




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DinatBackbone, DinatForImageClassification, DinatModel
 from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class 	_lowerCamelCase  :

      """simple docstring"""

      def __init__(       self   :	Optional[int]	,    snake_case   :	Union[str, Any]	,    snake_case   :	List[str]=13	,    snake_case   :	List[Any]=64	,    snake_case   :	List[Any]=4	,    snake_case   :	Any=3	,    snake_case   :	Any=16	,    snake_case   :	Any=[1, 2, 1]	,    snake_case   :	str=[2, 4, 8]	,    snake_case   :	str=3	,    snake_case   :	int=[[3], [1, 2], [1]]	,    snake_case   :	List[str]=2.0	,    snake_case   :	Optional[Any]=True	,    snake_case   :	Any=0.0	,    snake_case   :	Optional[int]=0.0	,    snake_case   :	List[str]=0.1	,    snake_case   :	Optional[Any]="gelu"	,    snake_case   :	Union[str, Any]=True	,    snake_case   :	str=0.02	,    snake_case   :	Optional[Any]=1E-5	,    snake_case   :	List[str]=True	,    snake_case   :	Tuple=None	,    snake_case   :	str=True	,    snake_case   :	Tuple=10	,    snake_case   :	str=["stage1", "stage2"]	,    snake_case   :	int=[1, 2]	,    ):
          __UpperCamelCase											=  parent
          __UpperCamelCase											=  batch_size
          __UpperCamelCase											=  image_size
          __UpperCamelCase											=  patch_size
          __UpperCamelCase											=  num_channels
          __UpperCamelCase											=  embed_dim
          __UpperCamelCase											=  depths
          __UpperCamelCase											=  num_heads
          __UpperCamelCase											=  kernel_size
          __UpperCamelCase											=  dilations
          __UpperCamelCase											=  mlp_ratio
          __UpperCamelCase											=  qkv_bias
          __UpperCamelCase											=  hidden_dropout_prob
          __UpperCamelCase											=  attention_probs_dropout_prob
          __UpperCamelCase											=  drop_path_rate
          __UpperCamelCase											=  hidden_act
          __UpperCamelCase											=  patch_norm
          __UpperCamelCase											=  layer_norm_eps
          __UpperCamelCase											=  initializer_range
          __UpperCamelCase											=  is_training
          __UpperCamelCase											=  scope
          __UpperCamelCase											=  use_labels
          __UpperCamelCase											=  num_labels
          __UpperCamelCase											=  out_features
          __UpperCamelCase											=  out_indices







      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

          __UpperCamelCase											=  None
          if self.use_labels:
              __UpperCamelCase											=  ids_tensor([self.batch_size]	,    self.num_labels			)

          __UpperCamelCase											=  self.get_config()

          return config, pixel_values, labels







      def  snake_case		(       self   :	Any			):
          return DinatConfig(
              num_labels=self.num_labels	,    image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    embed_dim=self.embed_dim	,    depths=self.depths	,    num_heads=self.num_heads	,    kernel_size=self.kernel_size	,    dilations=self.dilations	,    mlp_ratio=self.mlp_ratio	,    qkv_bias=self.qkv_bias	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    drop_path_rate=self.drop_path_rate	,    hidden_act=self.hidden_act	,    patch_norm=self.patch_norm	,    layer_norm_eps=self.layer_norm_eps	,    initializer_range=self.initializer_range	,    out_features=self.out_features	,    out_indices=self.out_indices	,    )







      def  snake_case		(       self   :	Optional[Any]	,    snake_case   :	List[Any]	,    snake_case   :	int	,    snake_case   :	Union[str, Any]			):
          __UpperCamelCase											=  DinatModel(config=snake_case			)
          model.to(snake_case			)
          model.eval()
          __UpperCamelCase											=  model(snake_case			)

          __UpperCamelCase											=  __UpperCamelCase											=  (config.image_size // config.patch_size) // (2 ** (len(config.depths			) - 1))
          __UpperCamelCase											=  int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

          self.parent.assertEqual(
              result.last_hidden_state.shape	,    (self.batch_size, expected_height, expected_width, expected_dim)			)







      def  snake_case		(       self   :	str	,    snake_case   :	List[str]	,    snake_case   :	Union[str, Any]	,    snake_case   :	int			):
          __UpperCamelCase											=  DinatForImageClassification(snake_case			)
          model.to(snake_case			)
          model.eval()
          __UpperCamelCase											=  model(snake_case	,    labels=snake_case			)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)			)

