

'''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


_lowerCAmelCase            =		16
_lowerCAmelCase            =		32




def 		UpperCamelCase			(					a					) ->      Optional[int]:






     '''simple docstring'''
     return int(x / 2**20					)






class     _SCREAMING_SNAKE_CASE       :



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



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




def 		UpperCamelCase			(					a						,		a = 16						,		a = "bert-base-cased"						,		a = 320						,		a = 160						,		) ->      Optional[int]:






     '''simple docstring'''
     __magic_name__		  =							AutoTokenizer.from_pretrained(_a					)
     __magic_name__		  =							load_dataset(
         '''glue'''						,		'''mrpc'''						,		split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}					)

     def tokenize_function(a					):
          # max_length=None => use the model max length (it's actually the default)
          __magic_name__		  =							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
     __magic_name__		  =							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
     __magic_name__		  =							tokenized_datasets.rename_column('''label'''						,		'''labels'''					)

     def collate_fn(a					):
          # 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.
     __magic_name__		  =							DataLoader(
         tokenized_datasets['''train''']						,		shuffle=_a						,		collate_fn=_a						,		batch_size=_a					)
     __magic_name__		  =							DataLoader(
         tokenized_datasets['''validation''']						,		shuffle=_a						,		collate_fn=_a						,		batch_size=_a					)

     return train_dataloader, eval_dataloader




def 		UpperCamelCase			(					a						,		a					) ->      int:






     '''simple docstring'''
     __magic_name__		  =							Accelerator()

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

     set_seed(_a					)
     __magic_name__  ,    __magic_name__		  =							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)
     __magic_name__		  =							AutoModelForSequenceClassification.from_pretrained(_a						,		return_dict=_a					)

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

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

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

     # Now we train the model
     __magic_name__		  =							{}
     for epoch in range(_a						,		_a					):
          with TorchTracemalloc() as tracemalloc:
               model.train()
               for step, batch in enumerate(_a					):
                    __magic_name__		  =							model(**_a					)
                    __magic_name__		  =							outputs.loss
                    __magic_name__		  =							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					)					)					)
          __magic_name__		  =							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 		UpperCamelCase			(					) ->      List[Any]:






     '''simple docstring'''
     __magic_name__		  =							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.'''						,		)
     __magic_name__		  =							parser.parse_args()
     __magic_name__		  =							{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
     training_function(_a						,		_a					)


if __name__ == "__main__":
      main()









"""simple docstring"""


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




