

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     :   Optional[Any]						     =  16
lowerCAmelCase     :   Tuple						     =  32

def    A_  (							a					,   a = 1_6					,   a = "bert-base-cased"  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[Any]           =     AutoTokenizer.from_pretrained(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     load_dataset('glue'					,   'mrpc'  )

      def tokenize_function(a  ):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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
      SCREAMING_SNAKE_CASE_				:  Dict           =     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.
      SCREAMING_SNAKE_CASE_				:  Any           =     DataLoader(
          tokenized_datasets['train']					,   shuffle=a					,   collate_fn=a					,   batch_size=a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(
          tokenized_datasets['validation']					,   shuffle=a					,   collate_fn=a					,   batch_size=a  )

      return train_dataloader, eval_dataloader

def    A_  (							a					,   a					,   a					,   a  ):

      """simple docstring"""



      model.eval()
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     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():
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model(**a  )
            SCREAMING_SNAKE_CASE_				:  Any           =     outputs.logits.argmax(dim=-1  )
            # It is slightly faster to call this once, than multiple times
            SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     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:
                        SCREAMING_SNAKE_CASE_				:  Any           =     predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                        SCREAMING_SNAKE_CASE_				:  Any           =     references[: len(eval_dataloader.dataset  ) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=a					,   references=a					,   )

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

def    A_  (							a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Tuple           =     Accelerator()

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

      set_seed(a  )
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     get_dataloaders(a					,   a					,   a  )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE_				:  Dict           =     accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            SCREAMING_SNAKE_CASE_				:  Tuple           =     1
      SCREAMING_SNAKE_CASE_				:  str           =     (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
      ):
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     get_linear_schedule_with_warmup(
                optimizer=a					,   num_warmup_steps=0					,   num_training_steps=a					,   )
      else:
            SCREAMING_SNAKE_CASE_				:  Any           =     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.
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     accelerator.prepare(
          a					,   a					,   a					,   a					,   a  )

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

      if args.partial_train_epoch is not None:
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     args.resume_from_checkpoint.split('epoch_'  )[1]
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ''
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     int(a  ) + 1
            SCREAMING_SNAKE_CASE_				:  int           =     evaluation_loop(a					,   a					,   a					,   a  )
            accelerator.print('resumed checkpoint performance:'					,   a  )
            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:
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     json.load(a  )
                  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
      SCREAMING_SNAKE_CASE_				:  Any           =     {}
      for epoch in range(a					,   a  ):
            model.train()
            for step, batch in enumerate(a  ):
                  SCREAMING_SNAKE_CASE_				:  int           =     model(**a  )
                  SCREAMING_SNAKE_CASE_				:  Optional[int]           =     outputs.loss
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     loss / gradient_accumulation_steps
                  accelerator.backward(a  )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            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(a					,   a  )

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  str           =     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(
          '--resume_from_checkpoint'					,   type=a					,   default=a					,   help='If the training should continue from a checkpoint folder.'					,   )
      parser.add_argument(
          '--partial_train_epoch'					,   type=a					,   default=a					,   help='If passed, the training will stop after this number of epochs.'					,   )
      parser.add_argument(
          '--num_epochs'					,   type=a					,   default=2					,   help='Number of train epochs.'					,   )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     parser.parse_args()
      SCREAMING_SNAKE_CASE_				:  int           =     {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

      training_function(a					,   a  )


if __name__ == "__main__":
    main()









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  field(default='''automatic-speech-recognition'''   ,     metadata={'''include_in_asdict_even_if_is_default''': True})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''audio''': Audio()})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''transcription''': Value('''string''')})
  SCREAMING_SNAKE_CASE    :      str       =  "audio"
  SCREAMING_SNAKE_CASE    :      str       =  "transcription"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."					)
        if not isinstance(features[self.audio_column] ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
        SCREAMING_SNAKE_CASE_				:  int           =     copy.deepcopy(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.input_schema.copy()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     features[self.audio_column]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_schema
        return task_template



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}






