'''simple docstring'''

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor



class      UpperCAmelCase    :





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=32      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=10      ,  __lowerCAmelCase=[10, 20, 30, 40]      ,  __lowerCAmelCase=[1, 1, 2, 1]      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase="relu"      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=None      ,  )    ->    Dict:
        lowercase__							:       Tuple										=   parent
        lowercase__							:       int										=   batch_size
        lowercase__							:       List[Any]										=   image_size
        lowercase__							:       Any										=   num_channels
        lowercase__							:       Tuple										=   embeddings_size
        lowercase__							:       Union[str, Any]										=   hidden_sizes
        lowercase__							:       Tuple										=   depths
        lowercase__							:       Union[str, Any]										=   is_training
        lowercase__							:       Any										=   use_labels
        lowercase__							:       Optional[Any]										=   hidden_act
        lowercase__							:       str										=   num_labels
        lowercase__							:       Optional[Any]										=   scope
        lowercase__							:       List[Any]										=   len(snake_case_					)

       def _lowerCAmelCase(		self					)    ->    int:
        lowercase__							:       List[Any]										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

        lowercase__							:       str										=   None
        if self.use_labels:
         lowercase__							:       Tuple										=   ids_tensor([self.batch_size]      ,  self.num_labels					)

        lowercase__							:       Dict										=   self.get_config()
        return config, pixel_values, labels

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        return RegNetConfig(
            num_channels=self.num_channels      ,  embeddings_size=self.embeddings_size      ,  hidden_sizes=self.hidden_sizes      ,  depths=self.depths      ,  hidden_act=self.hidden_act      ,  num_labels=self.num_labels      ,  )

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    List[str]:
        lowercase__							:       Union[str, Any]										=   TFRegNetModel(config=snake_case_					)
        lowercase__							:       Optional[int]										=   model(snake_case_      ,  training=snake_case_					)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,  )

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase					)    ->    Any:
        lowercase__							:       Optional[int]										=   self.num_labels
        lowercase__							:       Tuple										=   TFRegNetForImageClassification(snake_case_					)
        lowercase__							:       Optional[int]										=   model(snake_case_      ,  labels=snake_case_      ,  training=snake_case_					)
        self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)					)


       def _lowerCAmelCase(		self					)    ->    Union[str, Any]:
        lowercase__							:       List[str]										=   self.prepare_config_and_inputs()
        lowercase__							:       Optional[Any]										=   config_and_inputs
        lowercase__							:       str										=   {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_tf
class      UpperCAmelCase    (		__A       ,		__A       ,		unittest.TestCase   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE   						=       (
           {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
           if is_tf_available()
           else {}
       )

       SCREAMING_SNAKE_CASE   						=       False
       SCREAMING_SNAKE_CASE   						=       False
       SCREAMING_SNAKE_CASE   						=       False
       SCREAMING_SNAKE_CASE   						=       False
       SCREAMING_SNAKE_CASE   						=       False

       def _lowerCAmelCase(		self					)    ->    Dict:
        lowercase__							:       Dict										=   TFRegNetModelTester(self					)
        lowercase__							:       str										=   ConfigTester(self      ,  config_class=snake_case_      ,  has_text_modality=snake_case_					)

       def _lowerCAmelCase(		self					)    ->    List[str]:
        return

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

       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''					)					) == 0      ,  reason='''TF does not support backprop for grouped convolutions on CPU.'''      ,  )
       @slow
       def _lowerCAmelCase(		self					)    ->    str:
        super().test_keras_fit()

