



'''simple docstring'''





import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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_	(				_lowerCAmelCase			,_lowerCAmelCase = 16			) -> Tuple:
  __lowerCamelCase			:			str       			=					AutoTokenizer.from_pretrained('bert-base-cased'			)
  __lowerCamelCase			:			Union[str, Any]       			=					load_dataset('glue'			,'mrpc'			)

  def tokenize_function(_lowerCAmelCase			):
    # max_length=None => use the model max length (it's actually the default)
    __lowerCamelCase			:			List[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():
    __lowerCamelCase			:			Tuple       			=					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
  __lowerCamelCase			:			int       			=					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.
    __lowerCamelCase			:			Tuple       			=					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":
      __lowerCamelCase			:			Dict       			=					16
    elif accelerator.mixed_precision != "no":
      __lowerCamelCase			:			int       			=					8
    else:
      __lowerCamelCase			:			Optional[int]       			=					None

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

  # Instantiate dataloaders.
  __lowerCamelCase			:			int       			=					DataLoader(
      tokenized_datasets['train']			,shuffle=_lowerCAmelCase			,collate_fn=_lowerCAmelCase			,batch_size=_lowerCAmelCase			,drop_last=_lowerCAmelCase			)
  __lowerCamelCase			:			Union[str, Any]       			=					DataLoader(
      tokenized_datasets['validation']			,shuffle=_lowerCAmelCase			,collate_fn=_lowerCAmelCase			,batch_size=_lowerCAmelCase			,drop_last=(accelerator.mixed_precision == 'fp8')			,)

  return train_dataloader, eval_dataloader

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  # Initialize accelerator
  __lowerCamelCase			:			str       			=					Accelerator(cpu=args.cpu			,mixed_precision=args.mixed_precision			)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __lowerCamelCase			:			int       			=					config['lr']
  __lowerCamelCase			:			Tuple       			=					int(config['num_epochs']			)
  __lowerCamelCase			:			Tuple       			=					int(config['seed']			)
  __lowerCamelCase			:			List[Any]       			=					int(config['batch_size']			)

  __lowerCamelCase			:			List[Any]       			=					evaluate.load('glue'			,'mrpc'			)

  # If the batch size is too big we use gradient accumulation
  __lowerCamelCase			:			Optional[Any]       			=					1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    __lowerCamelCase			:			Tuple       			=					batch_size // MAX_GPU_BATCH_SIZE
    __lowerCamelCase			:			Optional[Any]       			=					MAX_GPU_BATCH_SIZE

  set_seed(_lowerCAmelCase			)
  __lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					get_dataloaders(_lowerCAmelCase			,_lowerCAmelCase			)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __lowerCamelCase			:			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).
  __lowerCamelCase			:			Optional[int]       			=					model.to(accelerator.device			)
  # Instantiate optimizer
  __lowerCamelCase			:			List[Any]       			=					AdamW(params=model.parameters()			,lr=_lowerCAmelCase			)

  # Instantiate scheduler
  __lowerCamelCase			:			List[Any]       			=					get_linear_schedule_with_warmup(
      optimizer=_lowerCAmelCase			,num_warmup_steps=100			,num_training_steps=(len(_lowerCAmelCase			) * num_epochs) // gradient_accumulation_steps			,)

  # 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.

  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			List[str]       			=					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			)
      __lowerCamelCase			:			Dict       			=					model(**_lowerCAmelCase			)
      __lowerCamelCase			:			Union[str, Any]       			=					outputs.loss
      __lowerCamelCase			:			str       			=					loss / gradient_accumulation_steps
      accelerator.backward(_lowerCAmelCase			)
      if step % gradient_accumulation_steps == 0:
        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():
        __lowerCamelCase			:			Dict       			=					model(**_lowerCAmelCase			)
      __lowerCamelCase			:			List[str]       			=					outputs.logits.argmax(dim=-1			)
      __lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					accelerator.gather_for_metrics((predictions, batch['labels'])			)
      metric.add_batch(
          predictions=_lowerCAmelCase			,references=_lowerCAmelCase			,)

