

"""simple docstring"""


import argparse
import gc
import json
import os

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


UpperCAmelCase													=16
UpperCAmelCase													=32



def   _A					(	_a	:						Optional[Any]				):



      """simple docstring"""






      return int(x / 2**2_0				)





class lowerCamelCase__   :



    '''simple docstring'''




    def __enter__( self    )    ->      Dict:
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          A				  =		torch.cuda.memory_allocated()
          return self




    def __exit__( self ,*lowerCamelCase_    )    ->      Dict:
          gc.collect()
          torch.cuda.empty_cache()
          A				  =		torch.cuda.memory_allocated()
          A				  =		torch.cuda.max_memory_allocated()
          A				  =		bamb(self.end - self.begin    )
          A				  =		bamb(self.peak - self.begin    )
          # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def   _A					(	_a	:						Accelerator     ,   _a	:						int = 1_6     ,   _a	:						str = "bert-base-cased"     ,   _a	:						int = 3_2_0     ,   _a	:						int = 1_6_0     ,   ):



      """simple docstring"""






      A				  =		AutoTokenizer.from_pretrained(_a				)
      A				  =		load_dataset(
          """glue"""     ,   """mrpc"""     ,   split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'}				)

      def tokenize_function(_a	:						int				):
            # max_length=None => use the model max length (it's actually the default)
            A				  =		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
      A				  =		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
      A				  =		tokenized_datasets.rename_column("""label"""     ,   """labels"""				)

      def collate_fn(_a	:						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(_a     ,   padding="""max_length"""     ,   max_length=1_2_8     ,   return_tensors="""pt"""				)
            return tokenizer.pad(_a     ,   padding="""longest"""     ,   return_tensors="""pt"""				)

      # Instantiate dataloaders.
      A				  =		DataLoader(
          tokenized_datasets["""train"""]     ,   shuffle=_a     ,   collate_fn=_a     ,   batch_size=_a				)
      A				  =		DataLoader(
          tokenized_datasets["""validation"""]     ,   shuffle=_a     ,   collate_fn=_a     ,   batch_size=_a				)

      return train_dataloader, eval_dataloader



def   _A					(	_a	:						Optional[Any]     ,   _a	:						List[str]				):



      """simple docstring"""






      A				  =		Accelerator()

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

      set_seed(_a				)
      A  ,			A				  =		get_dataloaders(_a     ,   _a     ,   _a     ,   args.n_train     ,   args.n_val				)

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

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

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

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

      # Now we train the model
      A				  =		{}
      for epoch in range(_a     ,   _a				):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(_a				):
                        A				  =		model(**_a				)
                        A				  =		outputs.loss
                        A				  =		loss / gradient_accumulation_steps
                        accelerator.backward(_a				)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin				)				)				)
            accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used				)				)
            accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked				)				)
            accelerator.print(
                """Total Peak Memory consumed during the train (max): {}""".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
            A				  =		tracemalloc.peaked + bamb(tracemalloc.begin				)
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir     ,   """peak_memory_utilization.json"""				)     ,   """w"""				) as f:
                  json.dump(_a     ,   _a				)



def   _A					(	):



      """simple docstring"""






      A				  =		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(
          """--peak_memory_upper_bound"""     ,   type=_a     ,   default=_a     ,   help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""     ,   )
      parser.add_argument(
          """--n_train"""     ,   type=_a     ,   default=3_2_0     ,   help="""Number of training examples to use."""     ,   )
      parser.add_argument(
          """--n_val"""     ,   type=_a     ,   default=1_6_0     ,   help="""Number of validation examples to use."""     ,   )
      parser.add_argument(
          """--num_epochs"""     ,   type=_a     ,   default=1     ,   help="""Number of train epochs."""     ,   )
      A				  =		parser.parse_args()
      A				  =		{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
      training_function(_a     ,   _a				)


if __name__ == "__main__":
  main()







"""simple docstring"""


def   _A					(	_a	:						Optional[int]     ,   _a	:						str				):



      """simple docstring"""






      A				  =		0
      A				  =		len(_a				) - 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

            A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

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

            A				  =		sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        A				  =		left
                        A				  =		point
                  elif point > right:
                        A				  =		right
                        A				  =		point
                  else:
                        if item < current_item:
                              A				  =		point - 1
                        else:
                              A				  =		point + 1
      return None



def   _A					(	_a	:						Any     ,   _a	:						List[str]     ,   _a	:						Dict     ,   _a	:						List[str]				):



      """simple docstring"""






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

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

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



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      if collection != sorted(_a				):
            raise ValueError("""Collection must be ascending sorted"""				)
      return True


if __name__ == "__main__":
  import sys
  UpperCAmelCase													=0
  if debug == 1:
    UpperCAmelCase													=[10, 30, 40, 45, 50, 66, 77, 93]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit("Sequence must be ascending sorted to apply interpolation search")
    UpperCAmelCase													=67

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




