



'''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  			=						16
__a  			=						32





def 		__snake_case(							_lowerCAmelCase						)						->					Dict:
      return int(x / 2**20						)







class        UpperCAmelCase_			:
    """simple docstring"""







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




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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16	,  _lowerCAmelCase = "bert-base-cased"	,  _lowerCAmelCase = 320	,  _lowerCAmelCase = 160	,  )						->					Tuple:
      snake_case__    :       int       		= AutoTokenizer.from_pretrained(_lowerCAmelCase						)
      snake_case__    :       int       		= 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)
            snake_case__    :       Tuple       		= 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
      snake_case__    :       List[str]       		= 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
      snake_case__    :       List[str]       		= 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=128	,  return_tensors="""pt"""						)
            return tokenizer.pad(_lowerCAmelCase	,  padding="""longest"""	,  return_tensors="""pt"""						)

      # Instantiate dataloaders.
      snake_case__    :       Dict       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Optional[int]       		= DataLoader(
          tokenized_datasets["""validation"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)

      return train_dataloader, eval_dataloader





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Dict:
      # Initialize accelerator
      snake_case__    :       Tuple       		= Accelerator()

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

      set_seed(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       List[str]       		= 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)
      snake_case__    :       Optional[int]       		= AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase	,  return_dict=_lowerCAmelCase						)

      # Instantiate optimizer
      snake_case__    :       Optional[int]       		= (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      snake_case__    :       Optional[Any]       		= optimizer_cls(params=model.parameters()	,  lr=_lowerCAmelCase						)

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

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

      # Now we train the model
      snake_case__    :       Optional[int]       		= {}
      for epoch in range(_lowerCAmelCase	,  _lowerCAmelCase						):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(_lowerCAmelCase						):
                        snake_case__    :       List[Any]       		= model(**_lowerCAmelCase						)
                        snake_case__    :       Optional[int]       		= outputs.loss
                        snake_case__    :       Union[str, Any]       		= 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						)						)						)
            snake_case__    :       str       		= 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 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= 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=320	,  help="""Number of training examples to use."""	,  )
      parser.add_argument(
          """--n_val"""	,  type=_lowerCAmelCase	,  default=160	,  help="""Number of validation examples to use."""	,  )
      parser.add_argument(
          """--num_epochs"""	,  type=_lowerCAmelCase	,  default=1	,  help="""Number of train epochs."""	,  )
      snake_case__    :       int       		= parser.parse_args()
      snake_case__    :       Tuple       		= {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()





'''simple docstring'''





import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""swish"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       int       		= get_activation("""silu"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Union[str, Any]       		= get_activation("""mish"""		)

          self.assertIsInstance(snake_case_					,	nn.Mish		)

          self.assertEqual(act(torch.tensor(-200					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""gelu"""		)

          self.assertIsInstance(snake_case_					,	nn.GELU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)
