


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor


class        _a							:

							def __init__(							self:	int    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	Optional[int]=13    ,						UpperCamelCase_:	Union[str, Any]=30    ,						UpperCamelCase_:	List[Any]=2    ,						UpperCamelCase_:	Tuple=3    ,						UpperCamelCase_:	Tuple=True    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Optional[Any]=32    ,						UpperCamelCase_:	Optional[Any]=2    ,						UpperCamelCase_:	List[Any]=4    ,						UpperCamelCase_:	Tuple=37    ,						UpperCamelCase_:	List[Any]="gelu"    ,						UpperCamelCase_:	List[str]=0.1    ,						UpperCamelCase_:	List[str]=0.1    ,						UpperCamelCase_:	int=10    ,						UpperCamelCase_:	Tuple=0.02    ,						UpperCamelCase_:	Optional[int]=3    ,						UpperCamelCase_:	Dict=0.6    ,						UpperCamelCase_:	int=None    ,						)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      image_size
									lowercase__            =      patch_size
									lowercase__            =      num_channels
									lowercase__            =      is_training
									lowercase__            =      use_labels
									lowercase__            =      hidden_size
									lowercase__            =      num_hidden_layers
									lowercase__            =      num_attention_heads
									lowercase__            =      intermediate_size
									lowercase__            =      hidden_act
									lowercase__            =      hidden_dropout_prob
									lowercase__            =      attention_probs_dropout_prob
									lowercase__            =      type_sequence_label_size
									lowercase__            =      initializer_range
									lowercase__            =      mask_ratio
									lowercase__            =      scope

									# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
									# (we add 1 for the [CLS] token)
									lowercase__            =      (image_size // patch_size) ** 2
									lowercase__            =      int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)

							def       lowerCamelCase_ (							self:	Tuple							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

									lowercase__            =      None
									if self.use_labels:
											lowercase__            =      ids_tensor([self.batch_size]    ,						self.type_sequence_label_size							)

									lowercase__            =      self.get_config()

									return config, pixel_values, labels

							def       lowerCamelCase_ (							self:	str							)			->   List[str]:





									"""simple docstring"""







									return ViTMAEConfig(
									    image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						decoder_hidden_size=self.hidden_size    ,						decoder_num_hidden_layers=self.num_hidden_layers    ,						decoder_num_attention_heads=self.num_attention_heads    ,						decoder_intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						is_decoder=UpperCamelCase_    ,						initializer_range=self.initializer_range    ,						mask_ratio=self.mask_ratio    ,						)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	Dict    ,						UpperCamelCase_:	Optional[Any]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      TFViTMAEModel(config=UpperCamelCase_							)
									lowercase__            =      model(UpperCamelCase_    ,						training=UpperCamelCase_							)
									self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int							)			->   Any:





									"""simple docstring"""







									lowercase__            =      TFViTMAEForPreTraining(UpperCamelCase_							)
									lowercase__            =      model(UpperCamelCase_    ,						training=UpperCamelCase_							)
									# expected sequence length = num_patches
									lowercase__            =      (self.image_size // self.patch_size) ** 2
									lowercase__            =      self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)							)

									# test greyscale images
									lowercase__            =      1
									lowercase__            =      TFViTMAEForPreTraining(UpperCamelCase_							)

									lowercase__            =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
									lowercase__            =      model(UpperCamelCase_    ,						training=UpperCamelCase_							)
									lowercase__            =      self.patch_size**2
									self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)							)


							def       lowerCamelCase_ (							self:	Tuple							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      self.prepare_config_and_inputs()
									((lowercase__)     ,		(lowercase__)     ,		(lowercase__))            =      config_and_inputs
									lowercase__            =      {'''pixel_values''': pixel_values}
									return config, inputs_dict


@require_tf
class        _a							(			UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			List[Any]        = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							_lowercase	:			Union[str, Any]        = {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

							_lowercase	:			str        = False
							_lowercase	:			int        = False
							_lowercase	:			Optional[int]        = False
							_lowercase	:			str        = False

							def       lowerCamelCase_ (							self:	List[str]							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      TFViTMAEModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						has_text_modality=UpperCamelCase_    ,						hidden_size=37							)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									self.config_tester.run_common_tests()

