"""simple docstring"""



import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_MAPPING,
				    LevitForImageClassification,
				    LevitForImageClassificationWithTeacher,
				    LevitModel,
				)
				from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.config_class(**self.inputs_dict							)
										self.parent.assertTrue(hasattr(snake_case_   ,      """hidden_sizes"""							)							)
										self.parent.assertTrue(hasattr(snake_case_   ,      """num_attention_heads"""							)							)





class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=13   ,      snake_case_=64   ,      snake_case_=3   ,      snake_case_=3   ,      snake_case_=2   ,      snake_case_=1   ,      snake_case_=16   ,      snake_case_=[128, 256, 384]   ,      snake_case_=[4, 6, 8]   ,      snake_case_=[2, 3, 4]   ,      snake_case_=[16, 16, 16]   ,      snake_case_=0   ,      snake_case_=[2, 2, 2]   ,      snake_case_=[2, 2, 2]   ,      snake_case_=0.02   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=2   ,      ):







										'''simple docstring'''


										A__       :     Any								=			parent
										A__       :     int								=			batch_size
										A__       :     Optional[int]								=			image_size
										A__       :     Optional[Any]								=			num_channels
										A__       :     Dict								=			kernel_size
										A__       :     int								=			stride
										A__       :     int								=			padding
										A__       :     Tuple								=			hidden_sizes
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			depths
										A__       :     List[Any]								=			key_dim
										A__       :     Optional[Any]								=			drop_path_rate
										A__       :     int								=			patch_size
										A__       :     List[str]								=			attention_ratio
										A__       :     Tuple								=			mlp_ratio
										A__       :     Tuple								=			initializer_range
										A__       :     Union[str, Any]								=			[
										    ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
										    ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
										]
										A__       :     Optional[Any]								=			is_training
										A__       :     Optional[int]								=			use_labels
										A__       :     Optional[Any]								=			num_labels
										A__       :     str								=			initializer_range





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return LevitConfig(
										    image_size=self.image_size   ,      num_channels=self.num_channels   ,      kernel_size=self.kernel_size   ,      stride=self.stride   ,      padding=self.padding   ,      patch_size=self.patch_size   ,      hidden_sizes=self.hidden_sizes   ,      num_attention_heads=self.num_attention_heads   ,      depths=self.depths   ,      key_dim=self.key_dim   ,      drop_path_rate=self.drop_path_rate   ,      mlp_ratio=self.mlp_ratio   ,      attention_ratio=self.attention_ratio   ,      initializer_range=self.initializer_range   ,      down_ops=self.down_ops   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[Any]								=			LevitModel(config=snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     Dict								=			model(snake_case_							)
										A__       :     Tuple								=			(self.image_size, self.image_size)
										A__				,							A__       :     str								=			image_size[0], image_size[1]
										for _ in range(4							):
																	A__       :     int								=			floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
																	A__       :     Tuple								=			floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,      (self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			self.num_labels
										A__       :     List[Any]								=			LevitForImageClassification(snake_case_							)
										model.to(snake_case_							)
										model.eval()
										A__       :     Optional[Any]								=			model(snake_case_   ,      labels=snake_case_							)
										self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self.prepare_config_and_inputs()
										A__				,							A__				,							A__       :     List[Any]								=			config_and_inputs
										A__       :     List[str]								=			{"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_torch
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			_UpperCamelCase  :						List[Any]					     =				(
			    {
			        'feature-extraction': LevitModel,
			        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase  :						Optional[int]					     =				False
			_UpperCamelCase  :						str					     =				False
			_UpperCamelCase  :						Dict					     =				False
			_UpperCamelCase  :						Any					     =				False
			_UpperCamelCase  :						List[Any]					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			LevitModelTester(self							)
										A__       :     Tuple								=			ConfigTester(self   ,      config_class=snake_case_   ,      has_text_modality=snake_case_   ,      hidden_size=37							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return





			@unittest.skip(reason="""Levit does not use inputs_embeds"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			@unittest.skip(reason="""Levit does not support input and output embeddings"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			@unittest.skip(reason="""Levit does not output attentions"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Dict								=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	A__       :     Union[str, Any]								=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										def check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							):
																	A__       :     Dict								=			model_class(snake_case_							)
																	model.to(snake_case_							)
																	model.eval()

																	with torch.no_grad():
																								A__       :     List[str]								=			model(**self._prepare_for_class(snake_case_   ,      snake_case_							)							)

																	A__       :     Optional[int]								=			outputs.hidden_states

																	A__       :     Any								=			len(self.model_tester.depths							) + 1
																	self.assertEqual(len(snake_case_							)   ,      snake_case_							)

