




'''simple docstring'''




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


a_   :	Any							   = 10_00


class 						__UpperCamelCase :






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase=1.2,	lowerCAmelCase="g",	lowerCAmelCase=0.5,	lowerCAmelCase="-",	lowerCAmelCase="test_out.jpg",	lowerCAmelCase=True,	lowerCAmelCase=False,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=0.7,	):



              """simple docstring"""







              if isinstance(lowerCAmelCase,	torch.Tensor						):
                     lowerCamelCase_							=img.numpy().astype('''np.uint8'''						)
              if isinstance(lowerCAmelCase,	lowerCAmelCase						):
                     lowerCamelCase_							=img_tensorize(lowerCAmelCase						)
              assert isinstance(lowerCAmelCase,	np.ndarray						)

              lowerCamelCase_,   lowerCamelCase_							=img.shape[1], img.shape[0]
              lowerCamelCase_							=mplfigure.Figure(frameon=lowerCAmelCase						)
              lowerCamelCase_							=fig.get_dpi()
              lowerCamelCase_							=(width * scale + 1e-2) / dpi
              lowerCamelCase_							=(height * scale + 1e-2) / dpi
              fig.set_size_inches(lowerCAmelCase,	lowerCAmelCase						)
              lowerCamelCase_							=fig.add_axes([0.0, 0.0, 1.0, 1.0]						)
              ax.axis('''off'''						)
              ax.set_xlim(0.0,	lowerCAmelCase						)
              ax.set_ylim(lowerCAmelCase						)

              lowerCamelCase_							=saveas
              lowerCamelCase_							=rgb
              lowerCamelCase_							=pynb
              lowerCamelCase_							=img
              lowerCamelCase_							=edgecolor
              lowerCamelCase_							=0.5
              lowerCamelCase_							=linestyle
              lowerCamelCase_							=int(np.sqrt(min(lowerCAmelCase,	lowerCAmelCase						)						) * scale // 3						)
              lowerCamelCase_							=width
              lowerCamelCase_							=height
              lowerCamelCase_							=scale
              lowerCamelCase_							=fig
              lowerCamelCase_							=ax
              lowerCamelCase_							=pad
              lowerCamelCase_							=idaobj
              lowerCamelCase_							=idaattr
              lowerCamelCase_							=FigureCanvasAgg(lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              if color is None:
                     lowerCamelCase_							=self.edgecolor
              ((lowerCamelCase_),   (lowerCamelCase_),   (lowerCamelCase_),   (lowerCamelCase_))							=box
              lowerCamelCase_							=xa - xa
              lowerCamelCase_							=ya - ya
              self.ax.add_patch(
                  mpl.patches.Rectangle(
                      (xa, ya),	lowerCAmelCase,	lowerCAmelCase,	fill=lowerCAmelCase,	edgecolor=lowerCAmelCase,	linewidth=self.font_size // 3,	alpha=self.alpha,	linestyle=self.linestyle,	)						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None						):



              """simple docstring"""







              if len(boxes.shape						) > 2:
                     lowerCamelCase_							=boxes[0]
              if len(obj_ids.shape						) > 1:
                     lowerCamelCase_							=obj_ids[0]
              if len(obj_scores.shape						) > 1:
                     lowerCamelCase_							=obj_scores[0]
              if len(attr_ids.shape						) > 1:
                     lowerCamelCase_							=attr_ids[0]
              if len(attr_scores.shape						) > 1:
                     lowerCamelCase_							=attr_scores[0]
              if isinstance(lowerCAmelCase,	torch.Tensor						):
                     lowerCamelCase_							=boxes.numpy()
              if isinstance(lowerCAmelCase,	lowerCAmelCase						):
                     lowerCamelCase_							=np.array(lowerCAmelCase						)
              assert isinstance(lowerCAmelCase,	np.ndarray						)
              lowerCamelCase_							=np.prod(boxes[:, 2:] - boxes[:, :2],	axis=1						)
              lowerCamelCase_							=np.argsort(-areas						).tolist()
              lowerCamelCase_							=boxes[sorted_idxs] if boxes is not None else None
              lowerCamelCase_							=obj_ids[sorted_idxs] if obj_ids is not None else None
              lowerCamelCase_							=obj_scores[sorted_idxs] if obj_scores is not None else None
              lowerCamelCase_							=attr_ids[sorted_idxs] if attr_ids is not None else None
              lowerCamelCase_							=attr_scores[sorted_idxs] if attr_scores is not None else None

