


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


a_ :Optional[Any]  							=			16
a_ :Dict  							=			32




def    a   (							A__							)	-> Dict:




      '''simple docstring'''

      return int(x / 2**2_0							)

class 							lowercase  :







     def __enter__(						self :						List[str]      ):
           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           SCREAMING_SNAKE_CASE__       :  Any						     =		torch.cuda.memory_allocated()
           return self







     def __exit__(						self :						Union[str, Any]   ,   *_lowercase :						Tuple      ):
           gc.collect()
           torch.cuda.empty_cache()
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.cuda.memory_allocated()
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		torch.cuda.max_memory_allocated()
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		bamb(self.end - self.begin      )
           SCREAMING_SNAKE_CASE__       :  str						     =		bamb(self.peak - self.begin      )
           # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def    a   (							A__      ,    A__ = 1_6      ,    A__ = "bert-base-cased"      ,    A__ = 3_2_0      ,    A__ = 1_6_0      ,    )	-> Optional[int]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  int						     =		AutoTokenizer.from_pretrained(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		load_dataset(
          '''glue'''      ,    '''mrpc'''      ,    split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""}							)

      def tokenize_function(A__							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		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__       :  str						     =		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__       :  List[str]						     =		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__       :  Union[str, Any]						     =		DataLoader(
          tokenized_datasets['''train''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		DataLoader(
          tokenized_datasets['''validation''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      return train_dataloader, eval_dataloader




def    a   (							A__      ,    A__							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		Accelerator()

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

      set_seed(A__							)
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		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)
      SCREAMING_SNAKE_CASE__       :  str						     =		AutoModelForSequenceClassification.from_pretrained(A__      ,    return_dict=A__							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      SCREAMING_SNAKE_CASE__       :  Dict						     =		optimizer_cls(params=model.parameters()      ,    lr=A__							)

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		accelerator.state.deepspeed_plugin.deepspeed_config[
                '''gradient_accumulation_steps'''
            ]
      else:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		1
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		(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__       :  List[str]						     =		get_linear_schedule_with_warmup(
                optimizer=A__      ,    num_warmup_steps=0      ,    num_training_steps=A__      ,    )
      else:
            SCREAMING_SNAKE_CASE__       :  Optional[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__       :  Tuple						     =		accelerator.prepare(
          A__      ,    A__      ,    A__      ,    A__      ,    A__							)

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

      # Now we train the model
      SCREAMING_SNAKE_CASE__       :  Dict						     =		{}
      for epoch in range(A__      ,    A__							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(A__							):
                        SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model(**A__							)
                        SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		outputs.loss
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		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							)							)							)
            SCREAMING_SNAKE_CASE__       :  Dict						     =		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   (							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		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.'''      ,    )
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		parser.parse_args()
      SCREAMING_SNAKE_CASE__       :  Dict						     =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(A__      ,    A__							)


if __name__ == "__main__":
    main()







def _lowerCamelCase  (		snake_case  ):
							assert (
							    isinstance(snake_case     , snake_case  ) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							_lowerCAmelCase			,		_lowerCAmelCase							=							1, 1
							for _ in range(number_of_steps - 1  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							current + previous, current
							return current


if __name__ == "__main__":
				import doctest

				doctest.testmod()






