





'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor




class  UpperCAmelCase     (			unittest.TestCase   ):




      def __init__(			self			: List[Any]			,			__snake_case			: Optional[int]			,			__snake_case			: int=7			,			__snake_case			: int=3			,			__snake_case			: Union[str, Any]=30			,			__snake_case			: int=4_00			,			__snake_case			: int=True			,			__snake_case			: Tuple=None			,			__snake_case			: List[str]=True			,			__snake_case			: int=[0.5, 0.5, 0.5]			,			__snake_case			: str=[0.5, 0.5, 0.5]			,			__snake_case			: Optional[Any]=True			,			__snake_case			: Dict=1 / 2_55			,			__snake_case			: List[Any]=True			,			)				->      Optional[int]:
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            _lowerCAmelCase							     =							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
            _lowerCAmelCase							     =							parent
            _lowerCAmelCase							     =							batch_size
            _lowerCAmelCase							     =							num_channels
            _lowerCAmelCase							     =							min_resolution
            _lowerCAmelCase							     =							max_resolution
            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size
            _lowerCAmelCase							     =							do_normalize
            _lowerCAmelCase							     =							image_mean
            _lowerCAmelCase							     =							image_std
            _lowerCAmelCase							     =							do_rescale
            _lowerCAmelCase							     =							rescale_factor
            _lowerCAmelCase							     =							do_pad




      def 				lowercase__    (			self			: List[str]  )				->      Optional[Any]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: List[str]			,			__snake_case			: Tuple=False  )				->      Any:
            if not batched:
                  _lowerCAmelCase							     =							image_inputs[0]
                  if isinstance(__snake_case			,			Image.Image  ):
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							image.size
                  else:
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							image.shape[1], image.shape[2]
                  if w < h:
                        _lowerCAmelCase							     =							int(self.size["""shortest_edge"""] * h / w  )
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                  elif w > h:
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                        _lowerCAmelCase							     =							int(self.size["""shortest_edge"""] * w / h  )
                  else:
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]
                        _lowerCAmelCase							     =							self.size["""shortest_edge"""]

            else:
                  _lowerCAmelCase							     =							[]
                  for image in image_inputs:
                        _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.get_expected_values([image]  )
                        expected_values.append((expected_height, expected_width)  )
                  _lowerCAmelCase							     =							max(__snake_case			,			key=lambda __snake_case  : item[0]  )[0]
                  _lowerCAmelCase							     =							max(__snake_case			,			key=lambda __snake_case  : item[1]  )[1]

            return expected_height, expected_width




@require_torch
@require_vision
class  UpperCAmelCase     (			snake_case_							,		unittest.TestCase   ):
      _lowercase:			Any						     =   DeformableDetrImageProcessor if is_vision_available() else None




      def 				lowercase__    (			self			: Dict  )				->      str:
            _lowerCAmelCase							     =							DeformableDetrImageProcessingTester(self  )




      @property
      def 				lowercase__    (			self			: str  )				->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 				lowercase__    (			self			: List[Any]  )				->      List[str]:
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(__snake_case			,			"""image_mean"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""image_std"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_normalize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_resize"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_rescale"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""do_pad"""  )  )
            self.assertTrue(hasattr(__snake_case			,			"""size"""  )  )




      def 				lowercase__    (			self			: Optional[Any]  )				->      Optional[Any]:
            _lowerCAmelCase							     =							self.image_processing_class.from_dict(self.image_processor_dict  )
            self.assertEqual(image_processor.size			,			{"""shortest_edge""": 18, """longest_edge""": 13_33}  )
            self.assertEqual(image_processor.do_pad			,			__snake_case  )

            _lowerCAmelCase							     =							self.image_processing_class.from_dict(
                self.image_processor_dict			,			size=42			,			max_size=84			,			pad_and_return_pixel_mask=__snake_case  )
            self.assertEqual(image_processor.size			,			{"""shortest_edge""": 42, """longest_edge""": 84}  )
            self.assertEqual(image_processor.do_pad			,			__snake_case  )




      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            pass




      def 				lowercase__    (			self			: Tuple  )				->      List[Any]:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PIL images
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			Image.Image  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

            self.assertEqual(
                encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

            # Test batched
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__snake_case  )

            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values
            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,			)




      def 				lowercase__    (			self			: Dict  )				->      Any:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random numpy tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			numpify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			np.ndarray  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

            self.assertEqual(
                encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__snake_case  )

            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,			)




      def 				lowercase__    (			self			: Optional[Any]  )				->      Any:
            # Initialize image_processing
            _lowerCAmelCase							     =							self.image_processing_class(**self.image_processor_dict  )
            # create random PyTorch tensors
            _lowerCAmelCase							     =							prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__snake_case			,			torchify=__snake_case  )
            for image in image_inputs:
                  self.assertIsInstance(__snake_case			,			torch.Tensor  )

