





'''simple docstring'''


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 				lowerCAmelCase_						(						unittest.TestCase						):




	def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=3     ,       _lowerCAmelCase=18     ,       _lowerCAmelCase=30     ,       _lowerCAmelCase=400     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=[0.48145466, 0.4578275, 0.40821073]     ,       _lowerCAmelCase=[0.26862954, 0.26130258, 0.27577711]     ,       _lowerCAmelCase=True     ,       )      ->							Optional[Any]:
					_lowerCAmelCase		     = parent
					_lowerCAmelCase		     = batch_size
					_lowerCAmelCase		     = num_channels
					_lowerCAmelCase		     = image_size
					_lowerCAmelCase		     = min_resolution
					_lowerCAmelCase		     = max_resolution
					_lowerCAmelCase		     = do_resize
					_lowerCAmelCase		     = size if size is not None else {"height": 18, "width": 18}
					_lowerCAmelCase		     = do_center_crop
					_lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 18, "width": 18}
					_lowerCAmelCase		     = do_normalize
					_lowerCAmelCase		     = image_mean
					_lowerCAmelCase		     = image_std
					_lowerCAmelCase		     = do_convert_rgb





	def 							_snake_case				(		self						)      ->							Union[str, Any]:
					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 				lowerCAmelCase_						(						UpperCamelCase__  ,unittest.TestCase						):
	__lowerCamelCase  :							List[Any]    					=   OwlViTImageProcessor if is_vision_available() else None




	def 							_snake_case				(		self						)      ->							Optional[int]:
					_lowerCAmelCase		     = OwlViTImageProcessingTester(self						)




	@property
	def 							_snake_case				(		self						)      ->							str:
					return self.image_processor_tester.prepare_image_processor_dict()




	def 							_snake_case				(		self						)      ->							Optional[Any]:
					_lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "do_resize"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "size"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "do_center_crop"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "center_crop"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "do_normalize"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "image_mean"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "image_std"						)						)
					self.assertTrue(hasattr(__lowerCamelCase     ,       "do_convert_rgb"						)						)




	def 							_snake_case				(		self						)      ->							Dict:
					_lowerCAmelCase		     = 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}						)

					_lowerCAmelCase		     = 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 							_snake_case				(		self						)      ->							List[str]:
					_lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					_lowerCAmelCase		     = prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase						)

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

					# Test not batched input
					_lowerCAmelCase		     = 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
					_lowerCAmelCase		     = image_processing(__lowerCamelCase     ,       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 							_snake_case				(		self						)      ->							str:
					_lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					_lowerCAmelCase		     = prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase     ,       numpify=__lowerCamelCase						)
					for image in image_inputs:
									self.assertIsInstance(__lowerCamelCase     ,       np.ndarray						)

					# Test not batched input
					_lowerCAmelCase		     = 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
					_lowerCAmelCase		     = image_processing(__lowerCamelCase     ,       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 							_snake_case				(		self						)      ->							List[Any]:
					_lowerCAmelCase		     = self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					_lowerCAmelCase		     = prepare_image_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase     ,       torchify=__lowerCamelCase						)
					for image in image_inputs:
									self.assertIsInstance(__lowerCamelCase     ,       torch.Tensor						)

					# Test not batched input
					_lowerCAmelCase		     = 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
					_lowerCAmelCase		     = image_processing(__lowerCamelCase     ,       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"],
					    )     ,       )









'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE			=							TypeVar("T", bound=bool)

class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = value
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = None




 @property
 def 							_snake_case				(		self						)      ->							T:
     return self._value





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							SkewNode[T] | None:
     if not roota:
         return roota

     if not roota:
         return roota

     if roota.value > roota.value:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = roota, roota

     _lowerCAmelCase		     = roota
     _lowerCAmelCase		     = roota.right
     _lowerCAmelCase		     = roota.left
     _lowerCAmelCase		     = SkewNode.merge(_lowerCAmelCase     ,       _lowerCAmelCase						)

     return result







class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase = ()						)      ->							None:
     _lowerCAmelCase		     = None
     if data:
         for item in data:
             self.insert(_lowerCAmelCase						)




 def __bool__(		self						)      ->							bool:
     return self._root is not None




 def __iter__(		self						)      ->							Iterator[T]:
     _lowerCAmelCase		     = []
     while self:
         result.append(self.pop()						)

     # Pushing items back to the heap not to clear it.
     for item in result:
         self.insert(_lowerCAmelCase						)

     return iter(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = SkewNode.merge(self._root     ,       SkewNode(_lowerCAmelCase						)						)




 def 							_snake_case				(		self						)      ->							T | None:
     _lowerCAmelCase		     = self.top()
     _lowerCAmelCase		     = (
         SkewNode.merge(self._root.left     ,       self._root.right						) if self._root else None
     )

     return result




 def 							_snake_case				(		self						)      ->							T:
     if not self._root:
         raise IndexError("Can't get top element for the empty heap."						)
     return self._root.value





 def 							_snake_case				(		self						)      ->							None:
     _lowerCAmelCase		     = None


if __name__ == "__main__":
       import doctest

       doctest.testmod()


