"""simple docstring"""


from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     str   							=							['input_features', 'attention_mask']


    def __init__(    self		:     Tuple      , __lowerCAmelCase		:     Any=80      , __lowerCAmelCase		:     Union[str, Any]=1_6000      , __lowerCAmelCase		:     Optional[int]=0.0      , __lowerCAmelCase		:     Union[str, Any]=10      , __lowerCAmelCase		:     Optional[int]=25      , __lowerCAmelCase		:     Tuple="hamming_window"      , __lowerCAmelCase		:     Tuple=32_768.0      , __lowerCAmelCase		:     List[Any]=0.97      , __lowerCAmelCase		:     List[Any]=1.0      , __lowerCAmelCase		:     List[Any]=True      , __lowerCAmelCase		:     int=True      , __lowerCAmelCase		:     Optional[int]=False      , **__lowerCAmelCase		:     List[str]      , ):
      super().__init__(feature_size=__lowerCAmelCase      , sampling_rate=__lowerCAmelCase      , padding_value=__lowerCAmelCase      , **__lowerCAmelCase    )

      _UpperCAmelCase         =					feature_size
      _UpperCAmelCase         =					sampling_rate
      _UpperCAmelCase         =					padding_value
      _UpperCAmelCase         =					hop_length
      _UpperCAmelCase         =					win_length
      _UpperCAmelCase         =					frame_signal_scale
      _UpperCAmelCase         =					preemphasis_coeff
      _UpperCAmelCase         =					mel_floor
      _UpperCAmelCase         =					normalize_means
      _UpperCAmelCase         =					normalize_vars
      _UpperCAmelCase         =					win_function
      _UpperCAmelCase         =					return_attention_mask

      _UpperCAmelCase         =					win_length * sampling_rate // 1000
      _UpperCAmelCase         =					hop_length * sampling_rate // 1000

      _UpperCAmelCase         =					optimal_fft_length(self.sample_size    )
      _UpperCAmelCase         =					(self.n_fft // 2) + 1


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     np.array    ):
      if self.win_function == "hamming_window":
        _UpperCAmelCase         =					window_function(window_length=self.sample_size      , name=self.win_function      , periodic=__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					window_function(window_length=self.sample_size      , name=self.win_function    )

      _UpperCAmelCase         =					mel_filter_bank(
          num_frequency_bins=self.n_freqs      , num_mel_filters=self.feature_size      , min_frequency=0.0      , max_frequency=self.sampling_rate / 2.0      , sampling_rate=self.sampling_rate      , )

      _UpperCAmelCase         =					spectrogram(
          one_waveform * self.frame_signal_scale      , window=__lowerCAmelCase      , frame_length=self.sample_size      , hop_length=self.sample_stride      , fft_length=self.n_fft      , center=__lowerCAmelCase      , preemphasis=self.preemphasis_coeff      , mel_filters=__lowerCAmelCase      , mel_floor=self.mel_floor      , log_mel="""log"""      , )
      return msfc_features.T


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int    ):
      # make sure we normalize float32 arrays
      if self.normalize_means:
        _UpperCAmelCase         =					x[:input_length].mean(axis=0    )
        _UpperCAmelCase         =					np.subtract(__lowerCAmelCase      , __lowerCAmelCase    )
      if self.normalize_vars:
        _UpperCAmelCase         =					x[:input_length].std(axis=0    )
        _UpperCAmelCase         =					np.divide(__lowerCAmelCase      , __lowerCAmelCase    )

      if input_length < x.shape[0]:
        _UpperCAmelCase         =					padding_value

      # make sure array is in float32
      _UpperCAmelCase         =					x.astype(np.floataa    )

      return x


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     List[np.ndarray]      , __lowerCAmelCase		:     Optional[np.ndarray] = None    ):
      _UpperCAmelCase         =					attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [self._normalize_one(__lowerCAmelCase      , __lowerCAmelCase      , self.padding_value    ) for x, n in zip(__lowerCAmelCase      , __lowerCAmelCase    )]


    def __call__(    self		:     Dict      , __lowerCAmelCase		:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      , __lowerCAmelCase		:     Union[bool, str, PaddingStrategy] = False      , __lowerCAmelCase		:     Optional[int] = None      , __lowerCAmelCase		:     bool = False      , __lowerCAmelCase		:     Optional[int] = None      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     Optional[Union[str, TensorType]] = None      , __lowerCAmelCase		:     Optional[int] = None      , **__lowerCAmelCase		:     Optional[int]      , ):

      if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
          raise ValueError(
              f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
              f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
              f''' {self.sampling_rate} and not {sampling_rate}.'''    )
      else:
        logger.warning(
            """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
            """Failing to do so can result in silent errors that might be hard to debug."""    )

