



'''simple docstring'''







import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      lowercase__  :	Dict            =      None

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=7  ,    __UpperCamelCase=3  ,    __UpperCamelCase=30  ,    __UpperCamelCase=4_00  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=1 / 2_55  ,    __UpperCamelCase=True  ,    __UpperCamelCase=FLAVA_IMAGE_MEAN  ,    __UpperCamelCase=FLAVA_IMAGE_STD  ,    __UpperCamelCase=14  ,    __UpperCamelCase=75  ,    __UpperCamelCase=None  ,    __UpperCamelCase=16  ,    __UpperCamelCase=0.3  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=FLAVA_CODEBOOK_MEAN  ,    __UpperCamelCase=FLAVA_CODEBOOK_STD  ,    ):
       UpperCamelCase__	     =      size if size is not None else {"height": 2_24, "width": 2_24}
       UpperCamelCase__	     =      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       UpperCamelCase__	     =      codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
       UpperCamelCase__	     =      codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      num_channels
       UpperCamelCase__	     =      do_resize
       UpperCamelCase__	     =      do_rescale
       UpperCamelCase__	     =      rescale_factor
       UpperCamelCase__	     =      min_resolution
       UpperCamelCase__	     =      max_resolution
       UpperCamelCase__	     =      size
       UpperCamelCase__	     =      resample if resample is not None else PILImageResampling.BICUBIC
       UpperCamelCase__	     =      do_normalize
       UpperCamelCase__	     =      image_mean
       UpperCamelCase__	     =      image_std
       UpperCamelCase__	     =      do_center_crop
       UpperCamelCase__	     =      crop_size

       UpperCamelCase__	     =      input_size_patches
       UpperCamelCase__	     =      total_mask_patches
       UpperCamelCase__	     =      mask_group_max_patches
       UpperCamelCase__	     =      mask_group_min_patches
       UpperCamelCase__	     =      mask_group_min_aspect_ratio
       UpperCamelCase__	     =      mask_group_max_aspect_ratio

       UpperCamelCase__	     =      codebook_do_resize
       UpperCamelCase__	     =      codebook_size
       UpperCamelCase__	     =      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
       UpperCamelCase__	     =      codebook_do_center_crop
       UpperCamelCase__	     =      codebook_crop_size
       UpperCamelCase__	     =      codebook_do_map_pixels
       UpperCamelCase__	     =      codebook_do_normalize
       UpperCamelCase__	     =      codebook_image_mean
       UpperCamelCase__	     =      codebook_image_std


   def 						_UpperCamelCase							(	self			):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "resample": self.resample,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "input_size_patches": self.input_size_patches,
           "total_mask_patches": self.total_mask_patches,
           "mask_group_max_patches": self.mask_group_max_patches,
           "mask_group_min_patches": self.mask_group_min_patches,
           "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
           "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
           "codebook_do_resize": self.codebook_do_resize,
           "codebook_size": self.codebook_size,
           "codebook_resample": self.codebook_resample,
           "codebook_do_center_crop": self.codebook_do_center_crop,
           "codebook_crop_size": self.codebook_crop_size,
           "codebook_do_map_pixels": self.codebook_do_map_pixels,
           "codebook_do_normalize": self.codebook_do_normalize,
           "codebook_image_mean": self.codebook_image_mean,
           "codebook_image_std": self.codebook_image_std,
       }


   def 						_UpperCamelCase							(	self			):
       return (self.size["height"], self.size["width"])


   def 						_UpperCamelCase							(	self			):
       return (
           (self.input_size_patches, self.input_size_patches)
           if not isinstance(self.input_size_patches  ,    __UpperCamelCase			)
           else self.input_size_patches
       )







   def 						_UpperCamelCase							(	self			):
       return (self.codebook_size["height"], self.codebook_size["width"])

@require_torch
@require_vision
class    lowerCamelCase_				(						lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  str					=						FlavaImageProcessor if is_vision_available() else None
   A			:  Optional[int]					=						None


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlavaImageProcessingTester(self			)


   @property
   def 						_UpperCamelCase							(	self			):
       return self.image_processor_tester.prepare_image_processor_dict()


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "image_mean"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "image_std"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "do_normalize"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "do_resize"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "resample"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "crop_size"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "do_center_crop"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "do_rescale"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "rescale_factor"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "masking_generator"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_do_resize"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_size"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_resample"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_do_center_crop"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_crop_size"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_do_map_pixels"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_do_normalize"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_image_mean"			)			)
       self.assertTrue(hasattr(__UpperCamelCase  ,    "codebook_image_std"			)			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size  ,    {"height": 2_24, "width": 2_24}			)
       self.assertEqual(image_processor.crop_size  ,    {"height": 2_24, "width": 2_24}			)
       self.assertEqual(image_processor.codebook_size  ,    {"height": 1_12, "width": 1_12}			)
       self.assertEqual(image_processor.codebook_crop_size  ,    {"height": 1_12, "width": 1_12}			)

       UpperCamelCase__	     =      self.image_processing_class.from_dict(
           self.image_processor_dict  ,    size=42  ,    crop_size=84  ,    codebook_size=33  ,    codebook_crop_size=66			)
       self.assertEqual(image_processor.size  ,    {"height": 42, "width": 42}			)
       self.assertEqual(image_processor.crop_size  ,    {"height": 84, "width": 84}			)
       self.assertEqual(image_processor.codebook_size  ,    {"height": 33, "width": 33}			)
       self.assertEqual(image_processor.codebook_crop_size  ,    {"height": 66, "width": 66}			)


   def 						_UpperCamelCase							(	self			):
       pass


   def 						_UpperCamelCase							(	self			):
       # Initialize image_processing
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__UpperCamelCase  ,    PIL.Image.Image			)

