






"""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


_SCREAMING_SNAKE_CASE					:				str					    =						16
_SCREAMING_SNAKE_CASE					:				List[str]					    =						32







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		int = 16		,				_lowerCamelCase       :		List[str] = "bert-base-cased" )      ->				Any:
  lowerCamelCase_         =						AutoTokenizer.from_pretrained(__UpperCAmelCase )
  lowerCamelCase_         =						load_dataset('glue'		,				'mrpc' )

  def tokenize_function(_lowerCamelCase       :		List[Any] ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase_         =						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
  lowerCamelCase_         =						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
  lowerCamelCase_         =						tokenized_datasets.rename_column('label'		,				'labels' )

  def collate_fn(_lowerCamelCase       :		List[str] ):
    # 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.
  lowerCamelCase_         =						DataLoader(
      tokenized_datasets['train']		,				shuffle=__UpperCAmelCase		,				collate_fn=__UpperCAmelCase		,				batch_size=__UpperCAmelCase )
  lowerCamelCase_         =						DataLoader(
      tokenized_datasets['validation']		,				shuffle=__UpperCAmelCase		,				collate_fn=__UpperCAmelCase		,				batch_size=__UpperCAmelCase )

  return train_dataloader, eval_dataloader







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Dict )      ->				str:
  # Initialize accelerator
  lowerCamelCase_         =						Accelerator()

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

  set_seed(__UpperCAmelCase )
  lowerCamelCase_         =						get_dataloaders(__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase )

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

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

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

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

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

      overall_step += 1

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

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

    if best_performance < eval_metric["accuracy"]:
      lowerCamelCase_         =						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(__UpperCAmelCase		,				__UpperCAmelCase )







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  lowerCamelCase_         =						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(
      '--performance_lower_bound'		,				type=__UpperCAmelCase		,				default=__UpperCAmelCase		,				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=__UpperCAmelCase		,				default=3		,				help='Number of train epochs.'		,				)
  lowerCamelCase_         =						parser.parse_args()
  lowerCamelCase_         =						{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
  training_function(__UpperCAmelCase		,				__UpperCAmelCase )


if __name__ == "__main__":
  main()





"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      0
   lowercase__:   List[Any]					=      len(__UpperCAmelCase ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      lowercase__:   Tuple					=      left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__UpperCAmelCase ):
         return None

      lowercase__:   Optional[int]					=      sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            lowercase__:   List[Any]					=      left
            lowercase__:   int					=      point
         elif point > right:
            lowercase__:   Dict					=      right
            lowercase__:   List[str]					=      point
         else:
            if item < current_item:
               lowercase__:   int					=      point - 1
            else:
               lowercase__:   int					=      point + 1
   return None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   lowercase__:   Optional[int]					=      left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__UpperCAmelCase ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   elif point > right:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    point - 1 )
      else:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    point + 1       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if collection != sorted(__UpperCAmelCase ):
      raise ValueError('''Collection must be ascending sorted''' )
   return True


if __name__ == "__main__":
       import sys
       __A			   = 0
       if debug == 1:
              __A			   = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              __A			   = 6_7

       __A			   = interpolation_search(collection, target)
       if result is not None:
              print(f'''{target} found at positions: {result}''')
       else:
              print("Not found")
