





"""simple docstring"""





import inspect
import unittest

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

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


if is_torch_available():
  import torch

  from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
  from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor






class    _lowerCAmelCase  (	_a			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.config_class(**self.inputs_dict )
         self.parent.assertTrue(hasattr(snake_case_       ,					'hidden_sizes' ) )
         self.parent.assertTrue(hasattr(snake_case_       ,					'neck_hidden_sizes' ) )
         self.parent.assertTrue(hasattr(snake_case_       ,					'num_attention_heads' ) )






class    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=6_4_0       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase="silu"       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=1_0       ,					__UpperCAmelCase=None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				parent
         lowerCAmelCase__     :Tuple											=				batch_size
         lowerCAmelCase__     :Union[str, Any]											=				image_size
         lowerCAmelCase__     :Tuple											=				patch_size
         lowerCAmelCase__     :Union[str, Any]											=				num_channels
         lowerCAmelCase__     :List[str]											=				last_hidden_size
         lowerCAmelCase__     :str											=				num_attention_heads
         lowerCAmelCase__     :Optional[Any]											=				hidden_act
         lowerCAmelCase__     :Dict											=				conv_kernel_size
         lowerCAmelCase__     :List[str]											=				output_stride
         lowerCAmelCase__     :Optional[Any]											=				hidden_dropout_prob
         lowerCAmelCase__     :int											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Tuple											=				classifier_dropout_prob
         lowerCAmelCase__     :str											=				use_labels
         lowerCAmelCase__     :Optional[Any]											=				is_training
         lowerCAmelCase__     :Any											=				num_labels
         lowerCAmelCase__     :Any											=				initializer_range
         lowerCAmelCase__     :Any											=				scope






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         lowerCAmelCase__     :List[Any]											=				None
         lowerCAmelCase__     :Optional[Any]											=				None
         if self.use_labels:
           lowerCAmelCase__     :Optional[int]											=				ids_tensor([self.batch_size]       ,					self.num_labels )
           lowerCAmelCase__     :Optional[Any]											=				ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels )

         lowerCAmelCase__     :Tuple											=				self.get_config()

         return config, pixel_values, labels, pixel_labels






       def 	snake_case		(       self ):



         '''simple docstring'''

         return MobileViTConfig(
             image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					num_attention_heads=self.num_attention_heads       ,					hidden_act=self.hidden_act       ,					conv_kernel_size=self.conv_kernel_size       ,					output_stride=self.output_stride       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					classifier_dropout_prob=self.classifier_dropout_prob       ,					initializer_range=self.initializer_range       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				MobileViTModel(config=snake_case_ )
         model.to(snake_case_ )
         model.eval()
         lowerCAmelCase__     :Optional[Any]											=				model(snake_case_ )
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,					(
                 self.batch_size,
                 self.last_hidden_size,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.num_labels
         lowerCAmelCase__     :str											=				MobileViTForImageClassification(snake_case_ )
         model.to(snake_case_ )
         model.eval()
         lowerCAmelCase__     :Union[str, Any]											=				model(snake_case_       ,					labels=snake_case_ )
         self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels) )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.num_labels
         lowerCAmelCase__     :Tuple											=				MobileViTForSemanticSegmentation(snake_case_ )
         model.to(snake_case_ )
         model.eval()
         lowerCAmelCase__     :List[str]											=				model(snake_case_ )
         self.parent.assertEqual(
             result.logits.shape       ,					(
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,					)
         lowerCAmelCase__     :int											=				model(snake_case_       ,					labels=snake_case_ )
         self.parent.assertEqual(
             result.logits.shape       ,					(
                 self.batch_size,
                 self.num_labels,
                 self.image_size // self.output_stride,
                 self.image_size // self.output_stride,
             )       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				self.prepare_config_and_inputs()
         lowerCAmelCase__     :int											=				config_and_inputs
         lowerCAmelCase__     :Any											=				{"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_torch
class    _lowerCAmelCase  (	_a    ,  _a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   (
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       __magic_name__	:Optional[Any]						    =   (
           {
               """feature-extraction""": MobileViTModel,
               """image-classification""": MobileViTForImageClassification,
               """image-segmentation""": MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __magic_name__	:int						    =   False
       __magic_name__	:Dict						    =   False
       __magic_name__	:int						    =   False
       __magic_name__	:Tuple						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				MobileViTModelTester(self )
         lowerCAmelCase__     :Any											=				MobileViTConfigTester(self       ,					config_class=snake_case_       ,					has_text_modality=snake_case_ )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






