



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	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[Any]:
   return int(x / 2**20     )



class     lowerCAmelCase_		:
       def __enter__(      self	)					->		List[str]:
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          UpperCamelCase   :  List[Any]						     =     torch.cuda.memory_allocated()
          return self






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






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

   def tokenize_function(snake_case__	:   Tuple     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase   :  List[str]						     =     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
   UpperCamelCase   :  Dict						     =     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
   UpperCamelCase   :  List[str]						     =     tokenized_datasets.rename_column('label'	,					'labels'     )

   def collate_fn(snake_case__	:   str     ):
      # 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.
   UpperCamelCase   :  List[Any]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[str]						     =     DataLoader(
       tokenized_datasets['validation']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )

   return train_dataloader, eval_dataloader
def 		UpperCamelCase     (       snake_case__	:   Any	,					snake_case__	:   int     )     ->					Optional[int]:
   # Initialize accelerator
   UpperCamelCase   :  Optional[int]						     =     Accelerator()

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

   set_seed(snake_case__     )
   UpperCamelCase   :  str						     =     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)
   UpperCamelCase   :  str						     =     AutoModelForSequenceClassification.from_pretrained(snake_case__	,					return_dict=snake_case__     )

   # Instantiate optimizer
   UpperCamelCase   :  Any						     =     (
       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=snake_case__     )

   if accelerator.state.deepspeed_plugin is not None:
      UpperCamelCase   :  Tuple						     =     accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      UpperCamelCase   :  Any						     =     1
   UpperCamelCase   :  Dict						     =     (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
   ):
      UpperCamelCase   :  Dict						     =     get_linear_schedule_with_warmup(
          optimizer=snake_case__	,					num_warmup_steps=0	,					num_training_steps=snake_case__	,					)
   else:
      UpperCamelCase   :  List[str]						     =     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.
   UpperCamelCase   :  int						     =     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
   UpperCamelCase   :  Optional[int]						     =     0
   # We also need to keep track of the stating epoch so files are named properly
   UpperCamelCase   :  Union[str, Any]						     =     0

   # Now we train the model
   UpperCamelCase   :  int						     =     {}
   for epoch in range(snake_case__	,					snake_case__     ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(snake_case__     ):
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
            UpperCamelCase   :  Dict						     =     outputs.loss
            UpperCamelCase   :  Any						     =     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     )     )     )
      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(snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					int:
   UpperCamelCase   :  Optional[int]						     =     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.'	,					)
   UpperCamelCase   :  int						     =     parser.parse_args()
   UpperCamelCase   :  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()








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      self	)					->		List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )
       def  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )



