




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__				:      List[str]	   = 16
lowercase__				:      Any	   = 32

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[int]:
     return int(x / 2**20)




class       a__	:







     def __enter__(     self			)  ->   Tuple:



          '''simple docstring'''



          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  ,		*A			)  ->   int:



          '''simple docstring'''



          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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase = 16		, __UpperCamelCase = "bert-base-cased"		, __UpperCamelCase = 3_20		, __UpperCamelCase = 1_60		, )      ->					str:
     a     =				AutoTokenizer.from_pretrained(__UpperCamelCase)
     a     =				load_dataset(
         "glue"		, "mrpc"		, split={"train": f'''train[:{n_train}]''', "validation": f'''validation[:{n_val}]'''})

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

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

     # 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(__UpperCamelCase):
          # 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(__UpperCamelCase		, padding="max_length"		, max_length=1_28		, return_tensors="pt")
          return tokenizer.pad(__UpperCamelCase		, padding="longest"		, return_tensors="pt")

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

     return train_dataloader, eval_dataloader

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # Initialize accelerator
     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(__UpperCamelCase)
     a  ,				a     =				get_dataloaders(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, 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(__UpperCamelCase		, return_dict=__UpperCamelCase)

     # 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=__UpperCamelCase)

     if accelerator.state.deepspeed_plugin is not None:
          a     =				accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          a     =				1
     a     =				(len(__UpperCamelCase) * 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=__UpperCamelCase		, num_warmup_steps=0		, num_training_steps=__UpperCamelCase		, )
     else:
          a     =				DummyScheduler(__UpperCamelCase		, total_num_steps=__UpperCamelCase		, 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(
         __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)

     # 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(__UpperCamelCase		, __UpperCamelCase):
          with TorchTracemalloc() as tracemalloc:
               model.train()
               for step, batch in enumerate(__UpperCamelCase):
                    a     =				model(**__UpperCamelCase)
                    a     =				outputs.loss
                    a     =				loss / gradient_accumulation_steps
                    accelerator.backward(__UpperCamelCase)
                    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(__UpperCamelCase		, __UpperCamelCase)

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


if __name__ == "__main__":
   main()







from math import isqrt

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     return all(number % divisor != 0 for divisor in range(2		, isqrt(__UpperCamelCase) + 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 10**6)      ->					int:
     a     =				0
     a     =				1
     a     =				7
     while prime_candidate < max_prime:
          primes_count += is_prime(__UpperCamelCase)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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

