

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							(       __A						):
    SCREAMING_SNAKE_CASE__         =    fname.split(os.path.sep						)[-1]
    return re.search(r"^(.*)_\d+\.jpg$"			,							_snake_case						).groups()[0]







class  UpperCamelCase_      (	__UpperCAmelCase      ):


  """simple docstring"""



  def __init__(						self     :					Optional[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[int]=None  ,	A_     :					Optional[Any]=None						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    file_names
      SCREAMING_SNAKE_CASE__         =    image_transform
      SCREAMING_SNAKE_CASE__         =    label_to_id



  def __len__(						self     :					Tuple						)       ->			str:
      return len(self.file_names						)







  def __getitem__(						self     :					str  ,	A_     :					Optional[int]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.file_names[idx]
      SCREAMING_SNAKE_CASE__         =    PIL.Image.open(UpperCAmelCase_						)
      SCREAMING_SNAKE_CASE__         =    raw_image.convert("RGB"						)
      if self.image_transform is not None:
          SCREAMING_SNAKE_CASE__         =    self.image_transform(UpperCAmelCase_						)
      SCREAMING_SNAKE_CASE__         =    extract_label(UpperCAmelCase_						)
      if self.label_to_id is not None:
          SCREAMING_SNAKE_CASE__         =    self.label_to_id[label]
      return {"image": image, "label": label}





def 				__a							(       __A			,							__A						):
    # Initialize accelerator
    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(_snake_case			,							(list, tuple)						):
        SCREAMING_SNAKE_CASE__         =    (image_size, image_size)

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

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

    # For training we use a simple RandomResizedCrop
    SCREAMING_SNAKE_CASE__         =    Compose([RandomResizedCrop(_snake_case			,							scale=(0.5, 1.0)						), ToTensor()]						)
    SCREAMING_SNAKE_CASE__         =    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
    SCREAMING_SNAKE_CASE__         =    Compose([Resize(_snake_case						), ToTensor()]						)
    SCREAMING_SNAKE_CASE__         =    PetsDataset([file_names[i] for i in eval_split]			,							image_transform=_snake_case			,							label_to_id=_snake_case						)

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE__         =    DataLoader(_snake_case			,							shuffle=_snake_case			,							batch_size=_snake_case			,							num_workers=4						)
    SCREAMING_SNAKE_CASE__         =    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)
    SCREAMING_SNAKE_CASE__         =    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).
    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=_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.
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    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`.
            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(_snake_case						)
            SCREAMING_SNAKE_CASE__         =    torch.nn.functional.cross_entropy(_snake_case			,							batch["label"]						)
            accelerator.backward(_snake_case						)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        SCREAMING_SNAKE_CASE__         =    0
        SCREAMING_SNAKE_CASE__         =    0
        for _, batch in enumerate(_snake_case						):
            # 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(_snake_case						)
            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}"						)





def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    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."						)
    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(_snake_case			,							_snake_case						)


if __name__ == "__main__":
  main()








from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

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

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


if is_tf_available():
		import tensorflow as tf

		from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
		from PIL import Image

		from transformers import ConvNextImageProcessor


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=13							,			UpperCAmelCase_=32							,			UpperCAmelCase_=3							,			UpperCAmelCase_=4							,			UpperCAmelCase_=[10, 20, 30, 40]							,			UpperCAmelCase_=[2, 2, 3, 2]							,			UpperCAmelCase_=True							,			UpperCAmelCase_=True							,			UpperCAmelCase_=37							,			UpperCAmelCase_="gelu"							,			UpperCAmelCase_=10							,			UpperCAmelCase_=0.02							,			UpperCAmelCase_=3							,			UpperCAmelCase_=None							,			):
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		image_size
											lowerCAmelCase =		num_channels
											lowerCAmelCase =		num_stages
											lowerCAmelCase =		hidden_sizes
											lowerCAmelCase =		depths
											lowerCAmelCase =		is_training
											lowerCAmelCase =		use_labels
											lowerCAmelCase =		intermediate_size
											lowerCAmelCase =		hidden_act
											lowerCAmelCase =		type_sequence_label_size
											lowerCAmelCase =		initializer_range
											lowerCAmelCase =		scope



				def    __snake_case		(			self    ):
											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    __snake_case		(			self    ):
											return ConvNextConfig(
											    num_channels=self.num_channels							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			num_stages=self.num_stages							,			hidden_act=self.hidden_act							,			is_decoder=UpperCAmelCase_							,			initializer_range=self.initializer_range							,			)



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		TFConvNextModel(config=UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			training=UpperCAmelCase_    )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,			)



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.type_sequence_label_size
											lowerCAmelCase =		TFConvNextForImageClassification(UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			labels=UpperCAmelCase_							,			training=UpperCAmelCase_    )
											self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)    )






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




@require_tf
class 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase  ,       unittest.TestCase	):





				'''simple docstring'''



				__a				:			Union[str, Any]					    =(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				__a				:			Optional[int]					    =(
				    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a				:			Any					    =False
				__a				:			int					    =False
				__a				:			Union[str, Any]					    =False
				__a				:			Optional[Any]					    =False
				__a				:			List[Any]					    =False



				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModelTester(self    )
											lowerCAmelCase =		ConfigTester(
											    self							,			config_class=UpperCAmelCase_							,			has_text_modality=UpperCAmelCase_							,			hidden_size=37							,			)



				@unittest.skip(reason='''ConvNext does not use inputs_embeds'''    )
				def    __snake_case		(			self    ):
											pass



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



				@unittest.skip(reason='''ConvNext does not support input and output embeddings'''    )
				def    __snake_case		(			self    ):
											pass



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )
																		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]							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase_    )



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



				def    __snake_case		(			self    ):
											def check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		model(**self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		lowerCAmelCase =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		lowerCAmelCase =		self.model_tester.num_stages
																		self.assertEqual(len(UpperCAmelCase_    )							,			expected_num_stages + 1    )

																		# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]    )							,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,			)

											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		True
																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		lowerCAmelCase =		True

																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_={}    ):
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    )
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    ).to_tuple()

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

																									recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    )

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''    )
											self.assertIsNotNone(UpperCAmelCase_    )




def    UpperCAmelCase							(  ):
							lowerCAmelCase =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image


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





				'''simple docstring'''



				@cached_property
				def    __snake_case		(			self    ):
											return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''    ) if is_vision_available() else None






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''    )

											lowerCAmelCase =		self.default_image_processor
											lowerCAmelCase =		prepare_img()
											lowerCAmelCase =		image_processor(images=UpperCAmelCase_							,			return_tensors='''tf'''    )

											# forward pass
											lowerCAmelCase =		model(**UpperCAmelCase_    )

											# verify the logits
											lowerCAmelCase =		tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape							,			UpperCAmelCase_    )

											lowerCAmelCase =		tf.constant([-0.0260, -0.4739, 0.1911]    )

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