       @unittest.skip(reason='''RegNet does not support input and output embeddings'''					)
       def _lowerCAmelCase(		self					)    ->    Tuple:
        pass

       def _lowerCAmelCase(		self					)    ->    str:
        lowercase__							:       int										=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__							:       int										=   model_class(snake_case_					)
         lowercase__							:       Optional[int]										=   inspect.signature(model.call					)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase__							:       Union[str, Any]										=   [*signature.parameters.keys()]

         lowercase__							:       Tuple										=   ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,  snake_case_					)

       def _lowerCAmelCase(		self					)    ->    Any:
        lowercase__							:       Union[str, Any]										=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_					)

       def _lowerCAmelCase(		self					)    ->    Optional[int]:


        def check_hidden_states_output(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase					):
         lowercase__							:       str										=   model_class(snake_case_					)
         lowercase__							:       Union[str, Any]										=   model(**self._prepare_for_class(snake_case_      ,  snake_case_					)      ,  training=snake_case_					)

         lowercase__							:       Optional[int]										=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         lowercase__							:       Union[str, Any]										=   self.model_tester.num_stages
         self.assertEqual(len(snake_case_					)      ,  expected_num_stages + 1					)

         # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]					)      ,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]      ,  )

        lowercase__							:       int										=   self.model_tester.prepare_config_and_inputs_for_common()
        lowercase__							:       Optional[int]										=   ["""basic""", """bottleneck"""]
        for model_class in self.all_model_classes:
         for layer_type in layers_type:
          lowercase__							:       Optional[Any]										=   layer_type
          lowercase__							:       Dict										=   True
          check_hidden_states_output(snake_case_      ,  snake_case_      ,  snake_case_					)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowercase__							:       List[Any]										=   True

          check_hidden_states_output(snake_case_      ,  snake_case_      ,  snake_case_					)

       def _lowerCAmelCase(		self					)    ->    Tuple:
        lowercase__							:       Optional[Any]										=   self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase={}					):
         lowercase__							:       List[str]										=   model(snake_case_      ,  return_dict=snake_case_      ,  **snake_case_					)
         lowercase__							:       Union[str, Any]										=   model(snake_case_      ,  return_dict=snake_case_      ,  **snake_case_					).to_tuple()

         def recursive_check(__lowerCAmelCase      ,  __lowerCAmelCase					):
          if isinstance(snake_case_      ,  (List, Tuple)					):
           for tuple_iterable_value, dict_iterable_value in zip(snake_case_      ,  snake_case_					):
            recursive_check(snake_case_      ,  snake_case_					)
          elif tuple_object is None:
           return
          else:
           self.assertTrue(
               all(tf.equal(snake_case_      ,  snake_case_					)					)      ,  msg=(
                   '''Tuple and dict output are not equal. Difference:'''
                   F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object					)					)}"""
               )      ,  )

          recursive_check(snake_case_      ,  snake_case_					)

        for model_class in self.all_model_classes:
         lowercase__							:       Any										=   model_class(snake_case_					)

         lowercase__							:       Dict										=   self._prepare_for_class(snake_case_      ,  snake_case_					)
         lowercase__							:       List[Any]										=   self._prepare_for_class(snake_case_      ,  snake_case_					)
         check_equivalence(snake_case_      ,  snake_case_      ,  snake_case_					)

         lowercase__							:       Tuple										=   self._prepare_for_class(snake_case_      ,  snake_case_      ,  return_labels=snake_case_					)
         lowercase__							:       List[str]										=   self._prepare_for_class(snake_case_      ,  snake_case_      ,  return_labels=snake_case_					)
         check_equivalence(snake_case_      ,  snake_case_      ,  snake_case_					)

         lowercase__							:       Optional[int]										=   self._prepare_for_class(snake_case_      ,  snake_case_					)
         lowercase__							:       List[str]										=   self._prepare_for_class(snake_case_      ,  snake_case_					)
         check_equivalence(snake_case_      ,  snake_case_      ,  snake_case_      ,  {'''output_hidden_states''': True}					)

         lowercase__							:       Dict										=   self._prepare_for_class(snake_case_      ,  snake_case_      ,  return_labels=snake_case_					)
         lowercase__							:       Union[str, Any]										=   self._prepare_for_class(snake_case_      ,  snake_case_      ,  return_labels=snake_case_					)
         check_equivalence(snake_case_      ,  snake_case_      ,  snake_case_      ,  {'''output_hidden_states''': True}					)

