'''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=  1_6
a=  3_2







def  _UpperCamelCase (     _a	:	Any			):




    """simple docstring"""



    return int(x / 2**2_0			)






class 			__lowercase		:







     """simple docstring"""






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







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







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	List[Any] = 1_6				,     _a	:	Tuple = "bert-base-cased"				,     _a	:	Any = 3_2_0				,     _a	:	Union[str, Any] = 1_6_0				,     ):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  AutoTokenizer.from_pretrained(__lowerCAmelCase			)
    __UpperCamelCase	:	Any		    =  load_dataset(
        'glue'				,     'mrpc'				,     split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""}			)

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

    def collate_fn(_a	:	Any			):
        # 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=1_2_8				,     return_tensors='pt'			)
        return tokenizer.pad(__lowerCAmelCase				,     padding='longest'				,     return_tensors='pt'			)

    # Instantiate dataloaders.
    __UpperCamelCase	:	Any		    =  DataLoader(
        tokenized_datasets['train']				,     shuffle=__lowerCAmelCase				,     collate_fn=__lowerCAmelCase				,     batch_size=__lowerCAmelCase			)
    __UpperCamelCase	:	List[str]		    =  DataLoader(
        tokenized_datasets['validation']				,     shuffle=__lowerCAmelCase				,     collate_fn=__lowerCAmelCase				,     batch_size=__lowerCAmelCase			)

    return train_dataloader, eval_dataloader







def  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  Accelerator()

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

    set_seed(__lowerCAmelCase			)
    __UpperCamelCase	:	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)
    __UpperCamelCase	:	Optional[int]		    =  AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase				,     return_dict=__lowerCAmelCase			)

    # Instantiate optimizer
    __UpperCamelCase	:	Dict		    =  (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase	:	str		    =  optimizer_cls(params=model.parameters()				,     lr=__lowerCAmelCase			)

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

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

    # Now we train the model
    __UpperCamelCase	:	Optional[Any]		    =  {}
    for epoch in range(__lowerCAmelCase				,     __lowerCAmelCase			):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(__lowerCAmelCase			):
                __UpperCamelCase	:	Union[str, Any]		    =  model(**__lowerCAmelCase			)
                __UpperCamelCase	:	Union[str, Any]		    =  outputs.loss
                __UpperCamelCase	:	List[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			)			)			)
        __UpperCamelCase	:	Optional[int]		    =  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  _UpperCamelCase (     ):




    """simple docstring"""



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


if __name__ == "__main__":
 main()




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



