
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	( lowercase			)       ->  List[str]:
		'''simple docstring'''

		UpperCamelCase          =					fname.split(os.path.sep			)[-1]
		return re.search(R'^(.*)_\d+\.jpg$'   ,	_UpperCAmelCase			).groups()[0]


class     lowercase (		__UpperCamelCase       ):



		def __init__(			self    ,				A_    ,				A_=None    ,				A_=None	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					file_names
				UpperCamelCase          =					image_transform
				UpperCamelCase          =					label_to_id



		def __len__(			self	)  -> Dict:
				"""simple docstring"""
				return len(self.file_names	)



		def __getitem__(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.file_names[idx]
				UpperCamelCase          =					PIL.Image.open(A_	)
				UpperCamelCase          =					raw_image.convert('RGB'	)
				if self.image_transform is not None:
						UpperCamelCase          =					self.image_transform(A_	)
				UpperCamelCase          =					extract_label(A_	)
				if self.label_to_id is not None:
						UpperCamelCase          =					self.label_to_id[label]
				return {"image": image, "label": label}






def 				A	( lowercase   ,	lowercase			)       ->  Tuple:
		'''simple docstring'''

		if args.with_tracking:
				UpperCamelCase          =					Accelerator(
				    cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	log_with='all'   ,	project_dir=args.project_dir			)
		else:
				UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

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

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(_UpperCAmelCase   ,	shuffle=_UpperCAmelCase   ,	batch_size=_UpperCAmelCase   ,	num_workers=4			)
		UpperCamelCase          =					DataLoader(_UpperCAmelCase   ,	shuffle=_UpperCAmelCase   ,	batch_size=_UpperCAmelCase   ,	num_workers=4			)

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

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

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

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

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

		# Instantiate learning rate scheduler
		UpperCamelCase          =					OneCycleLR(optimizer=_UpperCAmelCase   ,	max_lr=_UpperCAmelCase   ,	epochs=_UpperCAmelCase   ,	steps_per_epoch=len(_UpperCAmelCase			)			)

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

				if "epoch" in training_difference:
						UpperCamelCase          =					int(training_difference.replace('epoch_'   ,	''			)			) + 1
						UpperCamelCase          =					None
				else:
						UpperCamelCase          =					int(training_difference.replace('step_'   ,	''			)			)
						UpperCamelCase          =					resume_step // len(_UpperCAmelCase			)
						resume_step -= starting_epoch * len(_UpperCAmelCase			)

    # Now we train the model
		for epoch in range(_UpperCAmelCase   ,	_UpperCAmelCase			):
				model.train()
				if args.with_tracking:
						UpperCamelCase          =					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
						UpperCamelCase          =					accelerator.skip_first_batches(_UpperCAmelCase   ,	_UpperCAmelCase			)
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						UpperCamelCase          =					train_dataloader
				for batch in active_dataloader:
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						UpperCamelCase          =					{k: v.to(accelerator.device			) for k, v in batch.items()}
						UpperCamelCase          =					(batch['image'] - mean) / std
						UpperCamelCase          =					model(_UpperCAmelCase			)
						UpperCamelCase          =					torch.nn.functional.cross_entropy(_UpperCAmelCase   ,	batch['label']			)
						# We keep track of the loss at each epoch
						if args.with_tracking:
								total_loss += loss.detach().float()
						accelerator.backward(_UpperCAmelCase			)
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()
						overall_step += 1
						if isinstance(_UpperCAmelCase   ,	_UpperCAmelCase			):
								UpperCamelCase          =					f'''step_{overall_step}'''
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												UpperCamelCase          =					os.path.join(args.output_dir   ,	_UpperCAmelCase			)
										accelerator.save_state(_UpperCAmelCase			)
				model.eval()
				UpperCamelCase          =					0
				UpperCamelCase          =					0
				for step, batch in enumerate(_UpperCAmelCase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						UpperCamelCase          =					{k: v.to(accelerator.device			) for k, v in batch.items()}
						UpperCamelCase          =					(batch['image'] - mean) / std
						with torch.no_grad():
								UpperCamelCase          =					model(_UpperCAmelCase			)
						UpperCamelCase          =					outputs.argmax(dim=-1			)
						UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['label'])			)
						UpperCamelCase          =					predictions == references
						num_elems += accurate_preds.shape[0]
						accurate += accurate_preds.long().sum()

