


'''simple docstring'''


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


__magic_name__			=      16
__magic_name__			=      32




def      lowerCamelCase					(      lowerCamelCase  :      Accelerator							,						lowerCamelCase  :      int = 16							,						lowerCamelCase  :      str = "bert-base-cased"):
      A_						:  Tuple          =				AutoTokenizer.from_pretrained(UpperCamelCase__)
      A_						:  Dict          =				load_dataset("""glue"""							,						"""mrpc""")

      def tokenize_function(lowerCamelCase  :      int):
            # max_length=None => use the model max length (it's actually the default)
            A_						:  Tuple          =				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
      A_						:  List[Any]          =				datasets.map(
          UpperCamelCase__							,						batched=UpperCamelCase__							,						remove_columns=["""idx""", """sentence1""", """sentence2"""]							,						load_from_cache_file=UpperCamelCase__)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      A_						:  Union[str, Any]          =				tokenized_datasets.rename_column("""label"""							,						"""labels""")

      def collate_fn(lowerCamelCase  :      Dict):
            # 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(UpperCamelCase__							,						padding="""max_length"""							,						max_length=128							,						return_tensors="""pt""")
            return tokenizer.pad(UpperCamelCase__							,						padding="""longest"""							,						return_tensors="""pt""")

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

      return train_dataloader, eval_dataloader




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

      A_						:  Tuple          =				metric.compute()
      return eval_metric["accuracy"]




def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]):
      # Initialize accelerator
      A_						:  str          =				Accelerator()

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

      set_seed(UpperCamelCase__)
      A_						:  List[Any]          =				get_dataloaders(UpperCamelCase__							,						UpperCamelCase__							,						UpperCamelCase__)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A_						:  Union[str, Any]          =				AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__							,						return_dict=UpperCamelCase__)

      # Instantiate optimizer
      A_						:  Optional[int]          =				(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      A_						:  Dict          =				optimizer_cls(params=model.parameters()							,						lr=UpperCamelCase__)

      if accelerator.state.deepspeed_plugin is not None:
            A_						:  Tuple          =				accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            A_						:  Any          =				1
      A_						:  List[str]          =				(len(UpperCamelCase__) * num_epochs) // gradient_accumulation_steps

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

      # We need to keep track of how many total steps we have iterated over
      A_						:  Optional[int]          =				0
      # We also need to keep track of the stating epoch so files are named properly
      A_						:  int          =				0
      A_						:  int          =				evaluate.load("""glue"""							,						"""mrpc""")
      A_						:  int          =				num_epochs

      if args.partial_train_epoch is not None:
            A_						:  Dict          =				args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint)
            A_						:  str          =				args.resume_from_checkpoint.split("""epoch_""")[1]
            A_						:  Optional[int]          =				""""""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            A_						:  List[str]          =				int(UpperCamelCase__) + 1
            A_						:  Any          =				evaluation_loop(UpperCamelCase__							,						UpperCamelCase__							,						UpperCamelCase__							,						UpperCamelCase__)
            accelerator.print("""resumed checkpoint performance:"""							,						UpperCamelCase__)
            accelerator.print("""resumed checkpoint's scheduler's lr:"""							,						lr_scheduler.get_lr()[0])
            accelerator.print("""resumed optimizers's lr:"""							,						optimizer.param_groups[0]["""lr"""])
            with open(os.path.join(args.output_dir							,						F'state_{starting_epoch-1}.json')							,						"""r""") as f:
                  A_						:  Dict          =				json.load(UpperCamelCase__)
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      A_						:  int          =				{}
      for epoch in range(UpperCamelCase__							,						UpperCamelCase__):
            model.train()
            for step, batch in enumerate(UpperCamelCase__):
                  A_						:  Tuple          =				model(**UpperCamelCase__)
                  A_						:  List[str]          =				outputs.loss
                  A_						:  List[str]          =				loss / gradient_accumulation_steps
                  accelerator.backward(UpperCamelCase__)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            A_						:  List[Any]          =				F'epoch_{epoch}'
            A_						:  Dict          =				os.path.join(args.output_dir							,						UpperCamelCase__)
            accelerator.save_state(UpperCamelCase__)
            A_						:  List[Any]          =				evaluation_loop(UpperCamelCase__							,						UpperCamelCase__							,						UpperCamelCase__							,						UpperCamelCase__)
            A_						:  Any          =				accuracy
            A_						:  Optional[Any]          =				lr_scheduler.get_lr()[0]
            A_						:  Union[str, Any]          =				optimizer.param_groups[0]["""lr"""]
            A_						:  Tuple          =				epoch
            A_						:  Optional[Any]          =				overall_step
            accelerator.print(F'epoch {epoch}:'							,						UpperCamelCase__)

