code: from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  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.models.swin.modeling_tf_swin import (
  	    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
  	    TFSwinForImageClassification,
  	    TFSwinForMaskedImageModeling,
  	    TFSwinModel,
  	)
  
  
  if is_vision_available():
  	from PIL import Image
  
  	from transformers import AutoImageProcessor
  
  
  
  
  class      lowercase_						:
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=13	,    __SCREAMING_SNAKE_CASE=32	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=3	,    __SCREAMING_SNAKE_CASE=16	,    __SCREAMING_SNAKE_CASE=[1, 2, 1]	,    __SCREAMING_SNAKE_CASE=[2, 2, 4]	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=2.0	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0.0	,    __SCREAMING_SNAKE_CASE=0.0	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE="gelu"	,    __SCREAMING_SNAKE_CASE=False	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=0.0_2	,    __SCREAMING_SNAKE_CASE=1e-5	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=10	,    __SCREAMING_SNAKE_CASE=8	,    )		->None:
  								lowerCAmelCase				=	parent
  								lowerCAmelCase				=	batch_size
  								lowerCAmelCase				=	image_size
  								lowerCAmelCase				=	patch_size
  								lowerCAmelCase				=	num_channels
  								lowerCAmelCase				=	embed_dim
  								lowerCAmelCase				=	depths
  								lowerCAmelCase				=	num_heads
  								lowerCAmelCase				=	window_size
  								lowerCAmelCase				=	mlp_ratio
  								lowerCAmelCase				=	qkv_bias
  								lowerCAmelCase				=	hidden_dropout_prob
  								lowerCAmelCase				=	attention_probs_dropout_prob
  								lowerCAmelCase				=	drop_path_rate
  								lowerCAmelCase				=	hidden_act
  								lowerCAmelCase				=	use_absolute_embeddings
  								lowerCAmelCase				=	patch_norm
  								lowerCAmelCase				=	layer_norm_eps
  								lowerCAmelCase				=	initializer_range
  								lowerCAmelCase				=	is_training
  								lowerCAmelCase				=	scope
  								lowerCAmelCase				=	use_labels
  								lowerCAmelCase				=	type_sequence_label_size
  								lowerCAmelCase				=	encoder_stride
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								lowerCAmelCase				=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
  
  								lowerCAmelCase				=	None
  								if self.use_labels:
  															lowerCAmelCase				=	ids_tensor([self.batch_size]	,    self.type_sequence_label_size       )
  
  								lowerCAmelCase				=	self.get_config()
  
  								return config, pixel_values, labels
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								return SwinConfig(
  								    image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    embed_dim=self.embed_dim	,    depths=self.depths	,    num_heads=self.num_heads	,    window_size=self.window_size	,    mlp_ratio=self.mlp_ratio	,    qkv_bias=self.qkv_bias	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    drop_path_rate=self.drop_path_rate	,    hidden_act=self.hidden_act	,    use_absolute_embeddings=self.use_absolute_embeddings	,    path_norm=self.patch_norm	,    layer_norm_eps=self.layer_norm_eps	,    initializer_range=self.initializer_range	,    encoder_stride=self.encoder_stride	,    )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->str:
  								lowerCAmelCase				=	TFSwinModel(config=lowercase_       )
  								lowerCAmelCase				=	model(lowercase_       )
  
  								lowerCAmelCase				=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
  								lowerCAmelCase				=	int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )
  
  								self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, expected_seq_len, expected_dim)       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								lowerCAmelCase				=	TFSwinForMaskedImageModeling(config=lowercase_       )
  								lowerCAmelCase				=	model(lowercase_       )
  								self.parent.assertEqual(
  								    result.logits.shape	,    (self.batch_size, self.num_channels, self.image_size, self.image_size)       )
  
  								# test greyscale images
  								lowerCAmelCase				=	1
  								lowerCAmelCase				=	TFSwinForMaskedImageModeling(lowercase_       )
  
  								lowerCAmelCase				=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
  								lowerCAmelCase				=	model(lowercase_       )
  								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, 1, self.image_size, self.image_size)       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.type_sequence_label_size
  								lowerCAmelCase				=	TFSwinForImageClassification(lowercase_       )
  								lowerCAmelCase				=	model(lowercase_	,    labels=lowercase_       )
  								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)       )
  
