

'''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


__SCREAMING_SNAKE_CASE     =						1_6
__SCREAMING_SNAKE_CASE     =						3_2



def 			__a		(       lowerCAmelCase__ :    Optional[int]      ):
     return int(x / 2**20      )






class 			lowerCAmelCase__     :




     """simple docstring"""





     def __enter__(		self						:	Any	)       ->			Optional[int]:
          '''simple docstring'''




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







     def __exit__(		self						:	int      ,						*A__						:	Optional[int]	)       ->			int:
          '''simple docstring'''




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



def 			__a		(       lowerCAmelCase__ :    Dict     ,  lowerCAmelCase__ :    Union[str, Any] = 16     ,  lowerCAmelCase__ :    Optional[int] = "bert-base-cased"     ,  lowerCAmelCase__ :    str = 320     ,  lowerCAmelCase__ :    str = 160     ,  ):
     a__   :      Optional[int]     =						AutoTokenizer.from_pretrained(snake_case__      )
     a__   :      Dict     =						load_dataset(
         '''glue'''     ,  '''mrpc'''     ,  split={'''train''': F'train[:{n_train}]', '''validation''': F'validation[:{n_val}]'}      )

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

     # Apply the method we just defined to all the examples in all the splits of the dataset
     a__   :      List[str]     =						datasets.map(
         snake_case__     ,  batched=snake_case__     ,  remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,  load_from_cache_file=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__   :      Optional[Any]     =						tokenized_datasets.rename_column('''label'''     ,  '''labels'''      )

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

     # Instantiate dataloaders.
     a__   :      Dict     =						DataLoader(
         tokenized_datasets['''train''']     ,  shuffle=snake_case__     ,  collate_fn=snake_case__     ,  batch_size=snake_case__      )
     a__   :      List[str]     =						DataLoader(
         tokenized_datasets['''validation''']     ,  shuffle=snake_case__     ,  collate_fn=snake_case__     ,  batch_size=snake_case__      )

     return train_dataloader, eval_dataloader



def 			__a		(       lowerCAmelCase__ :    Optional[Any]     ,  lowerCAmelCase__ :    int      ):
     # Initialize accelerator
     a__   :      List[str]     =						Accelerator()

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

     set_seed(snake_case__      )
     a__   :      Dict     =						get_dataloaders(snake_case__     ,  snake_case__     ,  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__   :      Tuple     =						AutoModelForSequenceClassification.from_pretrained(snake_case__     ,  return_dict=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__   :      Optional[Any]     =						optimizer_cls(params=model.parameters()     ,  lr=snake_case__      )

     if accelerator.state.deepspeed_plugin is not None:
          a__   :      Any     =						accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          a__   :      Dict     =						1
     a__   :      Optional[Any]     =						(len(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__   :      List[Any]     =						get_linear_schedule_with_warmup(
              optimizer=snake_case__     ,  num_warmup_steps=0     ,  num_training_steps=snake_case__     ,  )
     else:
          a__   :      int     =						DummyScheduler(snake_case__     ,  total_num_steps=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__   :      Any     =						accelerator.prepare(
         snake_case__     ,  snake_case__     ,  snake_case__     ,  snake_case__     ,  snake_case__      )

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

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



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


if __name__ == "__main__":
 main()




"""simple docstring"""






import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate





def 				A_       ( snake_case__		)					->  Union[str, Any]:
			return x + 2






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	'''x = 3'''
					_UpperCamelCase					:List[str]					     =	{}
					_UpperCamelCase					:int					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3}   )

					_UpperCamelCase					:Tuple					     =	'''x = y'''
					_UpperCamelCase					:Optional[int]					     =	{'''y''': 5}
					_UpperCamelCase					:Optional[Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 5, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''y = add_two(x)'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )

					# Won't work without the tool
					with CaptureStdout() as out:
								_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result is None
					assert "tried to execute add_two" in out.out
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	'''x = 3'''
					_UpperCamelCase					:int					     =	{}
					_UpperCamelCase					:List[Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3}   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					_UpperCamelCase					:str					     =	{'''x''': 3}
					_UpperCamelCase					:Dict					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	'''x = 3\ny = 5'''
					_UpperCamelCase					:int					     =	{}
					_UpperCamelCase					:Optional[int]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	'''text = f\'This is x: {x}.\''''
					_UpperCamelCase					:List[str]					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == "This is x: 3."
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''text''': '''This is x: 3.'''}   )
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	'''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					_UpperCamelCase					:Dict					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 2}   )

					_UpperCamelCase					:Union[str, Any]					     =	{'''x''': 8}
					_UpperCamelCase					:Optional[int]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 8, '''y''': 5}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	'''test_list = [x, add_two(x)]'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:str					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				[3, 5]   )
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_list''': [3, 5]}   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''y = x'''
					_UpperCamelCase					:List[str]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 3
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''y''': 3}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	'''test_list = [x, add_two(x)]\ntest_list[1]'''
					_UpperCamelCase					:Optional[Any]					     =	{'''x''': 3}
					_UpperCamelCase					:Any					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_list''': [3, 5]}   )

					_UpperCamelCase					:str					     =	'''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					_UpperCamelCase					:Optional[int]					     =	{'''x''': 3}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''add_two''': add_two}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 5
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}   )





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	'''x = 0\nfor i in range(3):\n    x = i'''
					_UpperCamelCase					:Tuple					     =	{}
					_UpperCamelCase					:Union[str, Any]					     =	evaluate(SCREAMING_SNAKE_CASE__					,				{'''range''': range}					,				state=SCREAMING_SNAKE_CASE__   )
					assert result == 2
					self.assertDictEqual(SCREAMING_SNAKE_CASE__					,				{'''x''': 2, '''i''': 2}   )