              lowerCamelCase_							=[self._random_color(maximum=1						) for _ in range(len(lowerCAmelCase						)						)]
              lowerCamelCase_							=[assigned_colors[idx] for idx in sorted_idxs]
              if obj_ids is not None:
                     lowerCamelCase_							=self._create_text_labels_attr(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     for i in range(len(lowerCAmelCase						)						):
                            lowerCamelCase_							=assigned_colors[i]
                            self.add_box(boxes[i],	lowerCAmelCase						)
                            self.draw_labels(labels[i],	boxes[i],	lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=box
              lowerCamelCase_							=(xa, ya)
              lowerCamelCase_							=(ya - ya) * (xa - xa)
              lowerCamelCase_							=_SMALL_OBJ * self.scale
              if instance_area < small or ya - ya < 40 * self.scale:
                     if ya >= self.height - 5:
                            lowerCamelCase_							=(xa, ya)
                     else:
                            lowerCamelCase_							=(xa, ya)

              lowerCamelCase_							=(ya - ya) / np.sqrt(self.height * self.width						)
              lowerCamelCase_							=self._change_color_brightness(lowerCAmelCase,	brightness_factor=0.7						)
              lowerCamelCase_							=np.clip((height_ratio - 0.0_2) / 0.0_8 + 1,	1.2,	2						)
              font_size *= 0.7_5 * self.font_size

              self.draw_text(
                  text=lowerCAmelCase,	position=lowerCAmelCase,	color=lowerCAmelCase,	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase="g",	lowerCAmelCase="left",	):



              """simple docstring"""







              lowerCamelCase_							=0
              lowerCamelCase_							=self.font_size
              lowerCamelCase_							=np.maximum(list(mplc.to_rgb(lowerCAmelCase						)						),	0.2						)
              lowerCamelCase_							=max(0.8,	np.max(lowerCAmelCase						)						)
              lowerCamelCase_							={
                  '''facecolor''': '''black''',
                  '''alpha''': self.alpha,
                  '''pad''': self.pad,
                  '''edgecolor''': '''none''',
              }
              lowerCamelCase_,   lowerCamelCase_							=position
              self.ax.text(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	size=font_size * self.scale,	family='''sans-serif''',	bbox=lowerCAmelCase,	verticalalignment='''top''',	horizontalalignment=lowerCAmelCase,	color=lowerCAmelCase,	zorder=10,	rotation=lowerCAmelCase,	)






       def   lowercase__  (							self,	lowerCAmelCase=None						):



              """simple docstring"""







              if saveas is None:
                     lowerCamelCase_							=self.saveas
              if saveas.lower().endswith('''.jpg'''						) or saveas.lower().endswith('''.png'''						):
                     cva.imwrite(
                         lowerCAmelCase,	self._get_buffer()[:, :, ::-1],	)
              else:
                     self.fig.savefig(lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=[self.idaobj[i] for i in classes]
              lowerCamelCase_							=[self.idaattr[i] for i in attr_classes]
              lowerCamelCase_							=[
                  f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
                  for label, score, attr, attr_score in zip(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
              ]
              return labels






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=[self.idaobj[i] for i in classes]
              if scores is not None:
                     if labels is None:
                            lowerCamelCase_							=['''{:.0f}%'''.format(s * 100						) for s in scores]
                     else:
                            lowerCamelCase_							=['''{} {:.0f}%'''.format(lowerCAmelCase,	s * 100						) for li, s in zip(lowerCAmelCase,	lowerCAmelCase						)]
              return labels






       def   lowercase__  (							self,	lowerCAmelCase=255						):



              """simple docstring"""







              lowerCamelCase_							=np.random.randint(0,	len(_COLORS						)						)
              lowerCamelCase_							=_COLORS[idx] * maximum
              if not self.rgb:
                     lowerCamelCase_							=ret[::-1]
              return ret






       def   lowercase__  (							self						):