          # test greyscale images
          __UpperCamelCase											=  1
          __UpperCamelCase											=  DinatForImageClassification(snake_case			)
          model.to(snake_case			)
          model.eval()

          __UpperCamelCase											=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
          __UpperCamelCase											=  model(snake_case			)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)			)







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]	,    snake_case   :	Dict	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  DinatBackbone(config=snake_case			)
          model.to(snake_case			)
          model.eval()
          __UpperCamelCase											=  model(snake_case			)

          # verify hidden states
          self.parent.assertEqual(len(result.feature_maps			)	,    len(config.out_features			)			)
          self.parent.assertListEqual(list(result.feature_maps[0].shape			)	,    [self.batch_size, model.channels[0], 16, 16]			)

          # verify channels
          self.parent.assertEqual(len(model.channels			)	,    len(config.out_features			)			)

          # verify backbone works with out_features=None
          __UpperCamelCase											=  None
          __UpperCamelCase											=  DinatBackbone(config=snake_case			)
          model.to(snake_case			)
          model.eval()
          __UpperCamelCase											=  model(snake_case			)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps			)	,    1			)
          self.parent.assertListEqual(list(result.feature_maps[0].shape			)	,    [self.batch_size, model.channels[-1], 4, 4]			)

          # verify channels
          self.parent.assertEqual(len(model.channels			)	,    1			)







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase											=  self.prepare_config_and_inputs()
          __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  config_and_inputs
          __UpperCamelCase											=  {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_natten
@require_torch
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Tuple      =   (
          (
              DinatModel,
              DinatForImageClassification,
              DinatBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__     :	Optional[int]      =   (
          {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__     :	Optional[Any]      =   False

      lowerCAmelCase__     :	Optional[int]      =   False
      lowerCAmelCase__     :	str      =   False
      lowerCAmelCase__     :	Union[str, Any]      =   False
      lowerCAmelCase__     :	int      =   False







      def  snake_case		(       self   :	List[str]			):
          __UpperCamelCase											=  DinatModelTester(self			)
          __UpperCamelCase											=  ConfigTester(self	,    config_class=snake_case	,    embed_dim=37			)







      def  snake_case		(       self   :	List[str]			):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







      def  snake_case		(       self   :	List[str]			):
          return







      def  snake_case		(       self   :	List[str]			):
          __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case			)







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case			)







      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case			)







      @unittest.skip(reason='''Dinat does not use inputs_embeds'''			)
      def  snake_case		(       self   :	Tuple			):
          pass







      @unittest.skip(reason='''Dinat does not use feedforward chunking'''			)
      def  snake_case		(       self   :	Union[str, Any]			):
          pass







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(snake_case			)
              self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)			)
              __UpperCamelCase											=  model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case	,    nn.Linear			)			)







      def  snake_case		(       self   :	Dict			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(snake_case			)
              __UpperCamelCase											=  inspect.signature(model.forward			)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __UpperCamelCase											=  [*signature.parameters.keys()]

              __UpperCamelCase											=  ['''pixel_values''']
              self.assertListEqual(arg_names[:1]	,    snake_case			)







      def  snake_case		(       self   :	List[str]			):
          self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''			)







      def  snake_case		(       self   :	str	,    snake_case   :	Optional[int]	,    snake_case   :	List[Any]	,    snake_case   :	List[Any]	,    snake_case   :	int			):
          __UpperCamelCase											=  model_class(snake_case			)
          model.to(snake_case			)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase											=  model(**self._prepare_for_class(snake_case	,    snake_case			)			)

          __UpperCamelCase											=  outputs.hidden_states

          __UpperCamelCase											=  getattr(
              self.model_tester	,    '''expected_num_hidden_layers'''	,    len(self.model_tester.depths			) + 1			)
          self.assertEqual(len(snake_case			)	,    snake_case			)

          # Dinat has a different seq_length
          __UpperCamelCase											=  (
              config.patch_size
              if isinstance(config.patch_size	,    collections.abc.Iterable			)
              else (config.patch_size, config.patch_size)
          )

