




'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
						from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import PoolFormerImageProcessor





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.config_class(**self.inputs_dict )
							self.parent.assertTrue(hasattr(__UpperCamelCase,  "hidden_sizes" ) )
							self.parent.assertTrue(hasattr(__UpperCamelCase,  "num_encoder_blocks" ) )





class      lowerCamelCase_							:




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=13,  __UpperCamelCase=64,  __UpperCamelCase=3,  __UpperCamelCase=4,  __UpperCamelCase=[2, 2, 2, 2],  __UpperCamelCase=[8, 4, 2, 1],  __UpperCamelCase=[16, 32, 64, 128],  __UpperCamelCase=[1, 4, 8, 16],  __UpperCamelCase=False,  __UpperCamelCase=True,  __UpperCamelCase="gelu",  __UpperCamelCase=0.1,  __UpperCamelCase=0.02,  __UpperCamelCase=3,  __UpperCamelCase=None,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       image_size
							_snake_case	       =       num_channels
							_snake_case	       =       num_encoder_blocks
							_snake_case	       =       sr_ratios
							_snake_case	       =       depths
							_snake_case	       =       hidden_sizes
							_snake_case	       =       downsampling_rates
							_snake_case	       =       is_training
							_snake_case	       =       use_labels
							_snake_case	       =       hidden_act
							_snake_case	       =       hidden_dropout_prob
							_snake_case	       =       initializer_range
							_snake_case	       =       num_labels
							_snake_case	       =       scope






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							_snake_case	       =       None
							if self.use_labels:
									_snake_case	       =       ids_tensor([self.batch_size, self.image_size, self.image_size],  self.num_labels )

							_snake_case	       =       PoolFormerConfig(
							    image_size=self.image_size,  num_channels=self.num_channels,  num_encoder_blocks=self.num_encoder_blocks,  depths=self.depths,  hidden_sizes=self.hidden_sizes,  hidden_act=self.hidden_act,  hidden_dropout_prob=self.hidden_dropout_prob,  initializer_range=self.initializer_range,  )

							return config, pixel_values, labels






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       PoolFormerModel(config=__UpperCamelCase )
							model.to(__UpperCamelCase )
							model.eval()
							_snake_case	       =       model(__UpperCamelCase )
							_snake_case	       =       _snake_case	       =       self.image_size // 32.0
							self.parent.assertEqual(
							    result.last_hidden_state.shape,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.prepare_config_and_inputs()
							_snake_case       ,    _snake_case       ,    _snake_case	       =       config_and_inputs
							_snake_case	       =       {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowerCamelCase_							(       UpperCAmelCase_,       UpperCAmelCase_,       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
					lowercase__    = (
					    {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowercase__    = False
					lowercase__    = False
					lowercase__    = False
					lowercase__    = False
					lowercase__    = False






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       PoolFormerModelTester(self )
							_snake_case	       =       PoolFormerConfigTester(self,  config_class=__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							self.config_tester.run_common_tests()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase )






					@unittest.skip("PoolFormer does not use inputs_embeds" )
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							pass






					@unittest.skip("PoolFormer does not have get_input_embeddings method and get_output_embeddings methods" )
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							pass






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							def check_hidden_states_output(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       model_class(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.eval()

									with torch.no_grad():
											_snake_case	       =       model(**self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase ) )

									_snake_case	       =       outputs.hidden_states

									_snake_case	       =       self.model_tester.num_encoder_blocks
									self.assertEqual(len(__UpperCamelCase ),  __UpperCamelCase )

									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-3:] ),  [
									        self.model_tester.hidden_sizes[0],
									        self.model_tester.image_size // 4,
									        self.model_tester.image_size // 4,
									    ],  )

							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									_snake_case	       =       True
									check_hidden_states_output(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )

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

									check_hidden_states_output(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							if not self.model_tester.is_training:
									return

							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()
							_snake_case	       =       True

							for model_class in self.all_model_classes:
									if model_class in get_values(__UpperCamelCase ):
											continue
									_snake_case	       =       model_class(__UpperCamelCase )
									model.to(__UpperCamelCase )
									model.train()
									_snake_case	       =       self._prepare_for_class(__UpperCamelCase,  __UpperCamelCase,  return_labels=__UpperCamelCase )
									_snake_case	       =       model(**__UpperCamelCase ).loss
									loss.backward()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case       ,    _snake_case	       =       self.model_tester.prepare_config_and_inputs_for_common()

