


import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


A   =			16
A   =			32





def __UpperCAmelCase							(		__A			,    __A = 1_6			,    __A = "bert-base-cased"      )    ->       int:



       '''simple docstring'''





       UpperCAmelCase__					    =  AutoTokenizer.from_pretrained(_A      )
       UpperCAmelCase__					    =  load_dataset("glue"			,    "mrpc"      )

       def tokenize_function(__A      ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase__					    =  tokenizer(examples["sentence1"]			,    examples["sentence2"]			,    truncation=_A			,    max_length=_A      )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       UpperCAmelCase__					    =  datasets.map(
           _A			,    batched=_A			,    remove_columns=["idx", "sentence1", "sentence2"]			,    load_from_cache_file=_A      )

       # 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(__A      ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(_A			,    padding="max_length"			,    max_length=1_2_8			,    return_tensors="pt"      )
              return tokenizer.pad(_A			,    padding="longest"			,    return_tensors="pt"      )

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

       return train_dataloader, eval_dataloader





def __UpperCAmelCase							(		__A			,    __A      )    ->       Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__					    =  Accelerator()

       # 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__					    =  args.model_name_or_path

       set_seed(_A      )
       UpperCAmelCase__    ,      UpperCAmelCase__					    =  get_dataloaders(_A			,    _A			,    _A      )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase__					    =  AutoModelForSequenceClassification.from_pretrained(_A			,    return_dict=_A      )

       # Instantiate optimizer
       UpperCAmelCase__					    =  (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       UpperCAmelCase__					    =  optimizer_cls(params=model.parameters()			,    lr=_A      )

       if accelerator.state.deepspeed_plugin is not None:
              UpperCAmelCase__					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
                  "gradient_accumulation_steps"
              ]
       else:
              UpperCAmelCase__					    =  1
       UpperCAmelCase__					    =  (len(_A      ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              UpperCAmelCase__					    =  get_linear_schedule_with_warmup(
                  optimizer=_A			,    num_warmup_steps=0			,    num_training_steps=_A			,    )
       else:
              UpperCAmelCase__					    =  DummyScheduler(_A			,    total_num_steps=_A			,    warmup_num_steps=0      )

       # 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(
           _A			,    _A			,    _A			,    _A			,    _A      )

       # We need to keep track of how many total steps we have iterated over
       UpperCAmelCase__					    =  0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCAmelCase__					    =  0

       # Now we train the model
       UpperCAmelCase__					    =  evaluate.load("glue"			,    "mrpc"      )
       UpperCAmelCase__					    =  0
       UpperCAmelCase__					    =  {}
       for epoch in range(_A			,    _A      ):
              model.train()
              for step, batch in enumerate(_A      ):
                     UpperCAmelCase__					    =  model(**_A      )
                     UpperCAmelCase__					    =  outputs.loss
                     UpperCAmelCase__					    =  loss / gradient_accumulation_steps
                     accelerator.backward(_A      )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              UpperCAmelCase__					    =  0
              for step, batch in enumerate(_A      ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device      )
                     with torch.no_grad():
                            UpperCAmelCase__					    =  model(**_A      )
                     UpperCAmelCase__					    =  outputs.logits.argmax(dim=-1      )
                     # It is slightly faster to call this once, than multiple times
                     UpperCAmelCase__    ,      UpperCAmelCase__					    =  accelerator.gather(
                         (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(_A      ) - 1:
                                   UpperCAmelCase__					    =  predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                                   UpperCAmelCase__					    =  references[: len(eval_dataloader.dataset      ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=_A			,    references=_A			,    )

              UpperCAmelCase__					    =  metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""			,    _A      )
              UpperCAmelCase__					    =  eval_metric["accuracy"]

              if best_performance < eval_metric["accuracy"]:
                     UpperCAmelCase__					    =  eval_metric["accuracy"]

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir			,    "all_results.json"      )			,    "w"      ) as f:
                     json.dump(_A			,    _A      )





def __UpperCAmelCase							(		)    ->       Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__					    =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
       parser.add_argument(
           "--model_name_or_path"			,    type=_A			,    default="bert-base-cased"			,    help="Path to pretrained model or model identifier from huggingface.co/models."			,    required=_A			,    )
       parser.add_argument(
           "--output_dir"			,    type=_A			,    default="."			,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,    )
       parser.add_argument(
           "--performance_lower_bound"			,    type=_A			,    default=_A			,    help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."			,    )
       parser.add_argument(
           "--num_epochs"			,    type=_A			,    default=3			,    help="Number of train epochs."			,    )
       UpperCAmelCase__					    =  parser.parse_args()
       UpperCAmelCase__					    =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
       training_function(_A			,    _A      )


if __name__ == "__main__":
    main()






'''simple docstring'''




import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


__lowercase        =    logging.get_logger(__name__)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Dict						=			['''input_values''', '''attention_mask''']



			def __init__(  self			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = 16000			,       __lowerCAmelCase = 0.0			,       __lowerCAmelCase = False			,       __lowerCAmelCase = 80			,       __lowerCAmelCase = 16			,       __lowerCAmelCase = 64			,       __lowerCAmelCase = "hann_window"			,       __lowerCAmelCase = 1.0			,       __lowerCAmelCase = 80			,       __lowerCAmelCase = 7600			,       __lowerCAmelCase = 1E-1_0			,       __lowerCAmelCase = 2			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(feature_size=__lowerCAmelCase			,       sampling_rate=__lowerCAmelCase			,       padding_value=__lowerCAmelCase			,       **__lowerCAmelCase)
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    return_attention_mask

