






'''simple docstring'''







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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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     :      List[Any]   					=16
_A     :      List[str]   					=32





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = 16				)				->	Any:
       lowerCamelCase__ :     Union[str, Any]	  =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
       lowerCamelCase__ :     Dict	  =	load_dataset("""glue"""    ,      """mrpc"""				)

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

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

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

       # Instantiate dataloaders.
       lowerCamelCase__ :     List[Any]	  =	DataLoader(
           tokenized_datasets["""train"""]    ,      shuffle=UpperCamelCase    ,      collate_fn=UpperCamelCase    ,      batch_size=UpperCamelCase				)
       lowerCamelCase__ :     List[Any]	  =	DataLoader(
           tokenized_datasets["""validation"""]    ,      shuffle=UpperCamelCase    ,      collate_fn=UpperCamelCase    ,      batch_size=UpperCamelCase				)

       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     :      List[Any]   					=mocked_dataloaders  # noqa: F811





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

       lowerCamelCase__ :     List[Any]	  =	evaluate.load("""glue"""    ,      """mrpc"""				)

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

       set_seed(UpperCamelCase				)
       lowerCamelCase__ :     Optional[Any]	  =	get_dataloaders(UpperCamelCase    ,      UpperCamelCase				)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCamelCase__ :     List[str]	  =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,      return_dict=UpperCamelCase				)

       # 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__ :     List[Any]	  =	model.to(accelerator.device				)

       # Instantiate optimizer
       lowerCamelCase__ :     Union[str, Any]	  =	AdamW(params=model.parameters()    ,      lr=UpperCamelCase				)

       # Instantiate scheduler
       lowerCamelCase__ :     List[str]	  =	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase    ,      num_warmup_steps=100    ,      num_training_steps=(len(UpperCamelCase				) * 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__ :     Tuple	  =	accelerator.prepare(
           UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

       # Now we train the model
       for epoch in range(UpperCamelCase				):
              model.train()
              for step, batch in enumerate(UpperCamelCase				):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device				)
                     lowerCamelCase__ :     Any	  =	model(**UpperCamelCase				)
                     lowerCamelCase__ :     Union[str, Any]	  =	outputs.loss
                     lowerCamelCase__ :     str	  =	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase				)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              lowerCamelCase__ :     Any	  =	0
              for step, batch in enumerate(UpperCamelCase				):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device				)
                     with torch.no_grad():
                            lowerCamelCase__ :     int	  =	model(**UpperCamelCase				)
                     lowerCamelCase__ :     List[str]	  =	outputs.logits.argmax(dim=-1				)
                     lowerCamelCase__ :     Any	  =	accelerator.gather((predictions, batch["""labels"""])				)
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(UpperCamelCase				) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCamelCase__ :     Union[str, Any]	  =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
                                   lowerCamelCase__ :     List[Any]	  =	references[: len(eval_dataloader.dataset				) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=UpperCamelCase    ,      references=UpperCamelCase    ,      )

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





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
       lowerCamelCase__ :     Dict	  =	argparse.ArgumentParser(description="""Simple example of training script."""				)
       parser.add_argument(
           """--mixed_precision"""    ,      type=UpperCamelCase    ,      default=UpperCamelCase    ,      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__ :     Tuple	  =	parser.parse_args()
       lowerCamelCase__ :     Dict	  =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(UpperCamelCase    ,      UpperCamelCase				)


if __name__ == "__main__":
   main()









'''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


_A     :      List[str]   					=logging.get_logger(__name__)

class _lowercase     (    _lowercase		):
							a    				=	["""input_features""", """attention_mask"""]