							@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''							)
							def       lowerCamelCase_ (							self:	int							)			->   Tuple:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	int							)			->   Tuple:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											self.assertIsInstance(model.get_input_embeddings()    ,						(tf.keras.layers.Layer)							)
											lowercase__            =      model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(UpperCamelCase_    ,						tf.keras.layers.Layer							)							)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      inspect.signature(model.call							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowercase__            =      [*signature.parameters.keys()]

											lowercase__            =      ['''pixel_values''']
											self.assertListEqual(arg_names[:1]    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[Any]							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__            =      int((config.image_size // config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      self._prepare_for_class(UpperCamelCase_    ,						UpperCamelCase_							)

											lowercase__            =      model(UpperCamelCase_    ,						noise=UpperCamelCase_							)

											lowercase__            =      copy.deepcopy(self._prepare_for_class(UpperCamelCase_    ,						UpperCamelCase_							)							)
											lowercase__            =      model(**UpperCamelCase_    ,						noise=UpperCamelCase_							)
											lowercase__            =      outputs_dict[0].numpy()
											lowercase__            =      outputs_keywords[0].numpy()

											self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)    ,						1E-6							)

							def       lowerCamelCase_ (							self:	Dict							)			->   Tuple:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__            =      int((config.image_size // config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

									def prepare_numpy_arrays(UpperCamelCase_:	Union[str, Any]							):
											lowercase__            =      {}
											for k, v in inputs_dict.items():
													if tf.is_tensor(UpperCamelCase_							):
															lowercase__            =      v.numpy()
													else:
															lowercase__            =      np.array(UpperCamelCase_							)

											return inputs_np_dict

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)

											lowercase__            =      self._prepare_for_class(UpperCamelCase_    ,						UpperCamelCase_							)
											lowercase__            =      prepare_numpy_arrays(UpperCamelCase_							)

											lowercase__            =      model(UpperCamelCase_    ,						noise=UpperCamelCase_							)
											lowercase__            =      model(**UpperCamelCase_    ,						noise=UpperCamelCase_							)
											self.assert_outputs_same(UpperCamelCase_    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	str    ,						UpperCamelCase_:	Tuple    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	List[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__            =      int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
									lowercase__            =      tf.constant(UpperCamelCase_							)

									# Add `noise` argument.
									# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
									lowercase__            =      tf_noise

									super().check_pt_tf_models(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	str							)			->   int:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__            =      {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__							),)
									    for module_member_name in dir(UpperCamelCase_							)
									    if module_member_name.endswith('''MainLayer'''							)
									    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
									    and module_member_name[: -len('''MainLayer'''							)] == model_class.__name__[: -len('''Model'''							)]
									    for module_member in (getattr(UpperCamelCase_    ,						UpperCamelCase_							),)
									    if isinstance(UpperCamelCase_    ,						UpperCamelCase_							)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(UpperCamelCase_    ,						'''_keras_serializable'''    ,						UpperCamelCase_							)
									}

									lowercase__            =      int((config.image_size // config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
									lowercase__            =      tf.convert_to_tensor(UpperCamelCase_							)
									inputs_dict.update({'''noise''': noise}							)

									for main_layer_class in tf_main_layer_classes:
											lowercase__            =      main_layer_class(UpperCamelCase_							)

											lowercase__            =      {
											    name: tf.keras.Input(tensor.shape[1:]    ,						dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
											}

											lowercase__            =      tf.keras.Model(UpperCamelCase_    ,						outputs=main_layer(UpperCamelCase_							)							)
											lowercase__            =      model(UpperCamelCase_							)

											with tempfile.TemporaryDirectory() as tmpdirname:
													lowercase__            =      os.path.join(UpperCamelCase_    ,						'''keras_model.h5'''							)
													model.save(UpperCamelCase_							)
													lowercase__            =      tf.keras.models.load_model(
													    UpperCamelCase_    ,						custom_objects={main_layer_class.__name__: main_layer_class}							)
													assert isinstance(UpperCamelCase_    ,						tf.keras.Model							)
													lowercase__            =      model(UpperCamelCase_							)
													self.assert_outputs_same(UpperCamelCase_    ,						UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Tuple:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__            =      int((config.image_size // config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      self._prepare_for_class(UpperCamelCase_    ,						UpperCamelCase_							)
											lowercase__            =      model(UpperCamelCase_    ,						noise=UpperCamelCase_							)

											if model_class.__name__ == "TFViTMAEModel":
													lowercase__            =      outputs.last_hidden_state.numpy()
													lowercase__            =      0
											else:
													lowercase__            =      outputs.logits.numpy()
													lowercase__            =      0

											with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(UpperCamelCase_    ,						saved_model=UpperCamelCase_							)
													lowercase__            =      model_class.from_pretrained(UpperCamelCase_							)
													lowercase__            =      model(UpperCamelCase_    ,						noise=UpperCamelCase_							)

													if model_class.__name__ == "TFViTMAEModel":
															lowercase__            =      after_outputs['''last_hidden_state'''].numpy()
															lowercase__            =      0
													else:
															lowercase__            =      after_outputs['''logits'''].numpy()
															lowercase__            =      0