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

									_snake_case	       =       ["pixel_values"]
									self.assertListEqual(arg_names[:1],  __UpperCamelCase )






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case	       =       PoolFormerModel.from_pretrained(__UpperCamelCase )
									self.assertIsNotNone(__UpperCamelCase )






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
		return image





@require_torch
class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					@slow
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       PoolFormerImageProcessor()
							_snake_case	       =       PoolFormerForImageClassification.from_pretrained("sail/poolformer_s12" ).to(__UpperCamelCase )

							_snake_case	       =       image_processor(images=prepare_img(),  return_tensors="pt" ).to(__UpperCamelCase )

							# forward pass
							with torch.no_grad():
									_snake_case	       =       model(**__UpperCamelCase )

							# verify the logits
							_snake_case	       =       torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape,  __UpperCamelCase )

							_snake_case	       =       torch.tensor([-0.61_13, 0.16_85, -0.04_92] ).to(__UpperCamelCase )
							self.assertTrue(torch.allclose(outputs.logits[0, :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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






					def __len__(    self ):
							return len(self.file_names )






					def __getitem__(    self,  __UpperCamelCase ):
							_snake_case	       =       self.file_names[idx]
							_snake_case	       =       PIL.Image.open(__UpperCamelCase )
							_snake_case	       =       raw_image.convert("RGB" )
							if self.image_transform is not None:
									_snake_case	       =       self.image_transform(__UpperCamelCase )
							_snake_case	       =       extract_label(__UpperCamelCase )
							if self.label_to_id is not None:
									_snake_case	       =       self.label_to_id[label]
							return {"image": image, "label": label}






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_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
		_snake_case	       =       config["lr"]
		_snake_case	       =       int(config["num_epochs"]  )
		_snake_case	       =       int(config["seed"]  )
		_snake_case	       =       int(config["batch_size"]  )
		_snake_case	       =       config["image_size"]
		if not isinstance(lowercase_						,							(list, tuple)  ):
				_snake_case	       =       (image_size, image_size)

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

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

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

		# Split our filenames between train and validation
		_snake_case	       =       np.random.permutation(len(lowercase_  )  )
		_snake_case	       =       int(0.8 * len(lowercase_  )  )
		_snake_case	       =       random_perm[:cut]
		_snake_case	       =       random_perm[cut:]

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

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

		# Instantiate dataloaders.
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_snake_case	       =       create_model("resnet50d"						,							pretrained=lowercase_						,							num_classes=len(lowercase_  )  )

		# 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).
		_snake_case	       =       model.to(accelerator.device  )

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

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

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

		# Instantiate learning rate scheduler
		_snake_case	       =       OneCycleLR(optimizer=lowercase_						,							max_lr=lowercase_						,							epochs=lowercase_						,							steps_per_epoch=len(lowercase_  )  )

		# 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.
		_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case	       =       accelerator.prepare(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

		# Now we train the model
		for epoch in range(lowercase_  ):
				model.train()
				for step, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						_snake_case	       =       model(lowercase_  )
						_snake_case	       =       torch.nn.functional.cross_entropy(lowercase_						,							batch["label"]  )
						accelerator.backward(lowercase_  )
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				_snake_case	       =       0
				_snake_case	       =       0
				for _, batch in enumerate(lowercase_  ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						_snake_case	       =       {k: v.to(accelerator.device  ) for k, v in batch.items()}
						_snake_case	       =       (batch["image"] - mean) / std
						with torch.no_grad():
								_snake_case	       =       model(lowercase_  )
						_snake_case	       =       outputs.argmax(dim=-1  )
						_snake_case       ,    _snake_case	       =       accelerator.gather_for_metrics((predictions, batch["label"])  )
						_snake_case	       =       predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				_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 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       argparse.ArgumentParser(description="Simple example of training script."  )
		parser.add_argument("--data_dir"						,							required=lowercase_						,							help="The data folder on disk."  )
		parser.add_argument(
		    "--mixed_precision"						,							type=lowercase_						,							default=lowercase_						,							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=lowercase_						,							default=lowercase_						,							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."  )
		_snake_case	       =       parser.parse_args()
		_snake_case	       =       {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
		training_function(lowercase_						,							lowercase_  )


if __name__ == "__main__":
						main()





