


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


__snake_case   							=						1_6
__snake_case   							=						3_2






def 		_A		(		_lowercase					,  _lowercase = 16					,  _lowercase = "bert-base-cased" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			AutoTokenizer.from_pretrained(_lowercase )
     __UpperCamelCase =			load_dataset('glue'					,  'mrpc' )

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

     return train_dataloader, eval_dataloader






def 		_A		(		_lowercase					,  _lowercase )					-> 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(_lowercase )
     __UpperCamelCase,    __UpperCamelCase =			get_dataloaders(_lowercase					,  _lowercase					,  _lowercase )

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

     # 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=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          __UpperCamelCase =			accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          __UpperCamelCase =			1
     __UpperCamelCase =			(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
     ):
          __UpperCamelCase =			get_linear_schedule_with_warmup(
              optimizer=_lowercase					,  num_warmup_steps=0					,  num_training_steps=_lowercase					,  )
     else:
          __UpperCamelCase =			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.
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )

     # 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(_lowercase					,  _lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               __UpperCamelCase =			model(**_lowercase )
               __UpperCamelCase =			outputs.loss
               __UpperCamelCase =			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()
          __UpperCamelCase =			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():
                    __UpperCamelCase =			model(**_lowercase )
               __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(_lowercase ) - 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=_lowercase					,  references=_lowercase					,  )

          __UpperCamelCase =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''					,  _lowercase )
          __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(_lowercase					,  _lowercase )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			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.'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