													lowercase__            =      np.amax(np.abs(out_a - out_a							)							)
													self.assertLessEqual(UpperCamelCase_    ,						1E-5							)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   int:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									lowercase__            =      int((config.image_size // config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      self._prepare_for_class(UpperCamelCase_    ,						UpperCamelCase_							)

											lowercase__            =      model(UpperCamelCase_    ,						noise=UpperCamelCase_							)
											lowercase__            =      model.get_config()
											# make sure that returned config is jsonifiable, which is required by keras
											json.dumps(UpperCamelCase_							)
											lowercase__            =      model_class.from_config(model.get_config()							)
											# make sure it also accepts a normal config
											lowercase__            =      model_class.from_config(model.config							)
											lowercase__            =      new_model(UpperCamelCase_							)  # Build model
											new_model.set_weights(model.get_weights()							)
											lowercase__            =      new_model(UpperCamelCase_    ,						noise=UpperCamelCase_							)

											self.assert_outputs_same(UpperCamelCase_    ,						UpperCamelCase_							)

							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''							)
							def       lowerCamelCase_ (							self:	Tuple							)			->   str:





									"""simple docstring"""







									pass

							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''							)
							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   List[str]:





									"""simple docstring"""







									pass


							@slow
							def       lowerCamelCase_ (							self:	str							)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''							)
									self.assertIsNotNone(UpperCamelCase_							)
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image


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

							@cached_property
							def       lowerCamelCase_ (							self:	List[str]							)			->   List[str]:





									"""simple docstring"""







									return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''							) if is_vision_available() else None


							@slow
							def       lowerCamelCase_ (							self:	int							)			->   Optional[Any]:





									"""simple docstring"""







									np.random.seed(2							)

									lowercase__            =      TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''							)

									lowercase__            =      self.default_image_processor
									lowercase__            =      prepare_img()
									lowercase__            =      image_processor(images=UpperCamelCase_    ,						return_tensors='''tf'''							)

									# prepare a noise vector that will be also used for testing the TF model
									# (this way we can ensure that the PT and TF models operate on the same inputs)
									lowercase__            =      ViTMAEConfig()
									lowercase__            =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
									lowercase__            =      np.random.uniform(size=(1, num_patches)							)

									# forward pass
									lowercase__            =      model(**UpperCamelCase_    ,						noise=UpperCamelCase_							)

									# verify the logits
									lowercase__            =      tf.convert_to_tensor([1, 196, 768]							)
									self.assertEqual(outputs.logits.shape    ,						UpperCamelCase_							)

									lowercase__            =      tf.convert_to_tensor(
									    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

									tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,						UpperCamelCase_    ,						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 lowerCamelCase						(     __lowerCamelCase :    Tuple    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			fname.split(os.path.sep    )[-1]
    return re.search(R"""^(.*)_\d+\.jpg$"""					,       __lowerCamelCase    ).groups()[0]


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A=None ,		A=None       )		->   int:
        _SCREAMING_SNAKE_CASE									=			file_names
        _SCREAMING_SNAKE_CASE									=			image_transform
        _SCREAMING_SNAKE_CASE									=			label_to_id


    def __len__(		self       )		->   Optional[Any]:
        return len(self.file_names       )





    def __getitem__(		self ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.file_names[idx]
        _SCREAMING_SNAKE_CASE									=			PIL.Image.open(A       )
        _SCREAMING_SNAKE_CASE									=			raw_image.convert("""RGB"""       )
        if self.image_transform is not None:
            _SCREAMING_SNAKE_CASE									=			self.image_transform(A       )
        _SCREAMING_SNAKE_CASE									=			extract_label(A       )
        if self.label_to_id is not None:
            _SCREAMING_SNAKE_CASE									=			self.label_to_id[label]
        return {"image": image, "label": label}





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    Tuple    )						->str:
    # Initialize accelerator
    if args.with_tracking:
        _SCREAMING_SNAKE_CASE									=			Accelerator(
            cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       log_with="""all"""					,       project_dir=args.project_dir    )
    else:
        _SCREAMING_SNAKE_CASE									=			Accelerator(cpu=args.cpu					,       mixed_precision=args.mixed_precision    )

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _SCREAMING_SNAKE_CASE									=			config["""lr"""]
    _SCREAMING_SNAKE_CASE									=			int(config["""num_epochs"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""seed"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""batch_size"""]    )
    _SCREAMING_SNAKE_CASE									=			config["""image_size"""]
    if not isinstance(__lowerCamelCase					,       (list, tuple)    ):
        _SCREAMING_SNAKE_CASE									=			(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":
            _SCREAMING_SNAKE_CASE									=			args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _SCREAMING_SNAKE_CASE									=			int(args.checkpointing_steps    )
        else:
            raise ValueError(
                F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'    )
    else:
        _SCREAMING_SNAKE_CASE									=			None

