import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor
class     a_			(						__lowercase						):






 '''simple docstring'''






 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(_a      ,   'width_multiplier'))






class     a_			:






 '''simple docstring'''






 def __init__(      self :  Dict      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Union[str, Any]=13      ,   lowercase__ :  Optional[int]=64      ,   lowercase__ :  Dict=2      ,   lowercase__ :  str=3      ,   lowercase__ :  Any="swish"      ,   lowercase__ :  Dict=3      ,   lowercase__ :  Optional[int]=32      ,   lowercase__ :  int=0.1      ,   lowercase__ :  List[str]=0.02      ,   lowercase__ :  Union[str, Any]=True      ,   lowercase__ :  str=True      ,   lowercase__ :  Dict=10      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Optional[Any]=0.25      ,   lowercase__ :  Optional[int]=0.0      ,   lowercase__ :  List[str]=0.0      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = parent
        lowerCAmelCase__					     = batch_size
        lowerCAmelCase__					     = image_size
        lowerCAmelCase__					     = patch_size
        lowerCAmelCase__					     = num_channels
        lowerCAmelCase__					     = make_divisible(512 * width_multiplier      ,   divisor=8)
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = conv_kernel_size
        lowerCAmelCase__					     = output_stride
        lowerCAmelCase__					     = classifier_dropout_prob
        lowerCAmelCase__					     = use_labels
        lowerCAmelCase__					     = is_training
        lowerCAmelCase__					     = num_labels
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = scope
        lowerCAmelCase__					     = width_multiplier
        lowerCAmelCase__					     = ffn_dropout
        lowerCAmelCase__					     = attn_dropout




 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

        lowerCAmelCase__					     = self.get_config()

        return config, pixel_values, labels, pixel_labels




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        return MobileViTVaConfig(
            image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   hidden_act=self.hidden_act      ,   conv_kernel_size=self.conv_kernel_size      ,   output_stride=self.output_stride      ,   classifier_dropout_prob=self.classifier_dropout_prob      ,   initializer_range=self.initializer_range      ,   width_multiplier=self.width_multiplier      ,   ffn_dropout=self.ffn_dropout_prob      ,   attn_dropout=self.attn_dropout_prob      ,   )




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = MobileViTVaModel(config=_a)
        model.to(_a)
        model.eval()
        lowerCAmelCase__					     = model(_a)
        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 :  int      ,   lowercase__ :  Optional[Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str      ,   lowercase__ :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_labels
        lowerCAmelCase__					     = MobileViTVaForImageClassification(_a)
        model.to(_a)
        model.eval()
        lowerCAmelCase__					     = model(_a      ,   labels=_a)
        self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels))




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple      ,   lowercase__ :  List[Any]      ,   lowercase__ :  int      ,   lowercase__ :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.num_labels
        lowerCAmelCase__					     = MobileViTVaForSemanticSegmentation(_a)
        model.to(_a)
        model.eval()
        lowerCAmelCase__					     = model(_a)
        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__					     = model(_a      ,   labels=_a)
        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 :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = self.prepare_config_and_inputs()
        lowerCAmelCase__					     = config_and_inputs
        lowerCAmelCase__					     = {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class     a_			(						__lowercase	,					__lowercase	,					unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_     			= (
     (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )

 UpperCAmelCase_     			= (
     {
         "feature-extraction": MobileViTVaModel,
         "image-classification": MobileViTVaForImageClassification,
         "image-segmentation": MobileViTVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = MobileViTVaModelTester(self)
        lowerCAmelCase__					     = MobileViTVaConfigTester(self      ,   config_class=_a      ,   has_text_modality=_a)




 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 @unittest.skip(reason='MobileViTV2 does not use inputs_embeds')
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        pass




 @unittest.skip(reason='MobileViTV2 does not support input and output embeddings')
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        pass




 @unittest.skip(reason='MobileViTV2 does not output attentions')
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        pass




 @require_torch_multi_gpu
 @unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.')
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        pass




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







        '''simple docstring'''



        pass




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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

               lowerCAmelCase__					     = ['''pixel_values''']
               self.assertListEqual(arg_names[:1]      ,   _a)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a)




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        def check_hidden_states_output(lowercase__ :  Tuple      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]):
               lowerCAmelCase__					     = model_class(_a)
               model.to(_a)
               model.eval()

               with torch.no_grad():
                      lowerCAmelCase__					     = model(**self._prepare_for_class(_a      ,   _a))

               lowerCAmelCase__					     = outputs.hidden_states

               lowerCAmelCase__					     = 5
               self.assertEqual(len(_a)      ,   _a)

               # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
               # with the width and height being successively divided by 2.
               lowerCAmelCase__					     = 2
               for i in range(len(_a)):
                      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__					     = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__					     = True
               check_hidden_states_output(_a      ,   _a      ,   _a)