    __lowerCamelCase			:			Optional[Any]       			=					metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'epoch {epoch}:'			,_lowerCAmelCase			)

def 			a_	(				) -> List[str]:
  __lowerCamelCase			:			List[Any]       			=					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.'			)
  __lowerCamelCase			:			Optional[int]       			=					parser.parse_args()
  __lowerCamelCase			:			Union[str, Any]       			=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_lowerCAmelCase			,_lowerCAmelCase			)


if __name__ == "__main__":
   main()











'''simple docstring'''





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
   from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_UpperCamelCase											=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCamelCase											=		256






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""melgan"""]




  def __init__(					self :      Dict     ,      _a :      SpectrogramNotesEncoder     ,      _a :      SpectrogramContEncoder     ,      _a :      TaFilmDecoder     ,      _a :      DDPMScheduler     ,      _a :      OnnxRuntimeModel if is_onnx_available() else Any     ,      )   ->   None:
    super().__init__()

    # From MELGAN
    __lowerCamelCase			:			Any       			=					math.log(1e-5  )  # Matches MelGAN training.
    __lowerCamelCase			:			List[Any]       			=					4.0  # Largest value for most examples
    __lowerCamelCase			:			Tuple       			=					128

    self.register_modules(
        notes_encoder=_a     ,      continuous_encoder=_a     ,      decoder=_a     ,      scheduler=_a     ,      melgan=_a     ,      )




  def 		_lowercase	(					self :      Tuple     ,      _a :      int     ,      _a :      List[Any]=(-1.0, 1.0)     ,      _a :      Any=False  )   ->   Dict:
    __lowerCamelCase			,__lowerCamelCase			:			Any       			=					output_range
    if clip:
      __lowerCamelCase			:			List[Any]       			=					torch.clip(_a     ,      self.min_value     ,      self.max_value  )
    # Scale to [0, 1].
    __lowerCamelCase			:			Union[str, Any]       			=					(features - self.min_value) / (self.max_value - self.min_value)
    # Scale to [min_out, max_out].
    return zero_one * (max_out - min_out) + min_out




  def 		_lowercase	(					self :      Dict     ,      _a :      List[str]     ,      _a :      int=(-1.0, 1.0)     ,      _a :      Dict=False  )   ->   List[str]:
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					input_range
    __lowerCamelCase			:			Optional[Any]       			=					torch.clip(_a     ,      _a     ,      _a  ) if clip else outputs
    # Scale to [0, 1].
    __lowerCamelCase			:			str       			=					(outputs - min_out) / (max_out - min_out)
    # Scale to [self.min_value, self.max_value].
    return zero_one * (self.max_value - self.min_value) + self.min_value




  def 		_lowercase	(					self :      int     ,      _a :      Dict     ,      _a :      List[str]     ,      _a :      Tuple  )   ->   Any:
    __lowerCamelCase			:			Tuple       			=					input_tokens > 0
    __lowerCamelCase			,__lowerCamelCase			:			int       			=					self.notes_encoder(
        encoder_input_tokens=_a     ,      encoder_inputs_mask=_a  )

    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					self.continuous_encoder(
        encoder_inputs=_a     ,      encoder_inputs_mask=_a  )

    return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




  def 		_lowercase	(					self :      Tuple     ,      _a :      Tuple     ,      _a :      List[Any]     ,      _a :      int  )   ->   Dict:
    __lowerCamelCase			:			Any       			=					noise_time
    if not torch.is_tensor(_a  ):
      __lowerCamelCase			:			Union[str, Any]       			=					torch.tensor([timesteps]     ,      dtype=torch.long     ,      device=input_tokens.device  )
    elif torch.is_tensor(_a  ) and len(timesteps.shape  ) == 0:
      __lowerCamelCase			:			List[str]       			=					timesteps[None].to(input_tokens.device  )

    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
    __lowerCamelCase			:			Tuple       			=					timesteps * torch.ones(input_tokens.shape[0]     ,      dtype=timesteps.dtype     ,      device=timesteps.device  )