          __UpperCamelCase											=  image_size[0] // patch_size[0]
          __UpperCamelCase											=  image_size[1] // patch_size[1]

          self.assertListEqual(
              list(hidden_states[0].shape[-3:]			)	,    [height, width, self.model_tester.embed_dim]	,    )

          if model_class.__name__ != "DinatBackbone":
              __UpperCamelCase											=  outputs.reshaped_hidden_states
              self.assertEqual(len(snake_case			)	,    snake_case			)

              __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  reshaped_hidden_states[0].shape
              __UpperCamelCase											=  (
                  reshaped_hidden_states[0].view(snake_case	,    snake_case	,    snake_case	,    snake_case			).permute(0	,    2	,    3	,    1			)
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-3:]			)	,    [height, width, self.model_tester.embed_dim]	,    )







      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase											=  (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size	,    collections.abc.Iterable			)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
              __UpperCamelCase											=  True
              self.check_hidden_states_output(snake_case	,    snake_case	,    snake_case	,    snake_case			)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __UpperCamelCase											=  True

              self.check_hidden_states_output(snake_case	,    snake_case	,    snake_case	,    snake_case			)







      @slow
      def  snake_case		(       self   :	Dict			):
          for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase											=  DinatModel.from_pretrained(snake_case			)
              self.assertIsNotNone(snake_case			)







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCamelCase											=  _config_zero_init(snake_case			)
          for model_class in self.all_model_classes:
              __UpperCamelCase											=  model_class(config=snake_case			)
              for name, param in model.named_parameters():
                  if "embeddings" not in name and param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()	,    [0.0, 1.0]	,    msg=F"Parameter {name} of model {model_class} seems not properly initialized"	,    )







@require_natten
@require_vision
@require_torch
class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      @cached_property
      def  snake_case		(       self   :	Union[str, Any]			):
          return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''			) if is_vision_available() else None







      @slow
      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''			).to(snake_case			)
          __UpperCamelCase											=  self.default_image_processor

          __UpperCamelCase											=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
          __UpperCamelCase											=  image_processor(images=snake_case	,    return_tensors='''pt'''			).to(snake_case			)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)

          # verify the logits
          __UpperCamelCase											=  torch.Size((1, 1000)			)
          self.assertEqual(outputs.logits.shape	,    snake_case			)
          __UpperCamelCase											=  torch.tensor([-0.15_45, -0.76_67, 0.46_42]			).to(snake_case			)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]	,    snake_case	,    atol=1E-4			)			)







@require_torch
@require_natten
class 	_lowerCamelCase  (     unittest.TestCase							,     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	List[str]      =   (DinatBackbone,) if is_torch_available() else ()
      lowerCAmelCase__     :	Optional[int]      =   DinatConfig







      def  snake_case		(       self   :	List[str]			):
          __UpperCamelCase											=  DinatModelTester(self			)












import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a_										=       16
a_										=       32




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = 16 )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  AutoTokenizer.from_pretrained('''bert-base-cased''' )
    __UpperCamelCase											=  load_dataset('''glue'''   ,       '''mrpc''' )

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        __UpperCamelCase											=  datasets.map(
            lowercase_   ,       batched=lowercase_   ,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']   ,       )

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

    def collate_fn(lowercase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase											=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __UpperCamelCase											=  16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase											=  8
        else:
            __UpperCamelCase											=  None

        return tokenizer.pad(
            lowercase_   ,       padding='''longest'''   ,       max_length=lowercase_   ,       pad_to_multiple_of=lowercase_   ,       return_tensors='''pt'''   ,       )

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

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 a_										=       mocked_dataloaders  # noqa: F811




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,       lowercase_ ) == "1":
        __UpperCamelCase											=  2
    # Initialize accelerator
    __UpperCamelCase											=  Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase											=  config['''lr''']
    __UpperCamelCase											=  int(config['''num_epochs'''] )
    __UpperCamelCase											=  int(config['''seed'''] )
    __UpperCamelCase											=  int(config['''batch_size'''] )

    # New Code #
    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps   ,       '''isdigit''' ):
        if args.checkpointing_steps == "epoch":
            __UpperCamelCase											=  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCamelCase											=  int(args.checkpointing_steps )
        else:
            raise ValueError(
                F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
    else:
        __UpperCamelCase											=  None

    set_seed(lowercase_ )