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir							,						F'state_{epoch}.json')							,						"""w""") as f:
                        json.dump(UpperCamelCase__							,						UpperCamelCase__)




def      lowerCamelCase					(      ):
      A_						:  str          =				argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
      parser.add_argument(
          """--model_name_or_path"""							,						type=UpperCamelCase__							,						default="""bert-base-cased"""							,						help="""Path to pretrained model or model identifier from huggingface.co/models."""							,						required=UpperCamelCase__							,						)
      parser.add_argument(
          """--output_dir"""							,						type=UpperCamelCase__							,						default="""."""							,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,						)
      parser.add_argument(
          """--resume_from_checkpoint"""							,						type=UpperCamelCase__							,						default=UpperCamelCase__							,						help="""If the training should continue from a checkpoint folder."""							,						)
      parser.add_argument(
          """--partial_train_epoch"""							,						type=UpperCamelCase__							,						default=UpperCamelCase__							,						help="""If passed, the training will stop after this number of epochs."""							,						)
      parser.add_argument(
          """--num_epochs"""							,						type=UpperCamelCase__							,						default=2							,						help="""Number of train epochs."""							,						)
      A_						:  Optional[int]          =				parser.parse_args()
      A_						:  str          =				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

      training_function(UpperCamelCase__							,						UpperCamelCase__)


if __name__ == "__main__":
     main()







'''simple docstring'''


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

import numpy as np
import torch

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


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_features""", """is_longer"""]






  def __init__(							self :       Dict	,_a :       Optional[int]=64	,_a :       List[Any]=48000	,_a :       str=480	,_a :       Optional[Any]=10	,_a :       Optional[int]=1024	,_a :       Tuple=0.0	,_a :       str=False	,_a :       float = 0	,_a :       float = 14000	,_a :       int = None	,_a :       str = "fusion"	,_a :       str = "repeatpad"	,**_a :       Tuple	,):

        '''simple docstring'''




        super().__init__(
            feature_size=_a	,sampling_rate=_a	,padding_value=_a	,return_attention_mask=_a	,**_a	,)
        A_						:  Tuple          =				top_db
        A_						:  Tuple          =				truncation
        A_						:  Optional[Any]          =				padding
        A_						:  Optional[int]          =				fft_window_size
        A_						:  Dict          =				(fft_window_size >> 1) + 1
        A_						:  Any          =				hop_length
        A_						:  List[Any]          =				max_length_s
        A_						:  Tuple          =				max_length_s * sampling_rate
        A_						:  Tuple          =				sampling_rate
        A_						:  Optional[int]          =				frequency_min
        A_						:  Tuple          =				frequency_max
        A_						:  Tuple          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm=_a	,mel_scale="""htk"""	,)
        A_						:  Dict          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm="""slaney"""	,mel_scale="""slaney"""	,)






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				copy.deepcopy(self.__dict__ )
        A_						:  Tuple          =				self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
        return output






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[np.array] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				spectrogram(
            _a	,window_function(self.fft_window_size	,"""hann""" )	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=_a	,log_mel="""dB"""	,)
        return log_mel_spectrogram.T






  def        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				np.array_split(list(range(0	,total_frames - chunk_frames + 1 ) )	,3 )
        if len(ranges[1] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  List[Any]          =				[0]
        if len(ranges[2] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  int          =				[0]
        # randomly choose index for each part
        A_						:  List[str]          =				np.random.choice(ranges[0] )
        A_						:  int          =				np.random.choice(ranges[1] )
        A_						:  Optional[int]          =				np.random.choice(ranges[2] )

        A_						:  Tuple          =				mel[idx_front : idx_front + chunk_frames, :]
        A_						:  Dict          =				mel[idx_middle : idx_middle + chunk_frames, :]
        A_						:  Dict          =				mel[idx_back : idx_back + chunk_frames, :]

