




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


_snake_case										=       16
_snake_case										=       32







def 				lowerCAmelCase_	(     snake_case_     ):
      return int(x / 2**20     )







class 						lowercase							:

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



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







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = 16,snake_case_ = "bert-base-cased",snake_case_ = 320,snake_case_ = 160,):
      _A							:     List[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      _A							:     List[str]			=						load_dataset(
          """glue""","""mrpc""",split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}     )

      def tokenize_function(snake_case_     ):
            # max_length=None => use the model max length (it's actually the default)
            _A							:     Union[str, Any]			=						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
      _A							:     List[Any]			=						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
      _A							:     List[Any]			=						tokenized_datasets.rename_column("""label""","""labels"""     )

      def collate_fn(snake_case_     ):
            # 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.
      _A							:     Dict			=						DataLoader(
          tokenized_datasets["""train"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )
      _A							:     int			=						DataLoader(
          tokenized_datasets["""validation"""],shuffle=snake_case_,collate_fn=snake_case_,batch_size=snake_case_     )

      return train_dataloader, eval_dataloader







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      # Initialize accelerator
      _A							:     Tuple			=						Accelerator()

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

      set_seed(snake_case_     )
      _A			,   _A							:     Union[str, Any]			=						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)
      _A							:     Optional[int]			=						AutoModelForSequenceClassification.from_pretrained(snake_case_,return_dict=snake_case_     )

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

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

      # Now we train the model
      _A							:     Optional[Any]			=						{}
      for epoch in range(snake_case_,snake_case_     ):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(snake_case_     ):
                        _A							:     Dict			=						model(**snake_case_     )
                        _A							:     Dict			=						outputs.loss
                        _A							:     Dict			=						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     )     )     )
            _A							:     List[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(snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Optional[Any]			=						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.""",)
      _A							:     Optional[int]			=						parser.parse_args()
      _A							:     Optional[int]			=						{"""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 is_flaky, 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 DonutImageProcessor







class 						lowercase							(   unittest.TestCase		):

     def __init__(  self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=False		,     _a=True		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     )						->							Dict:
           _A							:     str			=						parent
           _A							:     int			=						batch_size
           _A							:     Optional[int]			=						num_channels
           _A							:     List[Any]			=						image_size
           _A							:     int			=						min_resolution
           _A							:     Optional[int]			=						max_resolution
           _A							:     Any			=						do_resize
           _A							:     List[str]			=						size if size is not None else {"""height""": 18, """width""": 20}
           _A							:     Optional[int]			=						do_thumbnail
           _A							:     str			=						do_align_axis
           _A							:     List[Any]			=						do_pad
           _A							:     Optional[Any]			=						do_normalize
           _A							:     Tuple			=						image_mean
           _A							:     List[str]			=						image_std



     def 					a__	(  self				)						->							Optional[int]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_thumbnail": self.do_thumbnail,
               "do_align_long_axis": self.do_align_axis,
               "do_pad": self.do_pad,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }







@require_torch
@require_vision
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						DonutImageProcessor if is_vision_available() else None

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						DonutImageProcessingTester(self				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_a		,     """do_resize"""				)				)
           self.assertTrue(hasattr(_a		,     """size"""				)				)
           self.assertTrue(hasattr(_a		,     """do_thumbnail"""				)				)
           self.assertTrue(hasattr(_a		,     """do_align_long_axis"""				)				)
           self.assertTrue(hasattr(_a		,     """do_pad"""				)				)
           self.assertTrue(hasattr(_a		,     """do_normalize"""				)				)
           self.assertTrue(hasattr(_a		,     """image_mean"""				)				)
           self.assertTrue(hasattr(_a		,     """image_std"""				)				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size		,     {"""height""": 18, """width""": 20}				)

           _A							:     Optional[int]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=42				)
           self.assertEqual(image_processor.size		,     {"""height""": 42, """width""": 42}				)

           # Previous config had dimensions in (width, height) order
           _A							:     List[str]			=						self.image_processing_class.from_dict(self.image_processor_dict		,     size=(42, 84)				)
           self.assertEqual(image_processor.size		,     {"""height""": 84, """width""": 42}				)

     def 					a__	(  self				)						->							Union[str, Any]:
           pass

     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           _A							:     Dict			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     Image.Image				)

           # Test not batched input
           _A							:     Union[str, Any]			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     Any			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

     @is_flaky()
     def 					a__	(  self				)						->							Dict:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           _A							:     Tuple			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     np.ndarray				)

           # Test not batched input
           _A							:     int			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     List[str]			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )



     @is_flaky()
     def 					a__	(  self				)						->							Optional[int]:
           # Initialize image_processing
           _A							:     str			=						self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           _A							:     Optional[int]			=						prepare_image_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a				)
           for image in image_inputs:
                 self.assertIsInstance(_a		,     torch.Tensor				)

           # Test not batched input
           _A							:     Any			=						image_processing(image_inputs[0]		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

           # Test batched
           _A							:     str			=						image_processing(_a		,     return_tensors="""pt"""				).pixel_values
           self.assertEqual(
               encoded_images.shape		,     (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,     )

