


'''simple docstring'''




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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 torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class 		UpperCAmelCase_      :





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]=1_3    ,      UpperCAmelCase__       :      Optional[int]=3_0    ,      UpperCAmelCase__       :      Dict=2    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Any=True    ,      UpperCAmelCase__       :      List[str]=True    ,      UpperCAmelCase__       :      str=3_2    ,      UpperCAmelCase__       :      List[str]=5    ,      UpperCAmelCase__       :      Optional[int]=4    ,      UpperCAmelCase__       :      List[str]=3_7    ,      UpperCAmelCase__       :      Dict="gelu"    ,      UpperCAmelCase__       :      str=0.1    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Tuple=1_0    ,      UpperCAmelCase__       :      int=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Tuple=2    ,      )				->  Tuple:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			patch_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			scope
        lowerCAmelCase     =			encoder_stride

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCAmelCase     =			(image_size // patch_size) ** 2
        lowerCAmelCase     =			num_patches + 1





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Union[str, Any]:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowerCAmelCase     =			None
        if self.use_labels:
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.type_sequence_label_size    )

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[int]:
        return ViTConfig(
            image_size=self.image_size    ,      patch_size=self.patch_size    ,      num_channels=self.num_channels    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      is_decoder=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      encoder_stride=self.encoder_stride    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      int    )				->  Optional[Any]:
        lowerCAmelCase     =			ViTModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      List[Any]    )				->  Union[str, Any]:
        lowerCAmelCase     =			ViTForMaskedImageModeling(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(
            result.reconstruction.shape    ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			ViTForMaskedImageModeling(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.reconstruction.shape    ,      (self.batch_size, 1, self.image_size, self.image_size)    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      str    )				->  Tuple:
        lowerCAmelCase     =			self.type_sequence_label_size
        lowerCAmelCase     =			ViTForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			ViTForImageClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase				:	Union[str, Any]		=     (
         {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     lowerCamelCase				:	int		=     True

     lowerCamelCase				:	str		=     False
     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	Optional[int]		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			ViTModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        self.config_tester.run_common_tests()





     @unittest.skip(reason='ViT does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        pass





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           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]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Optional[int]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			ViTModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





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




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





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'    ) if is_vision_available() else None





     @slow
     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Union[str, Any]:
        lowerCAmelCase     =			ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    ).to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(**UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor([-0.2_744, 0.8_215, -0.0_836]    ).to(UpperCAmelCase__    )

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





     @slow
     def 							__UpperCAmelCase      ( self       :      int    )				->  List[Any]:
        # ViT models have an `interpolate_pos_encoding` argument in their forward method,
        # allowing to interpolate the pre-trained position embeddings in order to use
        # the model on higher resolutions. The DINO model by Facebook AI leverages this
        # to visualize self-attention on higher resolution images.
        lowerCAmelCase     =			ViTModel.from_pretrained('facebook/dino-vits8'    ).to(UpperCAmelCase__    )

        lowerCAmelCase     =			ViTImageProcessor.from_pretrained('facebook/dino-vits8'    ,      size=4_8_0    )
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    )
        lowerCAmelCase     =			inputs.pixel_values.to(UpperCAmelCase__    )

        # forward pass
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    ,      interpolate_pos_encoding=UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			torch.Size((1, 3_6_0_1, 3_8_4)    )
        self.assertEqual(outputs.last_hidden_state.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]    ).to(UpperCAmelCase__    )

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





     @slow
     @require_accelerate
     @require_torch_gpu
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[int]:
        lowerCAmelCase     =			ViTModel.from_pretrained('facebook/dino-vits8'    ,      torch_dtype=torch.floataa    ,      device_map='auto'    )
        lowerCAmelCase     =			self.default_image_processor

        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='pt'    )
        lowerCAmelCase     =			inputs.pixel_values.to(UpperCAmelCase__    )

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )












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
#
########################################################################


UpperCAmelCase	:	Any		     =       16
UpperCAmelCase	:	str		     =       32
def      _A       (		SCREAMING_SNAKE_CASE     :   Accelerator       ,       SCREAMING_SNAKE_CASE     :   int = 16						):