    __UpperCamelCase						, __UpperCamelCase											=  get_dataloaders(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  evaluate.load('''glue'''   ,       '''mrpc''' )

    # If the batch size is too big we use gradient accumulation
    __UpperCamelCase											=  1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __UpperCamelCase											=  batch_size // MAX_GPU_BATCH_SIZE
        __UpperCamelCase											=  MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase											=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,       return_dict=lowercase_ )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __UpperCamelCase											=  model.to(accelerator.device )

    # Instantiate optimizer
    __UpperCamelCase											=  AdamW(params=model.parameters()   ,       lr=lowercase_ )

    # Instantiate scheduler
    __UpperCamelCase											=  get_linear_schedule_with_warmup(
        optimizer=lowercase_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps   ,       )

    # 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						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  accelerator.prepare(
        lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase											=  0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase											=  0

    # We need to load the checkpoint back in before training here with `load_state`
    # The total number of epochs is adjusted based on where the state is being loaded from,
    # as we assume continuation of the same training script
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}" )
            accelerator.load_state(args.resume_from_checkpoint )
            __UpperCamelCase											=  os.path.basename(args.resume_from_checkpoint )
        else:
            # Get the most recent checkpoint
            __UpperCamelCase											=  [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
            dirs.sort(key=os.path.getctime )
            __UpperCamelCase											=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCamelCase											=  os.path.splitext(lowercase_ )[0]

        if "epoch" in training_difference:
            __UpperCamelCase											=  int(training_difference.replace('''epoch_'''   ,       '''''' ) ) + 1
            __UpperCamelCase											=  None
        else:
            __UpperCamelCase											=  int(training_difference.replace('''step_'''   ,       '''''' ) )
            __UpperCamelCase											=  resume_step // len(lowercase_ )
            resume_step -= starting_epoch * len(lowercase_ )

    # Now we train the model
    for epoch in range(lowercase_   ,       lowercase_ ):
        model.train()
        # New Code #
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __UpperCamelCase											=  accelerator.skip_first_batches(lowercase_   ,       lowercase_ )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCamelCase											=  train_dataloader
        for step, batch in enumerate(lowercase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            __UpperCamelCase											=  model(**lowercase_ )
            __UpperCamelCase											=  outputs.loss
            __UpperCamelCase											=  loss / gradient_accumulation_steps
            accelerator.backward(lowercase_ )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
            # New Code #
            overall_step += 1

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `step_{overall_step}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if isinstance(lowercase_   ,       lowercase_ ):
                __UpperCamelCase											=  F"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
                    accelerator.save_state(lowercase_ )

        model.eval()
        for step, batch in enumerate(lowercase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device )
            with torch.no_grad():
                __UpperCamelCase											=  model(**lowercase_ )
            __UpperCamelCase											=  outputs.logits.argmax(dim=-1 )
            __UpperCamelCase						, __UpperCamelCase											=  accelerator.gather_for_metrics((predictions, batch['''labels''']) )
            metric.add_batch(
                predictions=lowercase_   ,       references=lowercase_   ,       )

        __UpperCamelCase											=  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"   ,       lowercase_ )

        # New Code #
        # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
        # These are saved to folders named `epoch_{epoch}`
        # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
        # If mixed precision was used, will also save a "scalar.bin" file
        if checkpointing_steps == "epoch":
            __UpperCamelCase											=  F"epoch_{epoch}"
            if args.output_dir is not None:
                __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
            accelerator.save_state(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  argparse.ArgumentParser(description='''Simple example of training script.''' )
    parser.add_argument(
        '''--mixed_precision'''   ,       type=lowercase_   ,       default=lowercase_   ,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']   ,       help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''   ,       )
    parser.add_argument('''--cpu'''   ,       action='''store_true'''   ,       help='''If passed, will train on the CPU.''' )
    parser.add_argument(
        '''--checkpointing_steps'''   ,       type=lowercase_   ,       default=lowercase_   ,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,       )
    parser.add_argument(
        '''--output_dir'''   ,       type=lowercase_   ,       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=lowercase_   ,       default=lowercase_   ,       help='''If the training should continue from a checkpoint folder.'''   ,       )
    __UpperCamelCase											=  parser.parse_args()
    __UpperCamelCase											=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase_   ,       lowercase_ )


if __name__ == "__main__":
 main()






