





'''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


_A:					Tuple							=				16
_A:					Tuple							=				32







def _lowerCAmelCase     (      _lowerCAmelCase    )->     List[str]:
 return int(x / 2**20    )






class UpperCAmelCase							:



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




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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase = 16       ,    _lowerCAmelCase = "bert-base-cased"       ,    _lowerCAmelCase = 3_20       ,    _lowerCAmelCase = 1_60       ,    )->     List[Any]:
 __UpperCAmelCase					=   AutoTokenizer.from_pretrained(_lowerCAmelCase    )
 __UpperCAmelCase					=   load_dataset(
     'glue'       ,    'mrpc'       ,    split={'train': F'train[:{n_train}]', 'validation': F'validation[:{n_val}]'}    )

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

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

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

 def collate_fn(_lowerCAmelCase    ):
  # 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(_lowerCAmelCase       ,    padding='max_length'       ,    max_length=1_28       ,    return_tensors='pt'    )
  return tokenizer.pad(_lowerCAmelCase       ,    padding='longest'       ,    return_tensors='pt'    )

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

 return train_dataloader, eval_dataloader







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Optional[int]:
 # Initialize accelerator
 __UpperCAmelCase					=   Accelerator()

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

 set_seed(_lowerCAmelCase    )
 __UpperCAmelCase					,    __UpperCAmelCase					=   get_dataloaders(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    args.n_train       ,    args.n_val    )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  __UpperCAmelCase					=   accelerator.state.deepspeed_plugin.deepspeed_config[
      'gradient_accumulation_steps'
  ]
 else:
  __UpperCAmelCase					=   1
 __UpperCAmelCase					=   (len(_lowerCAmelCase    ) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  __UpperCAmelCase					=   get_linear_schedule_with_warmup(
      optimizer=_lowerCAmelCase       ,    num_warmup_steps=0       ,    num_training_steps=_lowerCAmelCase       ,    )
 else:
  __UpperCAmelCase					=   DummyScheduler(_lowerCAmelCase       ,    total_num_steps=_lowerCAmelCase       ,    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.
 __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   accelerator.prepare(
     _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )

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

 # Now we train the model
 __UpperCAmelCase					=   {}
 for epoch in range(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(_lowerCAmelCase    ):
    __UpperCAmelCase					=   model(**_lowerCAmelCase    )
    __UpperCAmelCase					=   outputs.loss
    __UpperCAmelCase					=   loss / gradient_accumulation_steps
    accelerator.backward(_lowerCAmelCase    )
    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    )    )    )
  __UpperCAmelCase					=   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(_lowerCAmelCase       ,    _lowerCAmelCase    )







def _lowerCAmelCase     (      )->     str:
 __UpperCAmelCase					=   argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
 parser.add_argument(
     '--model_name_or_path'       ,    type=_lowerCAmelCase       ,    default='bert-base-cased'       ,    help='Path to pretrained model or model identifier from huggingface.co/models.'       ,    required=_lowerCAmelCase       ,    )
 parser.add_argument(
     '--output_dir'       ,    type=_lowerCAmelCase       ,    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=_lowerCAmelCase       ,    default=_lowerCAmelCase       ,    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=_lowerCAmelCase       ,    default=3_20       ,    help='Number of training examples to use.'       ,    )
 parser.add_argument(
     '--n_val'       ,    type=_lowerCAmelCase       ,    default=1_60       ,    help='Number of validation examples to use.'       ,    )
 parser.add_argument(
     '--num_epochs'       ,    type=_lowerCAmelCase       ,    default=1       ,    help='Number of train epochs.'       ,    )
 __UpperCAmelCase					=   parser.parse_args()
 __UpperCAmelCase					=   {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
 training_function(_lowerCAmelCase       ,    _lowerCAmelCase    )


if __name__ == "__main__":
    main()












'''simple docstring'''




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str | Literal[False]:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count += 1
   __UpperCAmelCase					=   '_'
 if count > 1:
  return False
 else:
  return "".join(_lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 while True:
  __UpperCAmelCase					=   ['$'] * len(_lowerCAmelCase    )
  __UpperCAmelCase					=   []
  for i in range(len(_lowerCAmelCase    )    ):
   for j in range(i + 1       ,    len(_lowerCAmelCase    )    ):
    __UpperCAmelCase					=   compare_string(binary[i]       ,    binary[j]    )
    if k is False:
     __UpperCAmelCase					=   '*'
     __UpperCAmelCase					=   '*'
     temp.append('X'    )
  for i in range(len(_lowerCAmelCase    )    ):
   if checka[i] == "$":
    pi.append(binary[i]    )
  if len(_lowerCAmelCase    ) == 0:
   return pi
  __UpperCAmelCase					=   list(set(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 for minterm in minterms:
  __UpperCAmelCase					=   ''
  for _ in range(_lowerCAmelCase    ):
   __UpperCAmelCase					=   str(minterm % 2    ) + string
   minterm //= 2
  temp.append(_lowerCAmelCase    )
 return temp







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     bool:
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   list(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 for i in range(len(_lowerCAmelCase    )    ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[str]:
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   [0] * len(_lowerCAmelCase    )
 for i in range(len(chart[0]    )    ):
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  for j in range(len(_lowerCAmelCase    )    ):
   if chart[j][i] == 1:
    count += 1
    __UpperCAmelCase					=   j
  if count == 1:
   __UpperCAmelCase					=   1
 for i in range(len(_lowerCAmelCase    )    ):
  if select[i] == 1:
   for j in range(len(chart[0]    )    ):
    if chart[i][j] == 1:
     for k in range(len(_lowerCAmelCase    )    ):
      __UpperCAmelCase					=   0
   temp.append(prime_implicants[i]    )
 while True:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   -1
  __UpperCAmelCase					=   0
  for i in range(len(_lowerCAmelCase    )    ):
   __UpperCAmelCase					=   chart[i].count(1    )
   if count_n > max_n:
    __UpperCAmelCase					=   count_n
    __UpperCAmelCase					=   i

  if max_n == 0:
   return temp

  temp.append(prime_implicants[rem]    )

  for i in range(len(chart[0]    )    ):
   if chart[rem][i] == 1:
    for j in range(len(_lowerCAmelCase    )    ):
     __UpperCAmelCase					=   0







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     list[list[int]]:
 __UpperCAmelCase					=   [[0 for x in range(len(_lowerCAmelCase    )    )] for x in range(len(_lowerCAmelCase    )    )]
 for i in range(len(_lowerCAmelCase    )    ):
  __UpperCAmelCase					=   prime_implicants[i].count('_'    )
  for j in range(len(_lowerCAmelCase    )    ):
   if is_for_table(prime_implicants[i]       ,    binary[j]       ,    _lowerCAmelCase    ):
    __UpperCAmelCase					=   1

 return chart







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   int(input('Enter the no. of variables\n'    )    )
 __UpperCAmelCase					=   [
     float(_lowerCAmelCase    )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'    ).split()
 ]
 __UpperCAmelCase					=   decimal_to_binary(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   check(_lowerCAmelCase    )
 print('Prime Implicants are:'    )
 print(_lowerCAmelCase    )
 __UpperCAmelCase					=   prime_implicant_chart(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   selection(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print('Essential Prime Implicants are:'    )
 print(_lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