				UpperCamelCase          =					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(_UpperCAmelCase			),
						        'epoch': epoch,
						    }   ,	step=_UpperCAmelCase   ,	)
				if checkpointing_steps == "epoch":
						UpperCamelCase          =					f'''epoch_{epoch}'''
						if args.output_dir is not None:
								UpperCamelCase          =					os.path.join(args.output_dir   ,	_UpperCAmelCase			)
						accelerator.save_state(_UpperCAmelCase			)

		if args.with_tracking:
				accelerator.end_training()


def 				A	( )       ->  int:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument('--data_dir'   ,	required=_UpperCAmelCase   ,	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=_UpperCAmelCase   ,	default=_UpperCAmelCase   ,	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=_UpperCAmelCase   ,	default=_UpperCAmelCase   ,	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=_UpperCAmelCase   ,	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=_UpperCAmelCase   ,	default=_UpperCAmelCase   ,	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=_UpperCAmelCase   ,	default='logs'   ,	help='Location on where to store experiment tracking logs` and relevent project information'   ,	)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
		training_function(_UpperCAmelCase   ,	_UpperCAmelCase			)


if __name__ == "__main__":
					main()


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()

				# fmt: off
				UpperCamelCase          =					['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
				# fmt: on
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)

				UpperCamelCase          =					{
				    'do_resize': True,
				    'size': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				A_	)
				with open(self.image_processor_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						json.dump(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Tuple:
				"""simple docstring"""
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[np.random.randint(255    ,				size=(3, 30, 400)    ,				dtype=np.uinta	)]

				UpperCamelCase          =					[Image.fromarray(np.moveaxis(A_    ,				0    ,				-1	)	) for x in image_inputs]

				return image_inputs



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					self.get_rust_tokenizer()
				UpperCamelCase          =					self.get_image_processor()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_slow.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname    ,				use_fast=A_	)

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_fast.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor_slow.tokenizer.get_vocab()    ,				tokenizer_slow.get_vocab()	)
				self.assertEqual(processor_fast.tokenizer.get_vocab()    ,				tokenizer_fast.get_vocab()	)
				self.assertEqual(tokenizer_slow.get_vocab()    ,				tokenizer_fast.get_vocab()	)
				self.assertIsInstance(processor_slow.tokenizer    ,				A_	)
				self.assertIsInstance(processor_fast.tokenizer    ,				A_	)

				self.assertEqual(processor_slow.image_processor.to_json_string()    ,				image_processor.to_json_string()	)
				self.assertEqual(processor_fast.image_processor.to_json_string()    ,				image_processor.to_json_string()	)
				self.assertIsInstance(processor_slow.image_processor    ,				A_	)
				self.assertIsInstance(processor_fast.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,				image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				UpperCamelCase          =					self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)'	)
				UpperCamelCase          =					self.get_image_processor(do_normalize=A_	)

				UpperCamelCase          =					OwlViTProcessor.from_pretrained(
				    self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=A_	)

				self.assertEqual(processor.tokenizer.get_vocab()    ,				tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer    ,				A_	)

				self.assertEqual(processor.image_processor.to_json_string()    ,				image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					image_processor(A_    ,				return_tensors='np'	)
				UpperCamelCase          =					processor(images=A_    ,				return_tensors='np'	)

				for key in input_image_proc.keys():
						self.assertAlmostEqual(input_image_proc[key].sum()    ,				input_processor[key].sum()    ,				delta=1e-2	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'

				UpperCamelCase          =					processor(text=A_    ,				return_tensors='np'	)

				UpperCamelCase          =					tokenizer(A_    ,				return_tensors='np'	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key][0].tolist()    ,				encoded_processor[key][0].tolist()	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(text=A_    ,				images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask', 'pixel_values']	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					[['cat', 'nasa badge'], ['person']]
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					len(A_	)
				UpperCamelCase          =					max([len(A_	) for texts in input_texts]	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(batch_size * num_max_text_queries, seq_length)	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					inputs['input_ids']
				UpperCamelCase          =					[
				    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				]

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)
				self.assertListEqual(list(input_ids[0]	)    ,				predicted_ids[0]	)
				self.assertListEqual(list(input_ids[1]	)    ,				predicted_ids[1]	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(images=A_    ,				query_images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['query_pixel_values', 'pixel_values']	)

				# test if it raises when no input is passed
				with pytest.raises(A_	):
						processor()



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				UpperCamelCase          =					processor.batch_decode(A_	)
				UpperCamelCase          =					tokenizer.batch_decode(A_	)

				self.assertListEqual(A_    ,				A_	)