																	A__       :     str								=			(self.model_tester.image_size, self.model_tester.image_size)
																	A__				,							A__       :     Optional[int]								=			image_size[0], image_size[1]
																	for _ in range(4							):
																								A__       :     Optional[int]								=			floor(
																								    (
																								        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																								        / self.model_tester.stride
																								    )
																								    + 1							)
																								A__       :     Dict								=			floor(
																								    (
																								        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																								        / self.model_tester.stride
																								    )
																								    + 1							)
																	# verify the first hidden states (first block)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]							)   ,      [
																	        height * width,
																	        self.model_tester.hidden_sizes[0],
																	    ]   ,      )

										A__				,							A__       :     str								=			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	A__       :     Tuple								=			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"]
																	A__       :     Optional[Any]								=			True

																	check_hidden_states_output(snake_case_   ,      snake_case_   ,      snake_case_							)





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







										'''simple docstring'''


										pass





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										A__       :     List[Any]								=			super()._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)

										if return_labels:
																	if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																								del inputs_dict["labels"]

										return inputs_dict





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										if not self.model_tester.is_training:
																	return

										A__				,							A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs_for_common()
										A__       :     List[str]								=			True

										for model_class in self.all_model_classes:
																	# LevitForImageClassificationWithTeacher supports inference-only
																	if (
																	    model_class in get_values(snake_case_							)
																	    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																	):
																								continue
																	A__       :     Optional[Any]								=			model_class(snake_case_							)
																	model.to(snake_case_							)
																	model.train()
																	A__       :     Optional[Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     Union[str, Any]								=			model(**snake_case_							).loss
																	loss.backward()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     Tuple								=			self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
																	return

										A__       :     Tuple								=			False
										A__       :     int								=			True

										for model_class in self.all_model_classes:
																	if model_class in get_values(snake_case_							) or not model_class.supports_gradient_checkpointing:
																								continue
																	# LevitForImageClassificationWithTeacher supports inference-only
																	if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																								continue
																	A__       :     Optional[Any]								=			model_class(snake_case_							)
																	model.gradient_checkpointing_enable()
																	model.to(snake_case_							)
																	model.train()
																	A__       :     List[Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)
																	A__       :     List[str]								=			model(**snake_case_							).loss
																	loss.backward()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__				,							A__       :     List[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

										A__       :     List[Any]								=			[
										    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
										    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
										    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
										]

										for model_class in self.all_model_classes:
																	if (
																	    model_class
																	    not in [
																	        *get_values(snake_case_							),
																	    ]
																	    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																	):
																								continue

																	for problem_type in problem_types:
																								with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''							):
																															A__       :     List[str]								=			problem_type["""title"""]
																															A__       :     Optional[Any]								=			problem_type["""num_labels"""]

																															A__       :     Union[str, Any]								=			model_class(snake_case_							)
																															model.to(snake_case_							)
																															model.train()

																															A__       :     Union[str, Any]								=			self._prepare_for_class(snake_case_   ,      snake_case_   ,      return_labels=snake_case_							)

																															if problem_type["num_labels"] > 1:
																																						A__       :     Tuple								=			inputs["""labels"""].unsqueeze(1							).repeat(1   ,      problem_type["""num_labels"""]							)

																															A__       :     str								=			inputs["""labels"""].to(problem_type["""dtype"""]							)

																															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																															# they have the same size." which is a symptom something in wrong for the regression problem.
																															# See https://github.com/huggingface/transformers/issues/11780
																															with warnings.catch_warnings(record=snake_case_							) as warning_list:
																																						A__       :     Optional[int]								=			model(**snake_case_							).loss
																															for w in warning_list:
																																						if "Using a target size that is different to the input size" in str(w.message							):
																																													raise ValueError(
																																													    F'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

																															loss.backward()





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	A__       :     int								=			LevitModel.from_pretrained(snake_case_							)
																	self.assertIsNotNone(snake_case_							)





def  _A(		):
							A__       :     str								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
							return image



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







			'''simple docstring'''





			@cached_property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)





			@slow
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
										    snake_case_							)

										A__       :     Optional[Any]								=			self.default_image_processor
										A__       :     List[Any]								=			prepare_img()
										A__       :     List[Any]								=			image_processor(images=snake_case_   ,      return_tensors="""pt"""							).to(snake_case_							)

										# forward pass
										with torch.no_grad():
																	A__       :     Optional[int]								=			model(**snake_case_							)

										# verify the logits
										A__       :     int								=			torch.Size((1, 1_000)							)
										self.assertEqual(outputs.logits.shape   ,      snake_case_							)

										A__       :     int								=			torch.tensor([1.04_48, -0.37_45, -1.83_17]							).to(snake_case_							)

										self.assertTrue(torch.allclose(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()