            # Test not batched input
            _lowerCAmelCase							     =							image_processing(image_inputs[0]			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case  )

            self.assertEqual(
                encoded_images.shape			,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,			)

            # Test batched
            _lowerCAmelCase							     =							image_processing(__snake_case			,			return_tensors="""pt"""  ).pixel_values

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							self.image_processor_tester.get_expected_values(__snake_case			,			batched=__snake_case  )

            self.assertEqual(
                encoded_images.shape			,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )			,			)




      @slow
      def 				lowercase__    (			self			: Optional[int]  )				->      Tuple:
            # prepare image and target
            _lowerCAmelCase							     =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
            with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""			,			"""r"""  ) as f:
                  _lowerCAmelCase							     =							json.loads(f.read()  )

            _lowerCAmelCase							     =							{"""image_id""": 3_97_69, """annotations""": target}

            # encode them
            _lowerCAmelCase							     =							DeformableDetrImageProcessor()
            _lowerCAmelCase							     =							image_processing(images=__snake_case			,			annotations=__snake_case			,			return_tensors="""pt"""  )

            # verify pixel values
            _lowerCAmelCase							     =							torch.Size([1, 3, 8_00, 10_66]  )
            self.assertEqual(encoding["""pixel_values"""].shape			,			__snake_case  )

            _lowerCAmelCase							     =							torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
            self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,			__snake_case			,			atol=1E-4  )  )

            # verify area
            _lowerCAmelCase							     =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,			__snake_case  )  )
            # verify boxes
            _lowerCAmelCase							     =							torch.Size([6, 4]  )
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,			__snake_case  )
            _lowerCAmelCase							     =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,			__snake_case			,			atol=1E-3  )  )
            # verify image_id
            _lowerCAmelCase							     =							torch.tensor([3_97_69]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,			__snake_case  )  )
            # verify is_crowd
            _lowerCAmelCase							     =							torch.tensor([0, 0, 0, 0, 0, 0]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,			__snake_case  )  )
            # verify class_labels
            _lowerCAmelCase							     =							torch.tensor([75, 75, 63, 65, 17, 17]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,			__snake_case  )  )
            # verify orig_size
            _lowerCAmelCase							     =							torch.tensor([4_80, 6_40]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,			__snake_case  )  )
            # verify size
            _lowerCAmelCase							     =							torch.tensor([8_00, 10_66]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,			__snake_case  )  )




      @slow
      def 				lowercase__    (			self			: Optional[Any]  )				->      int:
            # prepare image, target and masks_path
            _lowerCAmelCase							     =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
            with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""			,			"""r"""  ) as f:
                  _lowerCAmelCase							     =							json.loads(f.read()  )

            _lowerCAmelCase							     =							{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

            _lowerCAmelCase							     =							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""  )

            # encode them
            _lowerCAmelCase							     =							DeformableDetrImageProcessor(format="""coco_panoptic"""  )
            _lowerCAmelCase							     =							image_processing(images=__snake_case			,			annotations=__snake_case			,			masks_path=__snake_case			,			return_tensors="""pt"""  )

            # verify pixel values
            _lowerCAmelCase							     =							torch.Size([1, 3, 8_00, 10_66]  )
            self.assertEqual(encoding["""pixel_values"""].shape			,			__snake_case  )

            _lowerCAmelCase							     =							torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
            self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]			,			__snake_case			,			atol=1E-4  )  )

            # verify area
            _lowerCAmelCase							     =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]			,			__snake_case  )  )
            # verify boxes
            _lowerCAmelCase							     =							torch.Size([6, 4]  )
            self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape			,			__snake_case  )
            _lowerCAmelCase							     =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]			,			__snake_case			,			atol=1E-3  )  )
            # verify image_id
            _lowerCAmelCase							     =							torch.tensor([3_97_69]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]			,			__snake_case  )  )
            # verify is_crowd
            _lowerCAmelCase							     =							torch.tensor([0, 0, 0, 0, 0, 0]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]			,			__snake_case  )  )
            # verify class_labels
            _lowerCAmelCase							     =							torch.tensor([17, 17, 63, 75, 75, 93]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]			,			__snake_case  )  )
            # verify masks
            _lowerCAmelCase							     =							82_28_73
            self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()			,			__snake_case  )
            # verify orig_size
            _lowerCAmelCase							     =							torch.tensor([4_80, 6_40]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]			,			__snake_case  )  )
            # verify size
            _lowerCAmelCase							     =							torch.tensor([8_00, 10_66]  )
            self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]			,			__snake_case  )  )







import argparse
import json
import os

import evaluate
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			: Any							   =		16
__lowerCamelCase			: List[Any]							   =		32