  """simple docstring"""



  a__	:					int										=AutoTokenizer.from_pretrained("bert-base-cased"						)
  a__	:					List[str]										=load_dataset("glue"       ,       "mrpc"						)

  def tokenize_function(SCREAMING_SNAKE_CASE     :   List[Any]						):
    # max_length=None => use the model max length (it's actually the default)
    a__	:					int										=tokenizer(examples["sentence1"]       ,       examples["sentence2"]       ,       truncation=SCREAMING_SNAKE_CASE       ,       max_length=SCREAMING_SNAKE_CASE						)
    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():
    a__	:					Dict										=datasets.map(
        SCREAMING_SNAKE_CASE       ,       batched=SCREAMING_SNAKE_CASE       ,       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
  a__	:					Dict										=tokenized_datasets.rename_column("label"       ,       "labels"						)

  def collate_fn(SCREAMING_SNAKE_CASE     :   str						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__	:					Optional[Any]										=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":
      a__	:					str										=16
    elif accelerator.mixed_precision != "no":
      a__	:					Union[str, Any]										=8
    else:
      a__	:					List[str]										=None

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

  # Instantiate dataloaders.
  a__	:					Any										=DataLoader(
      tokenized_datasets["train"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)
  a__	:					int										=DataLoader(
      tokenized_datasets["validation"]       ,       shuffle=SCREAMING_SNAKE_CASE       ,       collate_fn=SCREAMING_SNAKE_CASE       ,       batch_size=SCREAMING_SNAKE_CASE						)

  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

     UpperCAmelCase	:	str		     =       mocked_dataloaders  # noqa: F811
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,       SCREAMING_SNAKE_CASE						) == "1":
    a__	:					Tuple										=2
  # Initialize accelerator
  a__	:					int										=Accelerator(cpu=args.cpu       ,       mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a__	:					Optional[int]										=config["lr"]
  a__	:					Union[str, Any]										=int(config["num_epochs"]						)
  a__	:					Any										=int(config["seed"]						)
  a__	:					Dict										=int(config["batch_size"]						)

  a__	:					int										=evaluate.load("glue"       ,       "mrpc"						)

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

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

  # 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).
  a__	:					List[str]										=model.to(accelerator.device						)

  # Instantiate optimizer
  a__	:					List[Any]										=AdamW(params=model.parameters()       ,       lr=SCREAMING_SNAKE_CASE						)

  # Instantiate scheduler
  a__	:					Optional[int]										=get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE       ,       num_warmup_steps=100       ,       num_training_steps=(len(SCREAMING_SNAKE_CASE						) * 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.
  a__					,							a__					,							a__					,							a__					,							a__	:					Optional[int]										=accelerator.prepare(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Now we train the model
  for epoch in range(SCREAMING_SNAKE_CASE						):
    model.train()
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      a__	:					Dict										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					List[Any]										=outputs.loss
      a__	:					List[str]										=loss / gradient_accumulation_steps
      accelerator.backward(SCREAMING_SNAKE_CASE						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    a__	:					Optional[Any]										=0
    for step, batch in enumerate(SCREAMING_SNAKE_CASE						):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device						)
      with torch.no_grad():
        a__	:					Any										=model(**SCREAMING_SNAKE_CASE						)
      a__	:					str										=outputs.logits.argmax(dim=-1						)
      a__					,							a__	:					List[str]										=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(SCREAMING_SNAKE_CASE						) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          a__	:					Optional[Any]										=predictions[: len(eval_dataloader.dataset						) - samples_seen]
          a__	:					Dict										=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=SCREAMING_SNAKE_CASE       ,       references=SCREAMING_SNAKE_CASE       ,       )

    a__	:					Tuple										=metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		):





  """simple docstring"""



  a__	:					List[str]										=argparse.ArgumentParser(description="Simple example of training script."						)
  parser.add_argument(
      "--mixed_precision"       ,       type=SCREAMING_SNAKE_CASE       ,       default=SCREAMING_SNAKE_CASE       ,       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."						)
  a__	:					str										=parser.parse_args()
  a__	:					Optional[int]										={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     main()