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        lowercase__							:       Tuple										=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case_					)


       @slow
       def _lowerCAmelCase(		self					)    ->    Any:


        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__							:       Any										=   TFRegNetModel.from_pretrained(snake_case_					)
         self.assertIsNotNone(snake_case_					)


def      __UpperCamelCase			(  ):
 lowercase__							:       Optional[int]										=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
 return image





@require_tf
@require_vision
class      UpperCAmelCase    (		unittest.TestCase   ):





       '''simple docstring'''



       @cached_property
       def _lowerCAmelCase(		self					)    ->    List[str]:
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
            if is_vision_available()
            else None
        )


       @slow
       def _lowerCAmelCase(		self					)    ->    Any:
        lowercase__							:       int										=   TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

        lowercase__							:       str										=   self.default_image_processor
        lowercase__							:       Any										=   prepare_img()
        lowercase__							:       Tuple										=   image_processor(images=snake_case_      ,  return_tensors='''tf'''					)

        # forward pass
        lowercase__							:       List[Any]										=   model(**snake_case_      ,  training=snake_case_					)

        # verify the logits
        lowercase__							:       Tuple										=   tf.TensorShape((1, 1000)					)
        self.assertEqual(outputs.logits.shape      ,  snake_case_					)

        lowercase__							:       Tuple										=   tf.constant([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]					)

        tf.debugging.assert_near(outputs.logits[0, :3]      ,  snake_case_      ,  atol=1E-4					)


"""simple docstring"""



import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def  _A(		lowerCAmelCase							):
							A__       :     int								=			fname.split(os.path.sep							)[-1]
							return re.search(r"""^(.*)_\d+\.jpg$"""			,  lowerCAmelCase							).groups()[0]
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=None   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     Dict								=			file_names
										A__       :     str								=			image_transform
										A__       :     Dict								=			label_to_id





			def __len__(			self							):







										'''simple docstring'''


										return len(self.file_names							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.file_names[idx]
										A__       :     Optional[Any]								=			PIL.Image.open(snake_case_							)
										A__       :     str								=			raw_image.convert("""RGB"""							)
										if self.image_transform is not None:
																	A__       :     Optional[int]								=			self.image_transform(snake_case_							)
										A__       :     Dict								=			extract_label(snake_case_							)
										if self.label_to_id is not None:
																	A__       :     List[Any]								=			self.label_to_id[label]
										return {"image": image, "label": label}





def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							# Initialize accelerator
							if args.with_tracking:
														A__       :     List[Any]								=			Accelerator(
														    cpu=args.cpu			,  mixed_precision=args.mixed_precision			,  log_with="""all"""			,  project_dir=args.project_dir							)
							else:
														A__       :     Optional[Any]								=			Accelerator(cpu=args.cpu			,  mixed_precision=args.mixed_precision							)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A__       :     List[Any]								=			config["""lr"""]
							A__       :     Any								=			int(config["""num_epochs"""]							)
							A__       :     List[Any]								=			int(config["""seed"""]							)
							A__       :     Tuple								=			int(config["""batch_size"""]							)
							A__       :     List[str]								=			config["""image_size"""]
							if not isinstance(lowerCAmelCase			,  (list, tuple)							):
														A__       :     List[str]								=			(image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps			,  """isdigit"""							):
														if args.checkpointing_steps == "epoch":
																					A__       :     List[Any]								=			args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A__       :     int								=			int(args.checkpointing_steps							)
														else:
																					raise ValueError(
																					    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
							else:
														A__       :     Any								=			None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														A__       :     int								=			os.path.split(lowerCAmelCase							)[-1].split("""."""							)[0]
														accelerator.init_trackers(lowerCAmelCase			,  lowerCAmelCase							)