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    str=80      ,							UpperCamelCase__:    Union[str, Any]=16_000      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Dict=25      ,							UpperCamelCase__:    List[Any]="hamming_window"      ,							UpperCamelCase__:    Tuple=32_768.0      ,							UpperCamelCase__:    List[Any]=0.97      ,							UpperCamelCase__:    Optional[int]=1.0      ,							UpperCamelCase__:    str=True      ,							UpperCamelCase__:    Optional[Any]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							**UpperCamelCase__:    Optional[int]      ,							):
														super().__init__(feature_size=UpperCamelCase__      ,							sampling_rate=UpperCamelCase__      ,							padding_value=UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     Dict	  =	feature_size
														lowerCamelCase__ :     Tuple	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     Union[str, Any]	  =	hop_length
														lowerCamelCase__ :     Any	  =	win_length
														lowerCamelCase__ :     Tuple	  =	frame_signal_scale
														lowerCamelCase__ :     Dict	  =	preemphasis_coeff
														lowerCamelCase__ :     Optional[Any]	  =	mel_floor
														lowerCamelCase__ :     Any	  =	normalize_means
														lowerCamelCase__ :     Optional[int]	  =	normalize_vars
														lowerCamelCase__ :     Dict	  =	win_function
														lowerCamelCase__ :     Dict	  =	return_attention_mask

														lowerCamelCase__ :     Optional[Any]	  =	win_length * sampling_rate // 1_000
														lowerCamelCase__ :     Tuple	  =	hop_length * sampling_rate // 1_000

														lowerCamelCase__ :     List[Any]	  =	optimal_fft_length(self.sample_size					)
														lowerCamelCase__ :     List[str]	  =	(self.n_fft // 2) + 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    np.array					):
														if self.win_function == "hamming_window":
																					lowerCamelCase__ :     Dict	  =	window_function(window_length=self.sample_size      ,							name=self.win_function      ,							periodic=UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	window_function(window_length=self.sample_size      ,							name=self.win_function					)

														lowerCamelCase__ :     List[Any]	  =	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      ,							)

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




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														# make sure we normalize float32 arrays
														if self.normalize_means:
																					lowerCamelCase__ :     List[str]	  =	x[:input_length].mean(axis=0					)
																					lowerCamelCase__ :     List[str]	  =	np.subtract(UpperCamelCase__      ,							UpperCamelCase__					)
														if self.normalize_vars:
																					lowerCamelCase__ :     Union[str, Any]	  =	x[:input_length].std(axis=0					)
																					lowerCamelCase__ :     str	  =	np.divide(UpperCamelCase__      ,							UpperCamelCase__					)

														if input_length < x.shape[0]:
																					lowerCamelCase__ :     List[Any]	  =	padding_value

														# make sure array is in float32
														lowerCamelCase__ :     str	  =	x.astype(np.floataa					)

														return x




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




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

														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."""					)

														lowerCamelCase__ :     List[str]	  =	isinstance(UpperCamelCase__      ,							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}'''					)
														lowerCamelCase__ :     List[str]	  =	is_batched_numpy or (
														    isinstance(UpperCamelCase__      ,							(list, tuple)					) and (isinstance(raw_speech[0]      ,							(np.ndarray, tuple, list)					))
														)

														if is_batched:
																					lowerCamelCase__ :     str	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.floataa					) for speech in raw_speech]
														elif not is_batched and not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					lowerCamelCase__ :     List[Any]	  =	np.asarray(UpperCamelCase__      ,							dtype=np.floataa					)
														elif isinstance(UpperCamelCase__      ,							np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																					lowerCamelCase__ :     Optional[Any]	  =	raw_speech.astype(np.floataa					)

														# always return batch
														if not is_batched:
																					lowerCamelCase__ :     Optional[Any]	  =	[raw_speech]

														# extract fbank features
														lowerCamelCase__ :     str	  =	[self._extract_mfsc_features(UpperCamelCase__					) for one_waveform in raw_speech]

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

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

														lowerCamelCase__ :     List[str]	  =	padded_inputs.get("""attention_mask"""					)
														if attention_mask is not None:
																					lowerCamelCase__ :     Any	  =	[np.asarray(UpperCamelCase__      ,							dtype=np.intaa					) for array in attention_mask]

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

														if return_tensors is not None:
																					lowerCamelCase__ :     Dict	  =	padded_inputs.convert_to_tensors(UpperCamelCase__					)

														return padded_inputs