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

    # Grab all the image filenames
    _SCREAMING_SNAKE_CASE									=			[os.path.join(args.data_dir					,       __lowerCamelCase    ) for fname in os.listdir(args.data_dir    ) if fname.endswith(""".jpg"""    )]

    # Build the label correspondences
    _SCREAMING_SNAKE_CASE									=			[extract_label(__lowerCamelCase    ) for fname in file_names]
    _SCREAMING_SNAKE_CASE									=			list(set(__lowerCamelCase    )    )
    id_to_label.sort()
    _SCREAMING_SNAKE_CASE									=			{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
    _SCREAMING_SNAKE_CASE									=			np.random.permutation(len(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			int(0.8 * len(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			random_perm[:cut]
    _SCREAMING_SNAKE_CASE									=			random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    _SCREAMING_SNAKE_CASE									=			Compose([RandomResizedCrop(__lowerCamelCase					,       scale=(0.5, 1.0)    ), ToTensor()]    )
    _SCREAMING_SNAKE_CASE									=			PetsDataset(
        [file_names[i] for i in train_split]					,       image_transform=__lowerCamelCase					,       label_to_id=__lowerCamelCase    )

    # For evaluation, we use a deterministic Resize
    _SCREAMING_SNAKE_CASE									=			Compose([Resize(__lowerCamelCase    ), ToTensor()]    )
    _SCREAMING_SNAKE_CASE									=			PetsDataset([file_names[i] for i in eval_split]					,       image_transform=__lowerCamelCase					,       label_to_id=__lowerCamelCase    )

    # Instantiate dataloaders.
    _SCREAMING_SNAKE_CASE									=			DataLoader(__lowerCamelCase					,       shuffle=__lowerCamelCase					,       batch_size=__lowerCamelCase					,       num_workers=4    )
    _SCREAMING_SNAKE_CASE									=			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)
    _SCREAMING_SNAKE_CASE									=			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).
    _SCREAMING_SNAKE_CASE									=			model.to(accelerator.device    )

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

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

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

    # Instantiate learning rate scheduler
    _SCREAMING_SNAKE_CASE									=			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.
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
    # We need to keep track of how many total steps we have iterated over
    _SCREAMING_SNAKE_CASE									=			0
    # We also need to keep track of the starting epoch so files are named properly
    _SCREAMING_SNAKE_CASE									=			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    )
            _SCREAMING_SNAKE_CASE									=			os.path.basename(args.resume_from_checkpoint    )
        else:
            # Get the most recent checkpoint
            _SCREAMING_SNAKE_CASE									=			[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
            dirs.sort(key=os.path.getctime    )
            _SCREAMING_SNAKE_CASE									=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _SCREAMING_SNAKE_CASE									=			os.path.splitext(__lowerCamelCase    )[0]

        if "epoch" in training_difference:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""epoch_"""					,       """"""    )    ) + 1
            _SCREAMING_SNAKE_CASE									=			None
        else:
            _SCREAMING_SNAKE_CASE									=			int(training_difference.replace("""step_"""					,       """"""    )    )
            _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			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
            _SCREAMING_SNAKE_CASE									=			train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _SCREAMING_SNAKE_CASE									=			{k: v.to(accelerator.device    ) for k, v in batch.items()}
            _SCREAMING_SNAKE_CASE									=			(batch["""image"""] - mean) / std
            _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			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    ):
                _SCREAMING_SNAKE_CASE									=			F'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
                    accelerator.save_state(__lowerCamelCase    )
        model.eval()
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			0
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            _SCREAMING_SNAKE_CASE									=			{k: v.to(accelerator.device    ) for k, v in batch.items()}
            _SCREAMING_SNAKE_CASE									=			(batch["""image"""] - mean) / std
            with torch.no_grad():
                _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			outputs.argmax(dim=-1    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			accelerator.gather_for_metrics((predictions, batch["""label"""])    )
            _SCREAMING_SNAKE_CASE									=			predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        _SCREAMING_SNAKE_CASE									=			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":
            _SCREAMING_SNAKE_CASE									=			F'epoch_{epoch}'
            if args.output_dir is not None:
                _SCREAMING_SNAKE_CASE									=			os.path.join(args.output_dir					,       __lowerCamelCase    )
            accelerator.save_state(__lowerCamelCase    )

    if args.with_tracking:
        accelerator.end_training()





def lowerCamelCase						(     )						->int:
    _SCREAMING_SNAKE_CASE									=			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"""					,       )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


if __name__ == "__main__":
      main()



