





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


_lowerCamelCase      							=   16
_lowerCamelCase      							=   32




def  __UpperCAmelCase(   lowercase_			,     lowercase_ = 16			,     lowercase_ = "bert-base-cased"   ):
       _lowerCamelCase				:  Union[str, Any]						=		AutoTokenizer.from_pretrained(lowercase_   )
       _lowerCamelCase				:  int						=		load_dataset('''glue'''			,     '''mrpc'''   )

       def tokenize_function(lowercase_   ):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase				:  Optional[int]						=		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
       _lowerCamelCase				:  Dict						=		datasets.map(
           lowercase_			,     batched=lowercase_			,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,     load_from_cache_file=lowercase_   )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _lowerCamelCase				:  List[str]						=		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.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase_			,     padding='''max_length'''			,     max_length=1_28			,     return_tensors='''pt'''   )
              return tokenizer.pad(lowercase_			,     padding='''longest'''			,     return_tensors='''pt'''   )

       # Instantiate dataloaders.
       _lowerCamelCase				:  Optional[int]						=		DataLoader(
           tokenized_datasets['''train''']			,     shuffle=lowercase_			,     collate_fn=lowercase_			,     batch_size=lowercase_   )
       _lowerCamelCase				:  Optional[Any]						=		DataLoader(
           tokenized_datasets['''validation''']			,     shuffle=lowercase_			,     collate_fn=lowercase_			,     batch_size=lowercase_   )

       return train_dataloader, eval_dataloader




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       # Initialize accelerator
       _lowerCamelCase				:  Union[str, Any]						=		Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase				:  Optional[Any]						=		config['''lr''']
       _lowerCamelCase				:  str						=		int(config['''num_epochs''']   )
       _lowerCamelCase				:  Dict						=		int(config['''seed''']   )
       _lowerCamelCase				:  Tuple						=		int(config['''batch_size''']   )
       _lowerCamelCase				:  Optional[Any]						=		args.model_name_or_path

       set_seed(lowercase_   )
       _lowerCamelCase,	_lowerCamelCase				:  Optional[int]						=		get_dataloaders(lowercase_			,     lowercase_			,     lowercase_   )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase				:  int						=		AutoModelForSequenceClassification.from_pretrained(lowercase_			,     return_dict=lowercase_   )

       # Instantiate optimizer
       _lowerCamelCase				:  Any						=		(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _lowerCamelCase				:  List[Any]						=		optimizer_cls(params=model.parameters()			,     lr=lowercase_   )

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCamelCase				:  int						=		accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              _lowerCamelCase				:  Optional[int]						=		1
       _lowerCamelCase				:  Any						=		(len(lowercase_   ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              _lowerCamelCase				:  Dict						=		get_linear_schedule_with_warmup(
                  optimizer=lowercase_			,     num_warmup_steps=0			,     num_training_steps=lowercase_			,     )
       else:
              _lowerCamelCase				:  Optional[Any]						=		DummyScheduler(lowercase_			,     total_num_steps=lowercase_			,     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.
       _lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase,	_lowerCamelCase				:  Dict						=		accelerator.prepare(
           lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     lowercase_   )

       # We need to keep track of how many total steps we have iterated over
       _lowerCamelCase				:  List[Any]						=		0
       # We also need to keep track of the stating epoch so files are named properly
       _lowerCamelCase				:  str						=		0

       # Now we train the model
       _lowerCamelCase				:  str						=		evaluate.load('''glue'''			,     '''mrpc'''   )
       _lowerCamelCase				:  Optional[Any]						=		0
       _lowerCamelCase				:  List[Any]						=		{}
       for epoch in range(lowercase_			,     lowercase_   ):
              model.train()
              for step, batch in enumerate(lowercase_   ):
                     _lowerCamelCase				:  Optional[int]						=		model(**lowercase_   )
                     _lowerCamelCase				:  Optional[int]						=		outputs.loss
                     _lowerCamelCase				:  int						=		loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_   )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              _lowerCamelCase				:  str						=		0
              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():
                            _lowerCamelCase				:  Any						=		model(**lowercase_   )
                     _lowerCamelCase				:  Union[str, Any]						=		outputs.logits.argmax(dim=-1   )
                     # It is slightly faster to call this once, than multiple times
                     _lowerCamelCase,	_lowerCamelCase				:  Tuple						=		accelerator.gather(
                         (predictions, batch['''labels'''])   )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(lowercase_   ) - 1:
                                   _lowerCamelCase				:  List[Any]						=		predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                                   _lowerCamelCase				:  int						=		references[: len(eval_dataloader.dataset   ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=lowercase_			,     references=lowercase_			,     )

              _lowerCamelCase				:  List[str]						=		metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""			,     lowercase_   )
              _lowerCamelCase				:  int						=		eval_metric['''accuracy''']

              if best_performance < eval_metric["accuracy"]:
                     _lowerCamelCase				:  Union[str, Any]						=		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(lowercase_			,     lowercase_   )