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int = 16 ,     __UpperCamelCase    :       str = "bert-base-cased"	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      load_dataset("""glue""" ,     """mrpc"""	)

  def tokenize_function(__UpperCamelCase    :       Optional[Any]	):
    # max_length=None => use the model max length (it's actually the default)
    SCREAMING_SNAKE_CASE__										=      tokenizer(examples["""sentence1"""] ,     examples["""sentence2"""] ,     truncation=__UpperCamelCase ,     max_length=__UpperCamelCase	)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  SCREAMING_SNAKE_CASE__										=      datasets.map(
      __UpperCamelCase ,     batched=__UpperCamelCase ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=__UpperCamelCase	)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  SCREAMING_SNAKE_CASE__										=      tokenized_datasets.rename_column("""label""" ,     """labels"""	)

  def collate_fn(__UpperCamelCase    :       List[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(__UpperCamelCase ,     padding="""max_length""" ,     max_length=1_28 ,     return_tensors="""pt"""	)
    return tokenizer.pad(__UpperCamelCase ,     padding="""longest""" ,     return_tensors="""pt"""	)

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""train"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""validation"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)

  return train_dataloader, eval_dataloader



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__										=      0
  for step, batch in enumerate(__UpperCamelCase	):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device	)
    with torch.no_grad():
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      outputs.logits.argmax(dim=-1	)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.gather(
        (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(__UpperCamelCase	) - 1:
        SCREAMING_SNAKE_CASE__										=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
        SCREAMING_SNAKE_CASE__										=      references[: len(eval_dataloader.dataset	) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase ,     references=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      metric.compute()
  return eval_metric["accuracy"]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator()

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

  set_seed(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_dataloaders(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE__										=      AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase ,     return_dict=__UpperCamelCase	)

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

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__										=      accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      (len(__UpperCamelCase	) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    SCREAMING_SNAKE_CASE__										=      get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase ,     num_warmup_steps=0 ,     num_training_steps=__UpperCamelCase ,     )
  else:
    SCREAMING_SNAKE_CASE__										=      DummyScheduler(__UpperCamelCase ,     total_num_steps=__UpperCamelCase ,     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.
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.prepare(
      __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

  # We need to keep track of how many total steps we have iterated over
  SCREAMING_SNAKE_CASE__										=      0
  # We also need to keep track of the stating epoch so files are named properly
  SCREAMING_SNAKE_CASE__										=      0
  SCREAMING_SNAKE_CASE__										=      evaluate.load("""glue""" ,     """mrpc"""	)
  SCREAMING_SNAKE_CASE__										=      num_epochs

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__										=      args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint	)
    SCREAMING_SNAKE_CASE__										=      args.resume_from_checkpoint.split("""epoch_"""	)[1]
    SCREAMING_SNAKE_CASE__										=      """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__										=      int(__UpperCamelCase	) + 1
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint performance:""" ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint's scheduler's lr:""" ,     lr_scheduler.get_lr()[0]	)
    accelerator.print("""resumed optimizers's lr:""" ,     optimizer.param_groups[0]["""lr"""]	)
    with open(os.path.join(args.output_dir ,     f"""state_{starting_epoch-1}.json"""	) ,     """r"""	) as f:
      SCREAMING_SNAKE_CASE__										=      json.load(__UpperCamelCase	)
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  SCREAMING_SNAKE_CASE__										=      {}
  for epoch in range(__UpperCamelCase ,     __UpperCamelCase	):
    model.train()
    for step, batch in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      outputs.loss
      SCREAMING_SNAKE_CASE__										=      loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__										=      f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__										=      os.path.join(args.output_dir ,     __UpperCamelCase	)
    accelerator.save_state(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      accuracy
    SCREAMING_SNAKE_CASE__										=      lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__										=      optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__										=      epoch
    SCREAMING_SNAKE_CASE__										=      overall_step
    accelerator.print(f"""epoch {epoch}:""" ,     __UpperCamelCase	)

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir ,     f"""state_{epoch}.json"""	) ,     """w"""	) as f:
        json.dump(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
  parser.add_argument(
      """--model_name_or_path""" ,     type=__UpperCamelCase ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=__UpperCamelCase ,     )
  parser.add_argument(
      """--output_dir""" ,     type=__UpperCamelCase ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
  parser.add_argument(
      """--resume_from_checkpoint""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If the training should continue from a checkpoint folder.""" ,     )
  parser.add_argument(
      """--partial_train_epoch""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If passed, the training will stop after this number of epochs.""" ,     )
  parser.add_argument(
      """--num_epochs""" ,     type=__UpperCamelCase ,     default=2 ,     help="""Number of train epochs.""" ,     )
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  SCREAMING_SNAKE_CASE__										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(__UpperCamelCase ,     __UpperCamelCase	)


if __name__ == "__main__":
   main()