        A_						:  Optional[int]          =				torch.tensor(mel[None, None, :] )
        A_						:  Dict          =				torch.nn.functional.interpolate(
            _a	,size=[chunk_frames, 64]	,mode="""bilinear"""	,align_corners=_a )
        A_						:  str          =				mel_shrink[0][0].numpy()
        A_						:  Tuple          =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0 )
        return mel_fusion






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[Any]	,_a :       int	,_a :       Dict ):

        '''simple docstring'''




        if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                    A_						:  Dict          =				True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    A_						:  Tuple          =				len(_a ) - max_length
                    A_						:  Optional[int]          =				np.random.randint(0	,overflow + 1 )
                    A_						:  List[Any]          =				waveform[idx : idx + max_length]
                    A_						:  Optional[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]
              elif truncation == "fusion":
                    A_						:  Dict          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Tuple          =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    A_						:  Optional[int]          =				mel.shape[0]
                    if chunk_frames == total_frames:
                          # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                          # In this case, we just use the whole audio.
                          A_						:  Optional[int]          =				np.stack([mel, mel, mel, mel]	,axis=0 )
                          A_						:  str          =				False
                    else:
                          A_						:  str          =				self._random_mel_fusion(_a	,_a	,_a )
                          A_						:  Optional[Any]          =				True
              else:
                    raise NotImplementedError(f'data_truncating {truncation} not implemented' )

        else:
              A_						:  Optional[int]          =				False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                    if padding == "repeat":
                          A_						:  int          =				int(max_length / len(_a ) )
                          A_						:  Any          =				np.stack(np.tile(_a	,n_repeat + 1 ) )[:max_length]
                    if padding == "repeatpad":
                          A_						:  List[str]          =				int(max_length / len(_a ) )
                          A_						:  Optional[Any]          =				np.stack(np.tile(_a	,_a ) )
                    A_						:  Any          =				np.pad(_a	,(0, max_length - waveform.shape[0])	,mode="""constant"""	,constant_values=0 )

              if truncation == "fusion":
                    A_						:  List[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Optional[Any]          =				np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0 )
              else:
                    A_						:  Union[str, Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]

        return input_mel, longer






  def __call__(							self :       List[Any]	,_a :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_a :       str = None	,_a :       Optional[str] = None	,_a :       Optional[int] = None	,_a :       Optional[int] = None	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Any	,):

        '''simple docstring'''




        A_						:  List[str]          =				truncation if truncation is not None else self.truncation
        A_						:  List[Any]          =				padding if padding else self.padding

        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.""" )

        A_						:  Any          =				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}' )
        A_						:  int          =				is_batched_numpy or (
            isinstance(_a	,(list, tuple) ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list) ))
        )

        if is_batched:
              A_						:  Optional[int]          =				[np.asarray(_a	,dtype=np.floataa ) for speech in raw_speech]
        elif not is_batched and not isinstance(_a	,np.ndarray ):
              A_						:  str          =				np.asarray(_a	,dtype=np.floataa )
        elif isinstance(_a	,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
              A_						:  Tuple          =				raw_speech.astype(np.floataa )

        # always return batch
        if not is_batched:
              A_						:  Any          =				[np.asarray(_a )]

        # convert to mel spectrogram, truncate and pad if needed.
        A_						:  str          =				[
            self._get_input_mel(_a	,max_length if max_length else self.nb_max_samples	,_a	,_a )
            for waveform in raw_speech
        ]

        A_						:  int          =				[]
        A_						:  Any          =				[]
        for mel, longer in padded_inputs:
              input_mel.append(_a )
              is_longer.append(_a )

        if truncation == "fusion" and sum(_a ) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              A_						:  List[Any]          =				np.random.randint(0	,len(_a ) )
              A_						:  List[str]          =				True

        if isinstance(input_mel[0]	,_a ):
              A_						:  Tuple          =				[np.asarray(_a	,dtype=np.floataa ) for feature in input_mel]

        # is_longer is a list of bool
        A_						:  List[str]          =				[[longer] for longer in is_longer]

        A_						:  Optional[Any]          =				{"""input_features""": input_mel, """is_longer""": is_longer}
        A_						:  int          =				BatchFeature(_a )

        if return_tensors is not None:
              A_						:  int          =				input_features.convert_to_tensors(_a )

        return input_features