              """simple docstring"""







              if not self.pynb:
                     lowerCamelCase_,   lowerCamelCase_							=self.canvas.print_to_buffer()
                     if (width, height) != (self.width, self.height):
                            lowerCamelCase_							=cva.resize(self.img,	(width, height)						)
                     else:
                            lowerCamelCase_							=self.img
              else:
                     lowerCamelCase_							=io.BytesIO()  # works for cairo backend
                     self.canvas.print_rgba(lowerCAmelCase						)
                     lowerCamelCase_,   lowerCamelCase_							=self.width, self.height
                     lowerCamelCase_							=buf.getvalue()
                     lowerCamelCase_							=self.img

              lowerCamelCase_							=np.frombuffer(lowerCAmelCase,	dtype='''uint8'''						)
              lowerCamelCase_							=buffer.reshape(lowerCAmelCase,	lowerCAmelCase,	4						)
              lowerCamelCase_,   lowerCamelCase_							=np.split(lowerCAmelCase,	[3],	axis=2						)

              try:
                     import numexpr as ne  # fuse them with numexpr

                     lowerCamelCase_							=ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''						)
              except ImportError:
                     lowerCamelCase_							=alpha.astype('''float32'''						) / 2_5_5.0
                     lowerCamelCase_							=img * (1 - alpha) + rgb * alpha

              return visualized_image.astype('''uint8'''						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              assert brightness_factor >= -1.0 and brightness_factor <= 1.0
              lowerCamelCase_							=mplc.to_rgb(lowerCAmelCase						)
              lowerCamelCase_							=colorsys.rgb_to_hls(*mplc.to_rgb(lowerCAmelCase						)						)
              lowerCamelCase_							=polygon_color[1] + (brightness_factor * polygon_color[1])
              lowerCamelCase_							=0.0 if modified_lightness < 0.0 else modified_lightness
              lowerCamelCase_							=1.0 if modified_lightness > 1.0 else modified_lightness
              lowerCamelCase_							=colorsys.hls_to_rgb(polygon_color[0],	lowerCAmelCase,	polygon_color[2]						)
              return modified_color





a_   :	Dict							   = (
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)










'''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
#
########################################################################

a_   :	Any							   = 32





def   a_							(   __snake_case							:    Accelerator     , __snake_case							:    int = 16     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained('''bert-base-cased'''     )
       lowerCamelCase_							=load_dataset('''glue'''     , '''mrpc'''     )

       def tokenize_function(__snake_case							:    str     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCamelCase_							=tokenizer(examples['''sentence1''']     , examples['''sentence2''']     , truncation=__snake_case     , max_length=__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():
              lowerCamelCase_							=datasets.map(
                  __snake_case     , batched=__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
       lowerCamelCase_							=tokenized_datasets.rename_column('''label'''     , '''labels'''     )

       def collate_fn(__snake_case							:    Union[str, Any]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase_							=16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase_							=8
              else:
                     lowerCamelCase_							=None

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

       # Instantiate dataloaders.
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''train''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__snake_case     )
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''validation''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__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

     a_   :	List[Any]							   = mocked_dataloaders  # noqa: F811





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     , __snake_case     ) == "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'''     )

       # 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(__snake_case     )     )
       def inner_training_loop(__snake_case							:    Tuple     ):
              # 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
              lowerCamelCase_							=observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase_							=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(__snake_case     )

              # 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=__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).
              lowerCamelCase_							=model.to(accelerator.device     )

              # Instantiate optimizer
              lowerCamelCase_							=AdamW(params=model.parameters()     , lr=__snake_case     )
              lowerCamelCase_,   lowerCamelCase_							=get_dataloaders(__snake_case     , __snake_case     )

              # Instantiate scheduler
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  optimizer=__snake_case     , num_warmup_steps=100     , num_training_steps=(len(__snake_case     ) * 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.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=accelerator.prepare(
                  __snake_case     , __snake_case     , __snake_case     , __snake_case     , __snake_case     )

              # Now we train the model
              for epoch in range(__snake_case     ):
                     model.train()
                     for step, batch in enumerate(__snake_case     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(__snake_case     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   lowerCamelCase_							=model(**__snake_case     )
                                   lowerCamelCase_							=outputs.loss
                                   accelerator.backward(__snake_case     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

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

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

    # 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   a_							(   )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser(description='''Simple example of training script.'''     )
       parser.add_argument(
           '''--mixed_precision'''     , type=__snake_case     , default=__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.'''     )
       lowerCamelCase_							=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
       lowerCamelCase_							={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
       training_function(__snake_case     , __snake_case     )


if __name__ == "__main__":
     main()




