

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


lowercase_             =     1_6
lowercase_             =     3_2
def   a   (    A__			:     List[str]  )					->   List[str]:





      """simple docstring"""



      return int(x / 2**20  )





class 				__lowerCAmelCase  :




  def __enter__(       self      )						->				Tuple:
        '''simple docstring'''






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




  def __exit__(       self							,     *lowerCAmelCase      )						->				str:
        '''simple docstring'''






        gc.collect()
        torch.cuda.empty_cache()
        _lowercase			 =torch.cuda.memory_allocated()
        _lowercase			 =torch.cuda.max_memory_allocated()
        _lowercase			 =bamb(self.end - self.begin      )
        _lowercase			 =bamb(self.peak - self.begin      )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def   a   (    A__			:     Accelerator   ,     A__			:     int = 16   ,     A__			:     str = "bert-base-cased"   ,     A__			:     int = 320   ,     A__			:     int = 160   ,     )					->   int:





      """simple docstring"""



      _lowercase			 =AutoTokenizer.from_pretrained(A__  )
      _lowercase			 =load_dataset(
          'glue'   ,     'mrpc'   ,     split={'train': F'''train[:{n_train}]''', 'validation': F'''validation[:{n_val}]'''}  )

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

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

      # Instantiate dataloaders.
      _lowercase			 =DataLoader(
          tokenized_datasets['train']   ,     shuffle=A__   ,     collate_fn=A__   ,     batch_size=A__  )
      _lowercase			 =DataLoader(
          tokenized_datasets['validation']   ,     shuffle=A__   ,     collate_fn=A__   ,     batch_size=A__  )

      return train_dataloader, eval_dataloader
def   a   (    A__			:     Optional[int]   ,     A__			:     List[str]  )					->   Any:





      """simple docstring"""



      _lowercase			 =Accelerator()

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

      set_seed(A__  )
      _lowercase , _lowercase			 =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)
      _lowercase			 =AutoModelForSequenceClassification.from_pretrained(A__   ,     return_dict=A__  )

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

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

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

      # Now we train the model
      _lowercase			 ={}
      for epoch in range(A__   ,     A__  ):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(A__  ):
                        _lowercase			 =model(**A__  )
                        _lowercase			 =outputs.loss
                        _lowercase			 =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  )  )  )
            _lowercase			 =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   (    )					->   str:





      """simple docstring"""



      _lowercase			 =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=320   ,     help='Number of training examples to use.'   ,     )
      parser.add_argument(
          '--n_val'   ,     type=A__   ,     default=160   ,     help='Number of validation examples to use.'   ,     )
      parser.add_argument(
          '--num_epochs'   ,     type=A__   ,     default=1   ,     help='Number of train epochs.'   ,     )
      _lowercase			 =parser.parse_args()
      _lowercase			 ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
      training_function(A__   ,     A__  )


if __name__ == "__main__":
  main()





def   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

      for values in graph.values():
            for i in values:
                  indegree[i] += 1

      for i in range(len(A__  )  ):
            if indegree[i] == 0:
                  queue.append(A__  )

      while queue:
            _lowercase			 =queue.pop(0  )
            cnt += 1
            topo.append(A__  )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(A__  )

      if cnt != len(A__  ):
            print('Cycle exists'  )
      else:
            print(A__  )


# Adjacency List of Graph
lowercase_             =     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