  								# test greyscale images
  								lowerCAmelCase				=	1
  								lowerCAmelCase				=	TFSwinForImageClassification(lowercase_       )
  								lowerCAmelCase				=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
  								lowerCAmelCase				=	model(lowercase_       )
  								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								lowerCAmelCase				=	self.prepare_config_and_inputs()
  								lowerCAmelCase				=	config_and_inputs
  								lowerCAmelCase				=	{'''pixel_values''': pixel_values}
  								return config, inputs_dict
  
  
  
  
  @require_tf
  class      lowercase_						(						__UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase		):
  	UpperCAmelCase_						:      Dict     =       (
  	    (
  	        TFSwinModel,
  	        TFSwinForImageClassification,
  	        TFSwinForMaskedImageModeling,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	UpperCAmelCase_						:      Tuple     =       (
  	    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
  	    if is_tf_available()
  	    else {}
  	)
  
  	UpperCAmelCase_						:      Optional[Any]     =       False
  	UpperCAmelCase_						:      List[str]     =       False
  	UpperCAmelCase_						:      Dict     =       False
  	UpperCAmelCase_						:      Union[str, Any]     =       False
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								lowerCAmelCase				=	TFSwinModelTester(self       )
  								lowerCAmelCase				=	ConfigTester(self	,    config_class=lowercase_	,    embed_dim=37       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								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     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
  								return
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*lowercase_       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_image_classification(*lowercase_       )
  
  
  	@unittest.skip(reason='''Swin does not use inputs_embeds'''       )
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
  								pass
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  															lowerCAmelCase				=	model_class(lowercase_       )
  															self.assertIsInstance(model.get_input_embeddings()	,    tf.keras.layers.Layer       )
  															lowerCAmelCase				=	model.get_output_embeddings()
  															self.assertTrue(x is None or isinstance(lowercase_	,    tf.keras.layers.Dense       )       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  															lowerCAmelCase				=	model_class(lowercase_       )
  															lowerCAmelCase				=	inspect.signature(model.call       )
  															# signature.parameters is an OrderedDict => so arg_names order is deterministic
  															lowerCAmelCase				=	[*signature.parameters.keys()]
  
  															lowerCAmelCase				=	['''pixel_values''']
  															self.assertListEqual(arg_names[:1]	,    lowercase_       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  								lowerCAmelCase				=	True
  
  								for model_class in self.all_model_classes:
  															lowerCAmelCase				=	True
  															lowerCAmelCase				=	False
  															lowerCAmelCase				=	True
  															lowerCAmelCase				=	model_class(lowercase_       )
  															lowerCAmelCase				=	model(**self._prepare_for_class(lowercase_	,    lowercase_       )       )
  															lowerCAmelCase				=	outputs.attentions
  															lowerCAmelCase				=	len(self.model_tester.depths       )
  															self.assertEqual(len(lowercase_       )	,    lowercase_       )
  
  															# check that output_attentions also work using config
  															del inputs_dict["output_attentions"]
  															lowerCAmelCase				=	True
  															lowerCAmelCase				=	config.window_size**2
  															lowerCAmelCase				=	model_class(lowercase_       )
  															lowerCAmelCase				=	model(**self._prepare_for_class(lowercase_	,    lowercase_       )       )
  															lowerCAmelCase				=	outputs.attentions
  															self.assertEqual(len(lowercase_       )	,    lowercase_       )
  
  															self.assertListEqual(
  															    list(attentions[0].shape[-3:]       )	,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,    )
  															lowerCAmelCase				=	len(lowercase_       )
  
  															# Check attention is always last and order is fine
  															lowerCAmelCase				=	True
  															lowerCAmelCase				=	True
  															lowerCAmelCase				=	model_class(lowercase_       )
  															lowerCAmelCase				=	model(**self._prepare_for_class(lowercase_	,    lowercase_       )       )
  
  															if hasattr(self.model_tester	,    '''num_hidden_states_types'''       ):
  																						lowerCAmelCase				=	self.model_tester.num_hidden_states_types
  															else:
  																						# also another +1 for reshaped_hidden_states
  																						lowerCAmelCase				=	2
  															self.assertEqual(out_len + added_hidden_states	,    len(lowercase_       )       )
  
  															lowerCAmelCase				=	outputs.attentions
  
  															self.assertEqual(len(lowercase_       )	,    lowercase_       )
  
  															self.assertListEqual(
  															    list(self_attentions[0].shape[-3:]       )	,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,    )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->str:
  								lowerCAmelCase				=	model_class(lowercase_       )
  								lowerCAmelCase				=	model(**self._prepare_for_class(lowercase_	,    lowercase_       )       )
  								lowerCAmelCase				=	outputs.hidden_states
  
  								lowerCAmelCase				=	getattr(
  								    self.model_tester	,    '''expected_num_hidden_layers'''	,    len(self.model_tester.depths       ) + 1       )
  								self.assertEqual(len(lowercase_       )	,    lowercase_       )
  