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

               check_hidden_states_output(_a      ,   _a      ,   _a)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_a)




 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_a)




 @slow
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowerCAmelCase__					     = MobileViTVaModel.from_pretrained(_a)
               self.assertIsNotNone(_a)






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



@require_torch
@require_vision
class     a_			(						unittest.TestCase						):






 '''simple docstring'''






 @cached_property
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        return (
            MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256')
            if is_vision_available()
            else None
        )




 @slow
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256').to(
            _a)

        lowerCAmelCase__					     = self.default_image_processor
        lowerCAmelCase__					     = prepare_img()
        lowerCAmelCase__					     = image_processor(images=_a      ,   return_tensors='pt').to(_a)

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

        # verify the logits
        lowerCAmelCase__					     = torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape      ,   _a)

        lowerCAmelCase__					     = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(_a)

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




 @slow
 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
        lowerCAmelCase__					     = model.to(_a)

        lowerCAmelCase__					     = MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

        lowerCAmelCase__					     = prepare_img()
        lowerCAmelCase__					     = image_processor(images=_a      ,   return_tensors='pt').to(_a)

        # forward pass
        with torch.no_grad():
               lowerCAmelCase__					     = model(**_a)
        lowerCAmelCase__					     = outputs.logits

        # verify the logits
        lowerCAmelCase__					     = torch.Size((1, 21, 32, 32))
        self.assertEqual(logits.shape      ,   _a)

        lowerCAmelCase__					     = torch.tensor(
            [
                [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
                [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
                [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
            ]      ,   device=_a      ,   )

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




 @slow
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')
        lowerCAmelCase__					     = model.to(_a)

        lowerCAmelCase__					     = MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3')

        lowerCAmelCase__					     = prepare_img()
        lowerCAmelCase__					     = image_processor(images=_a      ,   return_tensors='pt').to(_a)

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

        lowerCAmelCase__					     = outputs.logits.detach().cpu()

        lowerCAmelCase__					     = image_processor.post_process_semantic_segmentation(outputs=_a      ,   target_sizes=[(50, 60)])
        lowerCAmelCase__					     = torch.Size((50, 60))
        self.assertEqual(segmentation[0].shape      ,   _a)

        lowerCAmelCase__					     = image_processor.post_process_semantic_segmentation(outputs=_a)
        lowerCAmelCase__					     = torch.Size((32, 32))
        self.assertEqual(segmentation[0].shape      ,   _a)

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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


lowerCAmelCase__     						=			16
lowerCAmelCase__     						=			32






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = 1_6     ):
       lowerCAmelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'     )
       lowerCAmelCase__					     = load_dataset('glue'     ,  'mrpc'     )

       def tokenize_function(lowerCAmelCase__     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCAmelCase__					     = 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():
              lowerCAmelCase__					     = 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
       lowerCAmelCase__					     = 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.
              lowerCAmelCase__					     = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCAmelCase__					     = 1_6
              elif accelerator.mixed_precision != "no":
                     lowerCAmelCase__					     = 8
              else:
                     lowerCAmelCase__					     = None

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

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

    lowerCAmelCase__     						=			mocked_dataloaders  # noqa: F811






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

       lowerCAmelCase__					     = evaluate.load('glue'     ,  'mrpc'     )

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

       set_seed(lowerCAmelCase__     )
       lowerCAmelCase__     , lowerCAmelCase__					     = get_dataloaders(lowerCAmelCase__     ,  lowerCAmelCase__     )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCAmelCase__					     = 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).
       lowerCAmelCase__					     = model.to(accelerator.device     )

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

       # Instantiate scheduler
       lowerCAmelCase__					     = get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase__     ,  num_warmup_steps=1_0_0     ,  num_training_steps=(len(lowerCAmelCase__     ) * 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.
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = 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     )
                     lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.loss
                     lowerCAmelCase__					     = loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              lowerCAmelCase__					     = 0
              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():
                            lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.logits.argmax(dim=-1     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = accelerator.gather((predictions, batch['labels'])     )
                     # New Code #
                     # First we check if it's a distributed system
                     if accelerator.use_distributed:
                            # Then see if we're on the last batch of our eval dataloader
                            if step == len(lowerCAmelCase__     ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCAmelCase__					     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowerCAmelCase__					     = references[: len(eval_dataloader.dataset     ) - samples_seen]
                            else:
                                   # Otherwise we add the number of samples seen
                                   samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                     metric.add_batch(
                         predictions=lowerCAmelCase__     ,  references=lowerCAmelCase__     ,  )

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






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 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.'     ,  )
       parser.add_argument('--cpu'     ,  action='store_true'     ,  help='If passed, will train on the CPU.'     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(lowerCAmelCase__     ,  lowerCAmelCase__     )


if __name__ == "__main__":
    main()
