optional[Any]       		= int(config["""batch_size"""]						)

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

      # New Code #
      # We now can define an inner training loop function. It should take a batch size as the only parameter,
      # and build the dataloaders in there.
      # It also gets our decorator
      @find_executable_batch_size(starting_batch_size=_lowerCAmelCase						)
      def inner_training_loop(_lowerCAmelCase						):
            # And now just move everything below under this function
            # We need to bring in the Accelerator object from earlier
            nonlocal accelerator
            # And reset all of its attributes that could hold onto any memory:
            accelerator.free_memory()

            # Then we can declare the model, optimizer, and everything else:
            set_seed(_lowerCAmelCase						)

            # Instantiate the model (we build the model here so that the seed also control new weights initialization)
            snake_case__    :       str       		= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,  return_dict=_lowerCAmelCase						)

            # 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).
            snake_case__    :       Dict       		= model.to(accelerator.device						)

            # Instantiate optimizer
            snake_case__    :       str       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)
            snake_case__		,							snake_case__    :       List[Any]       		= get_dataloaders(_lowerCAmelCase	,  _lowerCAmelCase						)

            # Instantiate scheduler
            snake_case__    :       Optional[int]       		= get_linear_schedule_with_warmup(
                optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * 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.
            snake_case__		,							snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       Tuple       		= accelerator.prepare(
                _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

            # Now we train the model
            for epoch in range(_lowerCAmelCase						):
                  model.train()
                  for step, batch in enumerate(_lowerCAmelCase						):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device						)
                        snake_case__    :       List[str]       		= model(**_lowerCAmelCase						)
                        snake_case__    :       str       		= outputs.loss
                        accelerator.backward(_lowerCAmelCase						)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

                  snake_case__    :       Union[str, Any]       		= metric.compute()
                  # Use accelerator.print to print only on the main process.
                  accelerator.print(f"epoch {epoch}:"	,  _lowerCAmelCase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
      inner_training_loop()





def 		__snake_case(							)						->					List[Any]:
      snake_case__    :       str       		= argparse.ArgumentParser(description="""Simple example of training script."""						)
      parser.add_argument(
          """--mixed_precision"""	,  type=_lowerCAmelCase	,  default=_lowerCAmelCase	,  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."""	,  )
      parser.add_argument("""--cpu"""	,  action="""store_true"""	,  help="""If passed, will train on the CPU."""						)
      snake_case__    :       str       		= parser.parse_args()
      snake_case__    :       List[str]       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()





'''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


__a  			=						"base_with_context"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
      snake_case__    :       Dict       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)
      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[str]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       Dict       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

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

            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

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

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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

      snake_case__    :       str       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

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

            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

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

      return model





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
      snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

      snake_case__    :       List[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

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

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

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

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

            snake_case__    :       int       		= ly_weight["""MultiHeadDotProductAttention_0"""]
            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

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

      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

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

      return model





def 		__snake_case(							_lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
      snake_case__    :       List[str]       		= jnp.tree_util.tree_map(onp.array	,  _lowerCAmelCase						)

      snake_case__    :       int       		= [
          """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__    :       Any       		= os.path.join(args.checkpoint_path	,  """.."""	,  """config.gin"""						)
      snake_case__    :       Dict       		= inference.parse_training_gin_file(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       int       		= inference.InferenceModel(args.checkpoint_path	,  _lowerCAmelCase						)

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

      snake_case__    :       Union[str, 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"""	,  )

      snake_case__    :       Union[str, 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"""	,  )

      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	,  )

      snake_case__    :       Optional[int]       		= load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Union[str, Any]       		= load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Optional[Any]       		= load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,  _lowerCAmelCase						)

      snake_case__    :       str       		= OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

      snake_case__    :       Dict       		= SpectrogramDiffusionPipeline(
          notes_encoder=_lowerCAmelCase	,  continuous_encoder=_lowerCAmelCase	,  decoder=_lowerCAmelCase	,  scheduler=_lowerCAmelCase	,  melgan=_lowerCAmelCase	,  )
      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)