    __lowerCamelCase			:			int       			=					self.decoder(
        encodings_and_masks=_a     ,      decoder_input_tokens=_a     ,      decoder_noise_time=_a  )
    return logits




  @torch.no_grad()
  def __call__(					self :      Optional[int]     ,      _a :      List[List[int]]     ,      _a :      Optional[torch.Generator] = None     ,      _a :      int = 100     ,      _a :      bool = True     ,      _a :      str = "numpy"     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      )   ->   Union[AudioPipelineOutput, Tuple]:
    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(_a     ,      _a  ) or callback_steps <= 0)
    ):
      raise ValueError(
          f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
          f' {type(_a  )}.'  )

    __lowerCamelCase			:			Optional[int]       			=					np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]     ,      dtype=np.floataa  )
    __lowerCamelCase			:			Dict       			=					np.zeros([1, 0, self.n_dims]     ,      np.floataa  )
    __lowerCamelCase			:			List[Any]       			=					torch.ones((1, TARGET_FEATURE_LENGTH)     ,      dtype=_a     ,      device=self.device  )

    for i, encoder_input_tokens in enumerate(_a  ):
      if i == 0:
        __lowerCamelCase			:			List[str]       			=					torch.from_numpy(pred_mel[:1].copy()  ).to(
            device=self.device     ,      dtype=self.decoder.dtype  )
        # The first chunk has no previous context.
        __lowerCamelCase			:			List[Any]       			=					torch.zeros((1, TARGET_FEATURE_LENGTH)     ,      dtype=_a     ,      device=self.device  )
      else:
        # The full song pipeline does not feed in a context feature, so the mask
        # will be all 0s after the feature converter. Because we know we're
        # feeding in a full context chunk from the previous prediction, set it
        # to all 1s.
        __lowerCamelCase			:			int       			=					ones

      __lowerCamelCase			:			int       			=					self.scale_features(
          _a     ,      output_range=[-1.0, 1.0]     ,      clip=_a  )

      __lowerCamelCase			:			Tuple       			=					self.encode(
          input_tokens=torch.IntTensor([encoder_input_tokens]  ).to(device=self.device  )     ,      continuous_inputs=_a     ,      continuous_mask=_a     ,      )

      # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
      __lowerCamelCase			:			Optional[int]       			=					randn_tensor(
          shape=encoder_continuous_inputs.shape     ,      generator=_a     ,      device=self.device     ,      dtype=self.decoder.dtype     ,      )

      # set step values
      self.scheduler.set_timesteps(_a  )

      # Denoising diffusion loop
      for j, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
        __lowerCamelCase			:			List[Any]       			=					self.decode(
            encodings_and_masks=_a     ,      input_tokens=_a     ,      noise_time=t / self.scheduler.config.num_train_timesteps     ,      )

        # Compute previous output: x_t -> x_t-1
        __lowerCamelCase			:			Optional[int]       			=					self.scheduler.step(_a     ,      _a     ,      _a     ,      generator=_a  ).prev_sample

      __lowerCamelCase			:			List[Any]       			=					self.scale_to_features(_a     ,      input_range=[-1.0, 1.0]  )
      __lowerCamelCase			:			Union[str, Any]       			=					mel[:1]
      __lowerCamelCase			:			Union[str, Any]       			=					mel.cpu().float().numpy()

      __lowerCamelCase			:			Dict       			=					np.concatenate([full_pred_mel, pred_mel[:1]]     ,      axis=1  )

      # call the callback, if provided
      if callback is not None and i % callback_steps == 0:
        callback(_a     ,      _a  )

      logger.info('Generated segment'     ,      _a  )

    if output_type == "numpy" and not is_onnx_available():
      raise ValueError(
          'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'  )
    elif output_type == "numpy" and self.melgan is None:
      raise ValueError(
          'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'  )

    if output_type == "numpy":
      __lowerCamelCase			:			Tuple       			=					self.melgan(input_features=full_pred_mel.astype(np.floataa  )  )
    else:
      __lowerCamelCase			:			List[str]       			=					full_pred_mel

    if not return_dict:
      return (output,)

    return AudioPipelineOutput(audios=_a  )