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  Dict						=		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''   )
       parser.add_argument(
           '''--model_name_or_path'''			,     type=lowercase_			,     default='''bert-base-cased'''			,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,     required=lowercase_			,     )
       parser.add_argument(
           '''--output_dir'''			,     type=lowercase_			,     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=lowercase_			,     default=lowercase_			,     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=lowercase_			,     default=3			,     help='''Number of train epochs.'''			,     )
       _lowerCamelCase				:  Optional[Any]						=		parser.parse_args()
       _lowerCamelCase				:  str						=		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_			,     lowercase_   )


if __name__ == "__main__":
       main()








import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   ["""input_features"""]


      def __init__(		self   ,				a__=80   ,				a__=1_6000   ,				a__=160   ,				a__=30   ,				a__=400   ,				a__=0.0   ,				a__=False   ,				**a__   ,				):



             """simple docstring"""





             super().__init__(
                 feature_size=a__   ,				sampling_rate=a__   ,				padding_value=a__   ,				return_attention_mask=a__   ,				**a__   ,				)
             _lowerCamelCase				:  int						=		n_fft
             _lowerCamelCase				:  List[Any]						=		hop_length
             _lowerCamelCase				:  Tuple						=		chunk_length
             _lowerCamelCase				:  Optional[Any]						=		chunk_length * sampling_rate
             _lowerCamelCase				:  Optional[Any]						=		self.n_samples // hop_length
             _lowerCamelCase				:  List[str]						=		sampling_rate
             _lowerCamelCase				:  Optional[int]						=		mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2   ,				num_mel_filters=a__   ,				min_frequency=0.0   ,				max_frequency=8000.0   ,				sampling_rate=a__   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		spectrogram(
                 a__   ,				window_function(self.n_fft   ,				'''hann''')   ,				frame_length=self.n_fft   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=self.mel_filters   ,				log_mel='''log10'''   ,				)
             _lowerCamelCase				:  str						=		log_spec[:, :-1]
             _lowerCamelCase				:  Union[str, Any]						=		np.maximum(a__   ,				log_spec.max() - 8.0)
             _lowerCamelCase				:  Optional[int]						=		(log_spec + 4.0) / 4.0
             return log_spec


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



             """simple docstring"""





             if attention_mask is not None:
                    _lowerCamelCase				:  Dict						=		np.array(a__   ,				np.intaa)
                    _lowerCamelCase				:  Any						=		[]

                    for vector, length in zip(a__   ,				attention_mask.sum(-1)):
                           _lowerCamelCase				:  Any						=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
                           if length < normed_slice.shape[0]:
                                  _lowerCamelCase				:  Optional[int]						=		padding_value

                           normed_input_values.append(a__)
             else:
                    _lowerCamelCase				:  Optional[int]						=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

             return normed_input_values


      def __call__(		self   ,				a__   ,				a__ = True   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				a__ = "max_length"   ,				a__ = None   ,				a__ = None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             if sampling_rate is not None:
                    if sampling_rate != self.sampling_rate:
                           raise ValueError(
                               F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                               F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                               F""" was sampled with {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				:  Dict						=		isinstance(a__   ,				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				:  int						=		is_batched_numpy or (
                 isinstance(a__   ,				(list, tuple)) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)))
             )

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

             # always return batch
             if not is_batched:
                    _lowerCamelCase				:  List[Any]						=		[np.asarray([raw_speech]).T]

             _lowerCamelCase				:  List[str]						=		BatchFeature({'''input_features''': raw_speech})

             # convert into correct format for padding

             _lowerCamelCase				:  Optional[Any]						=		self.pad(
                 a__   ,				padding=a__   ,				max_length=max_length if max_length else self.n_samples   ,				truncation=a__   ,				pad_to_multiple_of=a__   ,				return_attention_mask=return_attention_mask or do_normalize   ,				)

             # zero-mean and unit-variance normalization
             if do_normalize:
                    _lowerCamelCase				:  Dict						=		self.zero_mean_unit_var_norm(
                        padded_inputs['''input_features''']   ,				attention_mask=padded_inputs['''attention_mask''']   ,				padding_value=self.padding_value   ,				)
                    _lowerCamelCase				:  int						=		np.stack(padded_inputs['''input_features''']   ,				axis=0)

             # make sure list is in array format
             _lowerCamelCase				:  Optional[Any]						=		padded_inputs.get('''input_features''').transpose(2   ,				0   ,				1)

             _lowerCamelCase				:  Tuple						=		[self._np_extract_fbank_features(a__) for waveform in input_features[0]]

             if isinstance(input_features[0]   ,				a__):
                    _lowerCamelCase				:  str						=		[np.asarray(a__   ,				dtype=np.floataa) for feature in input_features]
             else:
                    _lowerCamelCase				:  Dict						=		input_features

             if return_attention_mask:
                    # rescale from sample (48000) to feature (3000)
                    _lowerCamelCase				:  Optional[int]						=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

             if return_tensors is not None:
                    _lowerCamelCase				:  str						=		padded_inputs.convert_to_tensors(a__)

             return padded_inputs



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		copy.deepcopy(self.__dict__)
             _lowerCamelCase				:  int						=		self.__class__.__name__
             if "mel_filters" in output:
                    del output["mel_filters"]
             return output

