import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import OwlViTImageProcessor

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				str=7  ,					__SCREAMING_SNAKE_CASE    :				List[str]=3  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=18  ,					__SCREAMING_SNAKE_CASE    :				str=30  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=400  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=None  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,					__SCREAMING_SNAKE_CASE    :				str=True  ,					):






					"""simple docstring"""
					_snake_case              =      parent
					_snake_case              =      batch_size
					_snake_case              =      num_channels
					_snake_case              =      image_size
					_snake_case              =      min_resolution
					_snake_case              =      max_resolution
					_snake_case              =      do_resize
					_snake_case              =      size if size is not None else {'height': 18, 'width': 18}
					_snake_case              =      do_center_crop
					_snake_case              =      crop_size if crop_size is not None else {'height': 18, 'width': 18}
					_snake_case              =      do_normalize
					_snake_case              =      image_mean
					_snake_case              =      image_std
					_snake_case              =      do_convert_rgb







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					}

@require_torch
@require_vision
class 	_SCREAMING_SNAKE_CASE							(     lowercase__      ,     unittest.TestCase							):






		"""simple docstring"""







		A_						 =			OwlViTImageProcessor if is_vision_available() else None



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      OwlViTImageProcessingTester(self   )



		@property
		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(_A  ,					"do_resize"   )   )
					self.assertTrue(hasattr(_A  ,					"size"   )   )
					self.assertTrue(hasattr(_A  ,					"do_center_crop"   )   )
					self.assertTrue(hasattr(_A  ,					"center_crop"   )   )
					self.assertTrue(hasattr(_A  ,					"do_normalize"   )   )
					self.assertTrue(hasattr(_A  ,					"image_mean"   )   )
					self.assertTrue(hasattr(_A  ,					"image_std"   )   )
					self.assertTrue(hasattr(_A  ,					"do_convert_rgb"   )   )



		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      self.image_processing_class.from_dict(self.image_processor_dict   )
					self.assertEqual(image_processor.size  ,					{"height": 18, "width": 18}   )
					self.assertEqual(image_processor.crop_size  ,					{"height": 18, "width": 18}   )

					_snake_case              =      self.image_processing_class.from_dict(self.image_processor_dict  ,					size=42  ,					crop_size=84   )
					self.assertEqual(image_processor.size  ,					{"height": 42, "width": 42}   )
					self.assertEqual(image_processor.crop_size  ,					{"height": 84, "width": 84}   )



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					_snake_case              =      prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=_A   )

					for image in image_inputs:
								self.assertIsInstance(_A  ,					Image.Image   )

					# Test not batched input
					_snake_case              =      image_processing(image_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processing(_A  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_snake_case              =      prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=_A  ,					numpify=_A   )
					for image in image_inputs:
								self.assertIsInstance(_A  ,					np.ndarray   )

					# Test not batched input
					_snake_case              =      image_processing(image_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processing(_A  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)







		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					_snake_case              =      prepare_image_inputs(self.image_processor_tester  ,					equal_resolution=_A  ,					torchify=_A   )
					for image in image_inputs:
								self.assertIsInstance(_A  ,					torch.Tensor   )

					# Test not batched input
					_snake_case              =      image_processing(image_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processing(_A  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_images.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