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



         '''simple docstring'''

         pass






       @unittest.skip(reason='MobileViT does not support input and output embeddings' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @unittest.skip(reason='MobileViT does not output attentions' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :int											=				model_class(snake_case_ )
           lowerCAmelCase__     :List[str]											=				inspect.signature(model.forward )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCAmelCase__     :List[Any]											=				[*signature.parameters.keys()]

           lowerCAmelCase__     :Optional[Any]											=				["""pixel_values"""]
           self.assertListEqual(arg_names[:1]       ,					snake_case_ )






       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_ )






       def 	snake_case		(       self ):



         '''simple docstring'''

         def check_hidden_states_output(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Tuple											=				model_class(snake_case_ )
           model.to(snake_case_ )
           model.eval()

           with torch.no_grad():
             lowerCAmelCase__     :Optional[Any]											=				model(**self._prepare_for_class(snake_case_       ,					snake_case_ ) )

           lowerCAmelCase__     :Optional[Any]											=				outputs.hidden_states

           lowerCAmelCase__     :List[Any]											=				5
           self.assertEqual(len(snake_case_ )       ,					snake_case_ )

           # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
           # with the width and height being successively divided by 2.
           lowerCAmelCase__     :Optional[int]											=				2
           for i in range(len(snake_case_ ) ):
             self.assertListEqual(
                 list(hidden_states[i].shape[-2:] )       ,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,					)
             divisor *= 2

           self.assertEqual(self.model_tester.output_stride       ,					divisor // 2 )

         lowerCAmelCase__     :List[str]											=				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowerCAmelCase__     :Optional[Any]											=				True
           check_hidden_states_output(snake_case_       ,					snake_case_       ,					snake_case_ )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowerCAmelCase__     :Tuple											=				True

           check_hidden_states_output(snake_case_       ,					snake_case_       ,					snake_case_ )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_ )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_ )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase__     :Tuple											=				MobileViTModel.from_pretrained(snake_case_ )
           self.assertIsNotNone(snake_case_ )






def 					__A  ()   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :int											=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image






@require_torch
@require_vision
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @cached_property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small' ) if is_vision_available() else None






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small' ).to(snake_case_ )

         lowerCAmelCase__     :Any											=				self.default_image_processor
         lowerCAmelCase__     :Union[str, Any]											=				prepare_img()
         lowerCAmelCase__     :List[str]											=				image_processor(images=snake_case_       ,					return_tensors='pt' ).to(snake_case_ )

         # forward pass
         with torch.no_grad():
           lowerCAmelCase__     :Dict											=				model(**snake_case_ )

         # verify the logits
         lowerCAmelCase__     :List[str]											=				torch.Size((1, 1_0_0_0) )
         self.assertEqual(outputs.logits.shape       ,					snake_case_ )

         lowerCAmelCase__     :Optional[int]											=				torch.tensor([-1.93_64, -1.23_27, -0.46_53] ).to(snake_case_ )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,					snake_case_       ,					atol=1E-4 ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
         lowerCAmelCase__     :Optional[Any]											=				model.to(snake_case_ )

         lowerCAmelCase__     :Any											=				MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

         lowerCAmelCase__     :Union[str, Any]											=				prepare_img()
         lowerCAmelCase__     :Optional[int]											=				image_processor(images=snake_case_       ,					return_tensors='pt' ).to(snake_case_ )

         # forward pass
         with torch.no_grad():
           lowerCAmelCase__     :Tuple											=				model(**snake_case_ )
         lowerCAmelCase__     :Optional[int]											=				outputs.logits

         # verify the logits
         lowerCAmelCase__     :int											=				torch.Size((1, 2_1, 3_2, 3_2) )
         self.assertEqual(logits.shape       ,					snake_case_ )

         lowerCAmelCase__     :Dict											=				torch.tensor(
             [
                 [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
                 [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
                 [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
             ]       ,					device=snake_case_       ,					)

         self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,					snake_case_       ,					atol=1E-4 ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )
         lowerCAmelCase__     :int											=				model.to(snake_case_ )

         lowerCAmelCase__     :List[Any]											=				MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small' )

         lowerCAmelCase__     :Optional[Any]											=				prepare_img()
         lowerCAmelCase__     :List[str]											=				image_processor(images=snake_case_       ,					return_tensors='pt' ).to(snake_case_ )