      _UpperCAmelCase         =					isinstance(__lowerCAmelCase      , np.ndarray    ) and len(raw_speech.shape    ) > 1
      if is_batched_numpy and len(raw_speech.shape    ) > 2:
        raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''    )
      _UpperCAmelCase         =					is_batched_numpy or (
          isinstance(__lowerCAmelCase      , (list, tuple)    ) and (isinstance(raw_speech[0]      , (np.ndarray, tuple, list)    ))
      )

      if is_batched:
        _UpperCAmelCase         =					[np.asarray(__lowerCAmelCase      , dtype=np.floataa    ) for speech in raw_speech]
      elif not is_batched and not isinstance(__lowerCAmelCase      , np.ndarray    ):
        _UpperCAmelCase         =					np.asarray(__lowerCAmelCase      , dtype=np.floataa    )
      elif isinstance(__lowerCAmelCase      , np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
        _UpperCAmelCase         =					raw_speech.astype(np.floataa    )

      # always return batch
      if not is_batched:
        _UpperCAmelCase         =					[raw_speech]

      # extract fbank features
      _UpperCAmelCase         =					[self._extract_mfsc_features(__lowerCAmelCase    ) for one_waveform in raw_speech]

      # convert into correct format for padding
      _UpperCAmelCase         =					BatchFeature({"""input_features""": features}    )

      _UpperCAmelCase         =					self.pad(
          __lowerCAmelCase      , padding=__lowerCAmelCase      , max_length=__lowerCAmelCase      , truncation=__lowerCAmelCase      , pad_to_multiple_of=__lowerCAmelCase      , return_attention_mask=__lowerCAmelCase      , **__lowerCAmelCase      , )
      # make sure list is in array format
      _UpperCAmelCase         =					padded_inputs.get("""input_features"""    )
      if isinstance(input_features[0]      , __lowerCAmelCase    ):
        _UpperCAmelCase         =					[np.asarray(__lowerCAmelCase      , dtype=np.floataa    ) for feature in input_features]

      _UpperCAmelCase         =					padded_inputs.get("""attention_mask"""    )
      if attention_mask is not None:
        _UpperCAmelCase         =					[np.asarray(__lowerCAmelCase      , dtype=np.intaa    ) for array in attention_mask]

      if self.normalize_means or self.normalize_vars:
        _UpperCAmelCase         =					(
            np.array(__lowerCAmelCase      , dtype=np.intaa    )
            if self._get_padding_strategies(__lowerCAmelCase      , max_length=__lowerCAmelCase    ) is not PaddingStrategy.DO_NOT_PAD
            and padding
            else None
        )
        _UpperCAmelCase         =					self.normalize(
            padded_inputs["""input_features"""]      , attention_mask=__lowerCAmelCase    )

      if return_tensors is not None:
        _UpperCAmelCase         =					padded_inputs.convert_to_tensors(__lowerCAmelCase    )

      return padded_inputs

"""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
#
########################################################################


UpperCAmelCase__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







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

  def tokenize_function(lowercase						):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCAmelCase         =					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():
    _UpperCAmelCase         =					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
  _UpperCAmelCase         =					tokenized_datasets.rename_column("""label"""      ,"""labels"""						)

  def collate_fn(lowercase						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCAmelCase         =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _UpperCAmelCase         =					16
    elif accelerator.mixed_precision != "no":
      _UpperCAmelCase         =					8
    else:
      _UpperCAmelCase         =					None

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

  # Instantiate dataloaders.
  _UpperCAmelCase         =					DataLoader(
      tokenized_datasets["""train"""]      ,shuffle=lowercase      ,collate_fn=lowercase      ,batch_size=lowercase						)
  _UpperCAmelCase         =					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

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "1":
    _UpperCAmelCase         =					2
  # Initialize accelerator
  _UpperCAmelCase         =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCAmelCase         =					config["""lr"""]
  _UpperCAmelCase         =					int(config["""num_epochs"""]						)
  _UpperCAmelCase         =					int(config["""seed"""]						)
  _UpperCAmelCase         =					int(config["""batch_size"""]						)

  _UpperCAmelCase         =					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=lowercase						)
  def inner_training_loop(lowercase						):
    # 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(lowercase						)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase         =					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).
    _UpperCAmelCase         =					model.to(accelerator.device						)

    # Instantiate optimizer
    _UpperCAmelCase         =					AdamW(params=model.parameters()      ,lr=lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,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.
    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					accelerator.prepare(
        lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						)

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

      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():
          _UpperCAmelCase         =					model(**lowercase						)
        _UpperCAmelCase         =					outputs.logits.argmax(dim=-1						)
        _UpperCAmelCase       , _UpperCAmelCase         =					accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
        metric.add_batch(
            predictions=lowercase      ,references=lowercase      ,)

      _UpperCAmelCase         =					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''      ,lowercase						)

    # 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 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					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."""      ,)
  parser.add_argument("""--cpu"""      ,action="""store_true"""      ,help="""If passed, will train on the CPU."""						)
  _UpperCAmelCase         =					parser.parse_args()
  _UpperCAmelCase         =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase      ,lowercase						)


if __name__ == "__main__":
    main()