							# Grab all the image filenames
							A__       :     Union[str, Any]								=			[os.path.join(args.data_dir			,  lowerCAmelCase							) for fname in os.listdir(args.data_dir							) if fname.endswith(""".jpg"""							)]

							# Build the label correspondences
							A__       :     int								=			[extract_label(lowerCAmelCase							) for fname in file_names]
							A__       :     Dict								=			list(set(lowerCAmelCase							)							)
							id_to_label.sort()
							A__       :     int								=			{lbl: i for i, lbl in enumerate(lowerCAmelCase							)}

							# Set the seed before splitting the data.
							np.random.seed(lowerCAmelCase							)
							torch.manual_seed(lowerCAmelCase							)
							torch.cuda.manual_seed_all(lowerCAmelCase							)

							# Split our filenames between train and validation
							A__       :     str								=			np.random.permutation(len(lowerCAmelCase							)							)
							A__       :     Optional[int]								=			int(0.8 * len(lowerCAmelCase							)							)
							A__       :     Union[str, Any]								=			random_perm[:cut]
							A__       :     Optional[int]								=			random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							A__       :     Union[str, Any]								=			Compose([RandomResizedCrop(lowerCAmelCase			,  scale=(0.5, 1.0)							), ToTensor()]							)
							A__       :     Dict								=			PetsDataset(
							    [file_names[i] for i in train_split]			,  image_transform=lowerCAmelCase			,  label_to_id=lowerCAmelCase							)

							# For evaluation, we use a deterministic Resize
							A__       :     Optional[int]								=			Compose([Resize(lowerCAmelCase							), ToTensor()]							)
							A__       :     Optional[Any]								=			PetsDataset([file_names[i] for i in eval_split]			,  image_transform=lowerCAmelCase			,  label_to_id=lowerCAmelCase							)

							# Instantiate dataloaders.
							A__       :     List[Any]								=			DataLoader(lowerCAmelCase			,  shuffle=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  num_workers=4							)
							A__       :     str								=			DataLoader(lowerCAmelCase			,  shuffle=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  num_workers=4							)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A__       :     Dict								=			create_model("""resnet50d"""			,  pretrained=lowerCAmelCase			,  num_classes=len(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).
							A__       :     Tuple								=			model.to(accelerator.device							)

							# Freezing the base model
							for param in model.parameters():
														A__       :     Any								=			False
							for param in model.get_classifier().parameters():
														A__       :     Dict								=			True

							# We normalize the batches of images to be a bit faster.
							A__       :     List[Any]								=			torch.tensor(model.default_cfg["""mean"""]							)[None, :, None, None].to(accelerator.device							)
							A__       :     int								=			torch.tensor(model.default_cfg["""std"""]							)[None, :, None, None].to(accelerator.device							)

							# Instantiate optimizer
							A__       :     str								=			torch.optim.Adam(params=model.parameters()			,  lr=lr / 25							)

							# Instantiate learning rate scheduler
							A__       :     Optional[int]								=			OneCycleLR(optimizer=lowerCAmelCase			,  max_lr=lowerCAmelCase			,  epochs=lowerCAmelCase			,  steps_per_epoch=len(lowerCAmelCase							)							)

							# 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__       :     Dict								=			accelerator.prepare(
							    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							# We need to keep track of how many total steps we have iterated over
							A__       :     str								=			0
							# We also need to keep track of the starting epoch so files are named properly
							A__       :     Union[str, Any]								=			0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
																					accelerator.load_state(args.resume_from_checkpoint							)
																					A__       :     Dict								=			os.path.basename(args.resume_from_checkpoint							)
														else:
																					# Get the most recent checkpoint
																					A__       :     Tuple								=			[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
																					dirs.sort(key=os.path.getctime							)
																					A__       :     Optional[Any]								=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A__       :     Optional[Any]								=			os.path.splitext(lowerCAmelCase							)[0]

														if "epoch" in training_difference:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""epoch_"""			,  """"""							)							) + 1
																					A__       :     int								=			None
														else:
																					A__       :     Optional[Any]								=			int(training_difference.replace("""step_"""			,  """"""							)							)
																					A__       :     Union[str, Any]								=			resume_step // len(lowerCAmelCase							)
																					resume_step -= starting_epoch * len(lowerCAmelCase							)