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

       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()

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

       # Test batched
       UpperCamelCase__	     =      image_processing(__UpperCamelCase  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()

       # Test no bool masked pos
       self.assertFalse("bool_masked_pos" in encoded_images			)

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


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       # Initialize image_processing
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random tensors
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase  ,    **__UpperCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__UpperCamelCase  ,    __UpperCamelCase			)

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

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,    )

       UpperCamelCase__	     =      image_processing(__UpperCamelCase  ,    return_image_mask=__UpperCamelCase  ,    return_tensors="pt"			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape  ,    (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )  ,    )

       # Test batched
       UpperCamelCase__	     =      image_processing(__UpperCamelCase  ,    return_tensors="pt"			).pixel_values

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

       # Test masking
       UpperCamelCase__	     =      image_processing(__UpperCamelCase  ,    return_image_mask=__UpperCamelCase  ,    return_tensors="pt"			)

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_image_size()
       self.assertEqual(
           encoded_images.pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )

       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_mask_size()
       self.assertEqual(
           encoded_images.bool_masked_pos.shape  ,    (
               self.image_processor_tester.batch_size,
               expected_height,
               expected_width,
           )  ,    )


   def 						_UpperCamelCase							(	self			):
       self._test_call_framework(np.ndarray  ,    prepare_kwargs={"numpify": True}			)


   def 						_UpperCamelCase							(	self			):
       self._test_call_framework(torch.Tensor  ,    prepare_kwargs={"torchify": True}			)


   def 						_UpperCamelCase							(	self			):
       # Initialize image_processing
       random.seed(12_34			)
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase  ,    torchify=__UpperCamelCase			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_image_mask=__UpperCamelCase  ,    return_tensors="pt"			)
       self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,    75			)







   def 						_UpperCamelCase							(	self			):
       # Initialize image_processing
       UpperCamelCase__	     =      self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       UpperCamelCase__	     =      prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__UpperCamelCase  ,    PIL.Image.Image			)

       # Test not batched input
       UpperCamelCase__	     =      image_processing(image_inputs[0]  ,    return_codebook_pixels=__UpperCamelCase  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape  ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,    )

       # Test batched
       UpperCamelCase__	     =      image_processing(__UpperCamelCase  ,    return_codebook_pixels=__UpperCamelCase  ,    return_tensors="pt"			)
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.image_processor_tester.get_expected_codebook_image_size()
       self.assertEqual(
           encoded_images.codebook_pixel_values.shape  ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,    )









'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

lowercase__  :	Optional[Any]            =      32





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_ = 16						):
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained("bert-base-cased"						)
    UpperCamelCase__	     =      load_dataset("glue"       ,	"mrpc"						)

    def tokenize_function(lowerCAmelCase_						):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase__	     =      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():
        UpperCamelCase__	     =      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
    UpperCamelCase__	     =      tokenized_datasets.rename_column("label"       ,	"labels"						)

    def collate_fn(lowerCAmelCase_						):
        # 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(
            lowerCAmelCase_       ,	padding="longest"       ,	pad_to_multiple_of=lowerCAmelCase_       ,	return_tensors="pt"       ,	)

    # Instantiate dataloaders.
    UpperCamelCase__	     =      DataLoader(
        tokenized_datasets["train"]       ,	shuffle=lowerCAmelCase_       ,	collate_fn=lowerCAmelCase_       ,	batch_size=lowerCAmelCase_						)
    UpperCamelCase__	     =      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

      lowercase__  :	Dict            =      mocked_dataloaders  # noqa: F811





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,	lowerCAmelCase_						) == "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"]						)

    UpperCamelCase__	     =      evaluate.load("glue"       ,	"mrpc"						)

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase_						)						)
    def inner_training_loop(lowerCAmelCase_						):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        UpperCamelCase__	     =      observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCamelCase__	     =      num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        set_seed(lowerCAmelCase_						)

        # 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=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).
        UpperCamelCase__	     =      model.to(accelerator.device						)

        # Instantiate optimizer
        UpperCamelCase__	     =      AdamW(params=model.parameters()       ,	lr=lowerCAmelCase_						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      get_dataloaders(lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Instantiate scheduler
        UpperCamelCase__	     =      get_linear_schedule_with_warmup(
            optimizer=lowerCAmelCase_       ,	num_warmup_steps=1_00       ,	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.
        UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      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_						):
                # And perform gradient accumulation
                with accelerator.accumulate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                    UpperCamelCase__	     =      outputs.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():
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                UpperCamelCase__	     =      outputs.logits.argmax(dim=-1						)
                UpperCamelCase__     ,	UpperCamelCase__	     =      accelerator.gather_for_metrics((predictions, batch["labels"])						)
                metric.add_batch(
                    predictions=lowerCAmelCase_       ,	references=lowerCAmelCase_       ,	)

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()





def     snake_case__			():
    UpperCamelCase__	     =      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."						)
    UpperCamelCase__	     =      parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    UpperCamelCase__	     =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
    training_function(lowerCAmelCase_       ,	lowerCAmelCase_						)


if __name__ == "__main__":
      main()