  								# Swin has a different seq_length
  								lowerCAmelCase				=	to_atuple(config.patch_size       )
  
  								lowerCAmelCase				=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
  								self.assertListEqual(
  								    list(hidden_states[0].shape[-2:]       )	,    [num_patches, self.model_tester.embed_dim]	,    )
  
  								lowerCAmelCase				=	outputs.reshaped_hidden_states
  								self.assertEqual(len(lowercase_       )	,    lowercase_       )
  
  								lowerCAmelCase				=	reshaped_hidden_states[0].shape
  
  								lowerCAmelCase				=	tf.reshape(reshaped_hidden_states[0]	,    (batch_size, num_channels, height * width)       )
  								lowerCAmelCase				=	tf.transpose(lowercase_	,    (0, 2, 1)       )
  
  								self.assertListEqual(
  								    list(reshaped_hidden_states.shape[-2:]       )	,    [num_patches, self.model_tester.embed_dim]	,    )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
  								lowerCAmelCase				=	to_atuple(self.model_tester.image_size       )
  
  								for model_class in self.all_model_classes:
  															lowerCAmelCase				=	True
  															self.check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_       )
  
  															# check that output_hidden_states also work using config
  															del inputs_dict["output_hidden_states"]
  															lowerCAmelCase				=	True
  
  															self.check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  								lowerCAmelCase				=	3
  
  								lowerCAmelCase				=	to_atuple(self.model_tester.image_size       )
  								lowerCAmelCase				=	to_atuple(config.patch_size       )
  
  								lowerCAmelCase				=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  								lowerCAmelCase				=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  								for model_class in self.all_model_classes:
  															lowerCAmelCase				=	True
  															self.check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_	,    (padded_height, padded_width)       )
  
  															# check that output_hidden_states also work using config
  															del inputs_dict["output_hidden_states"]
  															lowerCAmelCase				=	True
  															self.check_hidden_states_output(lowercase_	,    lowercase_	,    lowercase_	,    (padded_height, padded_width)       )
  
  
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
  
  								for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															lowerCAmelCase				=	TFSwinModel.from_pretrained(lowercase_       )
  															self.assertIsNotNone(lowercase_       )
  
  
  
  
  @require_vision
  @require_tf
  class      lowercase_						(						unittest.TestCase		):
  
  
  	@cached_property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
  								return (
  								    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''       )
  								    if is_vision_available()
  								    else None
  								)
  
  
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''       )
  								lowerCAmelCase				=	self.default_image_processor
  
  								lowerCAmelCase				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
  								lowerCAmelCase				=	image_processor(images=lowercase_	,    return_tensors='''tf'''       )
  
  								# forward pass
  								lowerCAmelCase				=	model(lowercase_       )
  
  								# verify the logits
  								lowerCAmelCase				=	tf.TensorShape((1, 1000)       )
  								self.assertEqual(outputs.logits.shape	,    lowercase_       )
  								lowerCAmelCase				=	tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]       )
  								self.assertTrue(np.allclose(outputs.logits[0, :3]	,    lowercase_	,    atol=1e-4       )       )
  
  
  
  
style_context: 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      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			Optional[Any]:
  							lowerCAmelCase				=	fname.split(os.path.sep							)[-1]
  							return re.search(R'''^(.*)_\d+\.jpg$'''     ,						snake_case__							).groups()[0]
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
  								lowerCAmelCase				=	file_names
  								lowerCAmelCase				=	image_transform
  								lowerCAmelCase				=	label_to_id
  
  
  	def __len__(							self       )		->Optional[int]:
  								return len(self.file_names       )
  
  
  