    # Now we train the model
							for epoch in range(lowerCAmelCase			,  lowerCAmelCase							):
														model.train()
														if args.with_tracking:
																					A__       :     List[Any]								=			0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A__       :     int								=			accelerator.skip_first_batches(lowerCAmelCase			,  lowerCAmelCase							)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A__       :     Optional[int]								=			train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     Union[str, Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					A__       :     Tuple								=			model(lowerCAmelCase							)
																					A__       :     int								=			torch.nn.functional.cross_entropy(lowerCAmelCase			,  batch["""label"""]							)
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(lowerCAmelCase							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(lowerCAmelCase			,  lowerCAmelCase							):
																												A__       :     Union[str, Any]								=			F'''step_{overall_step}'''
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A__       :     List[str]								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																																			accelerator.save_state(lowerCAmelCase							)
														model.eval()
														A__       :     Any								=			0
														A__       :     Tuple								=			0
														for step, batch in enumerate(lowerCAmelCase							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					A__       :     List[Any]								=			{k: v.to(accelerator.device							) for k, v in batch.items()}
																					A__       :     Any								=			(batch["""image"""] - mean) / std
																					with torch.no_grad():
																												A__       :     Optional[int]								=			model(lowerCAmelCase							)
																					A__       :     Tuple								=			outputs.argmax(dim=-1							)
																					A__				,							A__       :     Tuple								=			accelerator.gather_for_metrics((predictions, batch["""label"""])							)
																					A__       :     int								=			predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														A__       :     List[Any]								=			accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''							)
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        """accuracy""": 100 * eval_metric,
																					        """train_loss""": total_loss.item() / len(lowerCAmelCase							),
																					        """epoch""": epoch,
																					    }			,  step=lowerCAmelCase			,  )
														if checkpointing_steps == "epoch":
																					A__       :     Any								=			F'''epoch_{epoch}'''
																					if args.output_dir is not None:
																												A__       :     Any								=			os.path.join(args.output_dir			,  lowerCAmelCase							)
																					accelerator.save_state(lowerCAmelCase							)

							if args.with_tracking:
														accelerator.end_training()

def  _A(		):
							A__       :     Union[str, Any]								=			argparse.ArgumentParser(description="""Simple example of training script."""							)
							parser.add_argument("""--data_dir"""			,  required=lowerCAmelCase			,  help="""The data folder on disk."""							)
							parser.add_argument("""--fp16"""			,  action="""store_true"""			,  help="""If passed, will use FP16 training."""							)
							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."""							)
							parser.add_argument(
							    """--checkpointing_steps"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""			,  )
							parser.add_argument(
							    """--output_dir"""			,  type=lowerCAmelCase			,  default="""."""			,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,  )
							parser.add_argument(
							    """--resume_from_checkpoint"""			,  type=lowerCAmelCase			,  default=lowerCAmelCase			,  help="""If the training should continue from a checkpoint folder."""			,  )
							parser.add_argument(
							    """--with_tracking"""			,  action="""store_true"""			,  help="""Whether to load in all available experiment trackers from the environment and use them for logging."""			,  )
							parser.add_argument(
							    """--project_dir"""			,  type=lowerCAmelCase			,  default="""logs"""			,  help="""Location on where to store experiment tracking logs` and relevent project information"""			,  )
							A__       :     Optional[Any]								=			parser.parse_args()
							A__       :     Any								=			{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(lowerCAmelCase			,  lowerCAmelCase							)


if __name__ == "__main__":
				main()





