






"""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


_UpperCamelCase    :   List[str]   			=						16
_UpperCamelCase    :   List[str]   			=						32




def       snake_case  (A_				:List[str]				):







  '''simple docstring'''

  return int(x / 2**2_0				)






class 						snake_case  :





    def __enter__(							self     : int      ):


      '''simple docstring'''




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





    def __exit__(							self     : Optional[Any]    ,		*A     : Union[str, Any]      ):


      '''simple docstring'''




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




def       snake_case  (A_				:Accelerator     ,      A_				:int = 1_6     ,      A_				:str = "bert-base-cased"     ,      A_				:int = 3_2_0     ,      A_				:int = 1_6_0     ,      ):







  '''simple docstring'''

  a					:      int    =       AutoTokenizer.from_pretrained(A_				)
  a					:      Union[str, Any]    =       load_dataset(
      'glue'     ,      'mrpc'     ,      split={'train': f'''train[:{n_train}]''', 'validation': f'''validation[:{n_val}]'''}				)

  def tokenize_function(A_				:int				):
    # max_length=None => use the model max length (it's actually the default)
    a					:      List[str]    =       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
  a					:      Tuple    =       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
  a					:      Optional[Any]    =       tokenized_datasets.rename_column('label'     ,      'labels'				)

  def collate_fn(A_				:Tuple				):
    # 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=1_2_8     ,      return_tensors='pt'				)
    return tokenizer.pad(A_     ,      padding='longest'     ,      return_tensors='pt'				)

  # Instantiate dataloaders.
  a					:      Optional[Any]    =       DataLoader(
      tokenized_datasets['train']     ,      shuffle=A_     ,      collate_fn=A_     ,      batch_size=A_				)
  a					:      int    =       DataLoader(
      tokenized_datasets['validation']     ,      shuffle=A_     ,      collate_fn=A_     ,      batch_size=A_				)

  return train_dataloader, eval_dataloader




def       snake_case  (A_				:Optional[int]     ,      A_				:Optional[int]				):







  '''simple docstring'''

  a					:      Any    =       Accelerator()

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

  set_seed(A_				)
  a,					a					:      Optional[int]    =       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)
  a					:      Optional[Any]    =       AutoModelForSequenceClassification.from_pretrained(A_     ,      return_dict=A_				)

  # Instantiate optimizer
  a					:      Union[str, Any]    =       (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  a					:      Union[str, Any]    =       optimizer_cls(params=model.parameters()     ,      lr=A_				)

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

  # We need to keep track of how many total steps we have iterated over
  a					:      Optional[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					:      Union[str, Any]    =       {}
  for epoch in range(A_     ,      A_				):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(A_				):
        a					:      Any    =       model(**A_				)
        a					:      int    =       outputs.loss
        a					:      Any    =       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				)				)				)
    a					:      Tuple    =       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       snake_case  ():







  '''simple docstring'''

  a					:      List[str]    =       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=3_2_0     ,      help='Number of training examples to use.'     ,      )
  parser.add_argument(
      '--n_val'     ,      type=A_     ,      default=1_6_0     ,      help='Number of validation examples to use.'     ,      )
  parser.add_argument(
      '--num_epochs'     ,      type=A_     ,      default=1     ,      help='Number of train epochs.'     ,      )
  a					:      int    =       parser.parse_args()
  a					:      Union[str, Any]    =       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
  training_function(A_     ,      A_				)


if __name__ == "__main__":
   main()












"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase    :   Union[str, Any]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return 1E-4