  
  	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
  								lowerCAmelCase				=	self.file_names[idx]
  								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
  								if self.image_transform is not None:
  															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
  								if self.label_to_id is not None:
  															lowerCAmelCase				=	self.label_to_id[label]
  								return {"image": image, "label": label}
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
  							# 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				=	config['''image_size''']
  							if not isinstance(snake_case__     ,						(list, tuple)							):
  														lowerCAmelCase				=	(image_size, image_size)
  
  							# Grab all the image filenames
  							lowerCAmelCase				=	[os.path.join(args.data_dir     ,						snake_case__							) for fname in os.listdir(args.data_dir							) if fname.endswith('''.jpg'''							)]
  
  							# Build the label correspondences
  							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
  							lowerCAmelCase				=	list(set(snake_case__							)							)
  							id_to_label.sort()
  							lowerCAmelCase				=	{lbl: i for i, lbl in enumerate(snake_case__							)}
  
  							# Set the seed before splitting the data.
  							np.random.seed(snake_case__							)
  							torch.manual_seed(snake_case__							)
  							torch.cuda.manual_seed_all(snake_case__							)
  
  							# Split our filenames between train and validation
  							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
  							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
  							lowerCAmelCase				=	random_perm[:cut]
  							lowerCAmelCase				=	random_perm[cut:]
  
  							# For training we use a simple RandomResizedCrop
  							lowerCAmelCase				=	Compose([RandomResizedCrop(snake_case__     ,						scale=(0.5, 1.0)							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset(
  							    [file_names[i] for i in train_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# For evaluation, we use a deterministic Resize
  							lowerCAmelCase				=	Compose([Resize(snake_case__							), ToTensor()]							)
  							lowerCAmelCase				=	PetsDataset([file_names[i] for i in eval_split]     ,						image_transform=snake_case__     ,						label_to_id=snake_case__							)
  
  							# Instantiate dataloaders.
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
  
  							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  							lowerCAmelCase				=	create_model('''resnet50d'''     ,						pretrained=snake_case__     ,						num_classes=len(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							)
  
  							# Freezing the base model
  							for param in model.parameters():
  														lowerCAmelCase				=	False
  							for param in model.get_classifier().parameters():
  														lowerCAmelCase				=	True
  
  							# We normalize the batches of images to be a bit faster.
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''mean''']							)[None, :, None, None].to(accelerator.device							)
  							lowerCAmelCase				=	torch.tensor(model.default_cfg['''std''']							)[None, :, None, None].to(accelerator.device							)
  
  							# Instantiate optimizer
  							lowerCAmelCase				=	torch.optim.Adam(params=model.parameters()     ,						lr=lr / 2_5							)
  
  							# Instantiate learning rate scheduler
  							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)
  
  							# 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__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
  																					accelerator.backward(snake_case__							)
  																					optimizer.step()
  																					lr_scheduler.step()
  																					optimizer.zero_grad()
  
  														model.eval()
  														lowerCAmelCase				=	0
  														lowerCAmelCase				=	0
  														for _, batch in enumerate(snake_case__							):
  																					# We could avoid this line since we set the accelerator with `device_placement=True`.
  																					lowerCAmelCase				=	{k: v.to(accelerator.device							) for k, v in batch.items()}
  																					lowerCAmelCase				=	(batch['''image'''] - mean) / std
  																					with torch.no_grad():
  																												lowerCAmelCase				=	model(snake_case__							)
  																					lowerCAmelCase				=	outputs.argmax(dim=-1							)
  																					lowerCAmelCase	,   lowerCAmelCase				=	accelerator.gather_for_metrics((predictions, batch['''label'''])							)
  																					lowerCAmelCase				=	predictions == references
  																					num_elems += accurate_preds.shape[0]
  																					accurate += accurate_preds.long().sum()
  
  														lowerCAmelCase				=	accurate.item() / num_elems
  														# Use accelerator.print to print only on the main process.
  														accelerator.print(f"epoch {epoch}: {1_0_0 * eval_metric:.2f}"							)
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
  							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
  							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
  							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(
  							    '''--checkpointing_steps'''     ,						type=snake_case__     ,						default=snake_case__     ,						help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,						)
  							parser.add_argument('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''							)
  							lowerCAmelCase				=	parser.parse_args()
  							lowerCAmelCase				=	{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
  							training_function(snake_case__     ,						snake_case__							)
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  