


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  return int(x / 2**20       )






class   _lowerCamelCase :




       """simple docstring"""



       def __enter__(      self		)->Any:




         '''simple docstring'''





         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         A_      :				Dict		     =					torch.cuda.memory_allocated()
         return self







       def __exit__(      self				,       *_SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         gc.collect()
         torch.cuda.empty_cache()
         A_      :				Dict		     =					torch.cuda.memory_allocated()
         A_      :				str		     =					torch.cuda.max_memory_allocated()
         A_      :				Dict		     =					bamb(self.end - self.begin		)
         A_      :				List[str]		     =					bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = 16 ,			SCREAMING_SNAKE_CASE = "bert-base-cased" ,			SCREAMING_SNAKE_CASE = 320 ,			SCREAMING_SNAKE_CASE = 160 ,			):
  A_      :				Dict		     =					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					load_dataset(
      '''glue''' ,			'''mrpc''' ,			split={'''train''': f'''train[:{n_train}]''', '''validation''': f'''validation[:{n_val}]'''}       )

  def tokenize_function(SCREAMING_SNAKE_CASE       ):
    # max_length=None => use the model max length (it's actually the default)
    A_      :				List[Any]		     =					tokenizer(examples['''sentence1'''] ,			examples['''sentence2'''] ,			truncation=SCREAMING_SNAKE_CASE ,			max_length=SCREAMING_SNAKE_CASE       )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  A_      :				Any		     =					datasets.map(
      SCREAMING_SNAKE_CASE ,			batched=SCREAMING_SNAKE_CASE ,			remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,			load_from_cache_file=SCREAMING_SNAKE_CASE       )

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

  def collate_fn(SCREAMING_SNAKE_CASE       ):
    # 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(SCREAMING_SNAKE_CASE ,			padding='''max_length''' ,			max_length=128 ,			return_tensors='''pt'''       )
    return tokenizer.pad(SCREAMING_SNAKE_CASE ,			padding='''longest''' ,			return_tensors='''pt'''       )

  # Instantiate dataloaders.
  A_      :				str		     =					DataLoader(
      tokenized_datasets['''train'''] ,			shuffle=SCREAMING_SNAKE_CASE ,			collate_fn=SCREAMING_SNAKE_CASE ,			batch_size=SCREAMING_SNAKE_CASE       )
  A_      :				Optional[Any]		     =					DataLoader(
      tokenized_datasets['''validation'''] ,			shuffle=SCREAMING_SNAKE_CASE ,			collate_fn=SCREAMING_SNAKE_CASE ,			batch_size=SCREAMING_SNAKE_CASE       )

  return train_dataloader, eval_dataloader


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  # Initialize accelerator
  A_      :				Optional[Any]		     =					Accelerator()

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

  set_seed(SCREAMING_SNAKE_CASE       )
  A_     ,       A_      :				Optional[Any]		     =					get_dataloaders(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			args.n_train ,			args.n_val       )

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

  # Instantiate optimizer
  A_      :				Optional[Any]		     =					(
      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=SCREAMING_SNAKE_CASE       )

  if accelerator.state.deepspeed_plugin is not None:
    A_      :				Optional[Any]		     =					accelerator.state.deepspeed_plugin.deepspeed_config[
        '''gradient_accumulation_steps'''
    ]
  else:
    A_      :				Optional[Any]		     =					1
  A_      :				Optional[int]		     =					(len(SCREAMING_SNAKE_CASE       ) * 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_      :				str		     =					get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE ,			num_warmup_steps=0 ,			num_training_steps=SCREAMING_SNAKE_CASE ,			)
  else:
    A_      :				Union[str, Any]		     =					DummyScheduler(SCREAMING_SNAKE_CASE ,			total_num_steps=SCREAMING_SNAKE_CASE ,			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_      :				List[Any]		     =					accelerator.prepare(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

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

  # Now we train the model
  A_      :				Union[str, Any]		     =					{}
  for epoch in range(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(SCREAMING_SNAKE_CASE       ):
        A_      :				List[str]		     =					model(**SCREAMING_SNAKE_CASE       )
        A_      :				Any		     =					outputs.loss
        A_      :				Optional[Any]		     =					loss / gradient_accumulation_steps
        accelerator.backward(SCREAMING_SNAKE_CASE       )
        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_      :				Any		     =					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(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				List[str]		     =					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
  parser.add_argument(
      '''--model_name_or_path''' ,			type=SCREAMING_SNAKE_CASE ,			default='''bert-base-cased''' ,			help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,			required=SCREAMING_SNAKE_CASE ,			)
  parser.add_argument(
      '''--output_dir''' ,			type=SCREAMING_SNAKE_CASE ,			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=SCREAMING_SNAKE_CASE ,			default=SCREAMING_SNAKE_CASE ,			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=SCREAMING_SNAKE_CASE ,			default=320 ,			help='''Number of training examples to use.''' ,			)
  parser.add_argument(
      '''--n_val''' ,			type=SCREAMING_SNAKE_CASE ,			default=160 ,			help='''Number of validation examples to use.''' ,			)
  parser.add_argument(
      '''--num_epochs''' ,			type=SCREAMING_SNAKE_CASE ,			default=1 ,			help='''Number of train epochs.''' ,			)
  A_      :				Any		     =					parser.parse_args()
  A_      :				List[Any]		     =					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
  training_function(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
     main()








from math import isclose, sqrt


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				List[str]		     =					point_y / 4 / point_x
  A_      :				Union[str, Any]		     =					2 * normal_gradient / (1 + normal_gradient * normal_gradient)
  A_      :				int		     =					(1 - normal_gradient * normal_gradient) / (
      1 + normal_gradient * normal_gradient
  )
  A_      :				List[str]		     =					(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

  # to find the next point, solve the simultaeneous equations:
  # y^2 + 4x^2 = 100
  # y - b = m * (x - a)
  # ==> A x^2 + B x + C = 0
  A_      :				List[str]		     =					outgoing_gradient**2 + 4
  A_      :				List[str]		     =					2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
  A_      :				Any		     =					(point_y - outgoing_gradient * point_x) ** 2 - 100

  A_      :				str		     =					(
      -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)
  A_      :				Any		     =					(
      -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
  ) / (2 * quadratic_term)

  # two solutions, one of which is our input point
  A_      :				int		     =					x_minus if isclose(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) else x_plus
  A_      :				Any		     =					point_y + outgoing_gradient * (next_x - point_x)

  return next_x, next_y, outgoing_gradient


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE = 1.4 ,			SCREAMING_SNAKE_CASE = -9.6       ):
  A_      :				int		     =					0
  A_      :				float		     =					first_x_coord
  A_      :				float		     =					first_y_coord
  A_      :				float		     =					(1_0.1 - point_y) / (0.0 - point_x)

  while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
    A_     ,       A_     ,       A_      :				List[str]		     =					next_point(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    num_reflections += 1

  return num_reflections


if __name__ == "__main__":
     print(F'''{solution() = }''')