					lowerCAmelCase     =    num_mel_bins
					lowerCAmelCase     =    hop_length
					lowerCAmelCase     =    win_length
					lowerCAmelCase     =    win_function
					lowerCAmelCase     =    frame_signal_scale
					lowerCAmelCase     =    fmin
					lowerCAmelCase     =    fmax
					lowerCAmelCase     =    mel_floor
					lowerCAmelCase     =    reduction_factor

					lowerCAmelCase     =    win_length * sampling_rate // 1000
					lowerCAmelCase     =    hop_length * sampling_rate // 1000
					lowerCAmelCase     =    optimal_fft_length(self.sample_size)
					lowerCAmelCase     =    (self.n_fft // 2) + 1

					lowerCAmelCase     =    window_function(window_length=self.sample_size			,       name=self.win_function			,       periodic=__lowerCAmelCase)

					lowerCAmelCase     =    mel_filter_bank(
					    num_frequency_bins=self.n_freqs			,       num_mel_filters=self.num_mel_bins			,       min_frequency=self.fmin			,       max_frequency=self.fmax			,       sampling_rate=self.sampling_rate			,       norm="""slaney"""			,       mel_scale="""slaney"""			,       )

					if frame_signal_scale != 1.0:
							warnings.warn(
							    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,       __lowerCAmelCase			,       )
					if reduction_factor != 2.0:
							warnings.warn(
							    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,       __lowerCAmelCase			,       )



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 				a_			(  __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = 0.0):




					"""simple docstring"""


					if attention_mask is not None:
							lowerCAmelCase     =    np.array(__lowerCAmelCase			,       np.intaa)
							lowerCAmelCase     =    []

							for vector, length in zip(__lowerCAmelCase			,       attention_mask.sum(-1)):
									lowerCAmelCase     =    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
									if length < normed_slice.shape[0]:
											lowerCAmelCase     =    padding_value

									normed_input_values.append(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

					return normed_input_values



			def 				a_			(  self			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    spectrogram(
					    __lowerCAmelCase			,       window=self.window			,       frame_length=self.sample_size			,       hop_length=self.sample_stride			,       fft_length=self.n_fft			,       mel_filters=self.mel_filters			,       mel_floor=self.mel_floor			,       log_mel="""log10"""			,       )
					return log_mel_spec.T



			def __call__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					if audio is None and audio_target is None:
							raise ValueError("""You must provide either `audio` or `audio_target` values.""")

					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 audio 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.""")

					if audio is not None:
							lowerCAmelCase     =    self._process_audio(
							    __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       **__lowerCAmelCase			,       )
					else:
							lowerCAmelCase     =    None

					if audio_target is not None:
							lowerCAmelCase     =    self._process_audio(
							    __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       **__lowerCAmelCase			,       )

							if inputs is None:
									return inputs_target
							else:
									lowerCAmelCase     =    inputs_target["""input_values"""]
									lowerCAmelCase     =    inputs_target.get("""attention_mask""")
									if decoder_attention_mask is not None:
											lowerCAmelCase     =    decoder_attention_mask

					return inputs



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


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

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

					# always return batch
					if not is_batched:
							lowerCAmelCase     =    [speech]

					# needed to make pad() work on spectrogram inputs
					lowerCAmelCase     =    self.feature_size

					# convert into correct format for padding
					if is_target:
							lowerCAmelCase     =    [self._extract_mel_features(__lowerCAmelCase) for waveform in speech]
							lowerCAmelCase     =    BatchFeature({"""input_values""": features})
							lowerCAmelCase     =    self.num_mel_bins
					else:
							lowerCAmelCase     =    BatchFeature({"""input_values""": speech})

					lowerCAmelCase     =    self.pad(
					    __lowerCAmelCase			,       padding=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       **__lowerCAmelCase			,       )

					lowerCAmelCase     =    feature_size_hack

					# convert input values to correct format
					lowerCAmelCase     =    padded_inputs["""input_values"""]
					if not isinstance(input_values[0]			,       np.ndarray):
							lowerCAmelCase     =    [np.asarray(__lowerCAmelCase			,       dtype=np.floataa) for array in input_values]
					elif (
					    not isinstance(__lowerCAmelCase			,       np.ndarray)
					    and isinstance(input_values[0]			,       np.ndarray)
					    and input_values[0].dtype is np.dtype(np.floataa)
					):
							lowerCAmelCase     =    [array.astype(np.floataa) for array in input_values]
					elif isinstance(__lowerCAmelCase			,       np.ndarray) and input_values.dtype is np.dtype(np.floataa):
							lowerCAmelCase     =    input_values.astype(np.floataa)

					# convert attention_mask to correct format
					lowerCAmelCase     =    padded_inputs.get("""attention_mask""")
					if attention_mask is not None:
							lowerCAmelCase     =    [np.asarray(__lowerCAmelCase			,       dtype=np.intaa) for array in attention_mask]

					# zero-mean and unit-variance normalization
					if not is_target and self.do_normalize:
							lowerCAmelCase     =    (
							    attention_mask
							    if self._get_padding_strategies(__lowerCAmelCase			,       max_length=__lowerCAmelCase) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							lowerCAmelCase     =    self.zero_mean_unit_var_norm(
							    padded_inputs["""input_values"""]			,       attention_mask=__lowerCAmelCase			,       padding_value=self.padding_value)

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

					return padded_inputs







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    super().to_dict()

					# Don't serialize these as they are derived from the other properties.
					lowerCAmelCase     =    ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
					for name in names:
							if name in output:
									del output[name]

					return output