         # forward pass
         with torch.no_grad():
           lowerCAmelCase__     :Dict											=				model(**snake_case_ )

         lowerCAmelCase__     :List[str]											=				outputs.logits.detach().cpu()

         lowerCAmelCase__     :Optional[Any]											=				image_processor.post_process_semantic_segmentation(outputs=snake_case_       ,					target_sizes=[(5_0, 6_0)] )
         lowerCAmelCase__     :List[str]											=				torch.Size((5_0, 6_0) )
         self.assertEqual(segmentation[0].shape       ,					snake_case_ )

         lowerCAmelCase__     :List[Any]											=				image_processor.post_process_semantic_segmentation(outputs=snake_case_ )
         lowerCAmelCase__     :str											=				torch.Size((3_2, 3_2) )
         self.assertEqual(segmentation[0].shape       ,					snake_case_ )





'''simple docstring'''





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
# and perform gradient accumulation
#
# 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 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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Optional[Any]:
      snake_case__    :       Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Optional[int]       		= load_dataset("""glue"""	,  """mrpc"""						)

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

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            snake_case__    :       List[str]       		= datasets.map(
                _lowerCAmelCase	,  batched=_lowerCAmelCase	,  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
      snake_case__    :       Any       		= tokenized_datasets.rename_column("""label"""	,  """labels"""						)

      def collate_fn(_lowerCAmelCase						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            snake_case__    :       List[str]       		= 128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  snake_case__    :       Optional[Any]       		= 16
            elif accelerator.mixed_precision != "no":
                  snake_case__    :       Tuple       		= 8
            else:
                  snake_case__    :       int       		= None

            return tokenizer.pad(
                _lowerCAmelCase	,  padding="""longest"""	,  max_length=_lowerCAmelCase	,  pad_to_multiple_of=_lowerCAmelCase	,  return_tensors="""pt"""	,  )

      # Instantiate dataloaders.
      snake_case__    :       List[Any]       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Dict       		= DataLoader(
          tokenized_datasets["""validation"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)

      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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,  _lowerCAmelCase						) == "1":
            snake_case__    :       int       		= 2
      # New Code #
      snake_case__    :       Any       		= int(args.gradient_accumulation_steps						)
      # Initialize accelerator
      snake_case__    :       Any       		= Accelerator(
          cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  gradient_accumulation_steps=_lowerCAmelCase						)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      snake_case__    :       List[Any]       		= config["""lr"""]
      snake_case__    :       Optional[Any]       		= int(config["""num_epochs"""]						)
      snake_case__    :       Union[str, Any]       		= int(config["""seed"""]						)
      snake_case__    :       List[str]       		= int(config["""batch_size"""]						)

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

      set_seed(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Tuple       		= get_dataloaders(_lowerCAmelCase	,  _lowerCAmelCase						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__    :       Optional[int]       		= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,  return_dict=_lowerCAmelCase						)

      # 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).
      snake_case__    :       Tuple       		= model.to(accelerator.device						)

      # Instantiate optimizer
      snake_case__    :       Any       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)

      # Instantiate scheduler
      snake_case__    :       List[Any]       		= get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * num_epochs)	,  )

      # 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.
      snake_case__		,							snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       List[Any]       		= accelerator.prepare(
          _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

      # Now we train the model
      for epoch in range(_lowerCAmelCase						):
            model.train()
            for step, batch in enumerate(_lowerCAmelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(_lowerCAmelCase						):
                        snake_case__    :       Any       		= model(**_lowerCAmelCase						)
                        snake_case__    :       str       		= output.loss
                        accelerator.backward(_lowerCAmelCase						)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(_lowerCAmelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  with torch.no_grad():
                        snake_case__    :       str       		= model(**_lowerCAmelCase						)
                  snake_case__    :       Optional[int]       		= outputs.logits.argmax(dim=-1						)
                  snake_case__		,							snake_case__    :       List[Any]       		= accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                  metric.add_batch(
                      predictions=_lowerCAmelCase	,  references=_lowerCAmelCase	,  )

            snake_case__    :       str       		= metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"	,  _lowerCAmelCase						)





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= argparse.ArgumentParser(description="""Simple example of training script."""						)
      parser.add_argument(
          """--mixed_precision"""	,  type=_lowerCAmelCase	,  default=_lowerCAmelCase	,  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."""	,  )
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,  type=_lowerCAmelCase	,  default=1	,  help="""The number of minibatches to be ran before gradients are accumulated."""	,  )
      parser.add_argument("""--cpu"""	,  action="""store_true"""	,  help="""If passed, will train on the CPU."""						)
      snake_case__    :       Tuple       		= parser.parse_args()
      snake_case__    :       Dict       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()
