
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 MobileNetVaImageProcessor





class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=7 ,     _A=3 ,     _A=18 ,     _A=30 ,     _A=400 ,     _A=True ,     _A=None ,     _A=True ,     _A=None ,     )				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      size if size is not None else {'shortest_edge': 20}
          SCREAMING_SNAKE_CASE_	=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
          SCREAMING_SNAKE_CASE_	=      parent
          SCREAMING_SNAKE_CASE_	=      batch_size
          SCREAMING_SNAKE_CASE_	=      num_channels
          SCREAMING_SNAKE_CASE_	=      image_size
          SCREAMING_SNAKE_CASE_	=      min_resolution
          SCREAMING_SNAKE_CASE_	=      max_resolution
          SCREAMING_SNAKE_CASE_	=      do_resize
          SCREAMING_SNAKE_CASE_	=      size
          SCREAMING_SNAKE_CASE_	=      do_center_crop
          SCREAMING_SNAKE_CASE_	=      crop_size




       def     _UpperCamelCase	(							self	)				->				str:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class 				UpperCamelCase__		(	__lowerCamelCase       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =MobileNetVaImageProcessor if is_vision_available() else None



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      MobileNetVaImageProcessingTester(self	)



       @property
       def     _UpperCamelCase	(							self	)				->				List[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      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'''	)	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size ,     {'''shortest_edge''': 20}	)
          self.assertEqual(image_processor.crop_size ,     {'''height''': 18, '''width''': 18}	)

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



       def     _UpperCamelCase	(							self	)				->				Dict:
          pass



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          SCREAMING_SNAKE_CASE_	=      prepare_image_inputs(self.image_processor_tester ,     equal_resolution=lowerCamelCase_	)
          for image in image_inputs:
             self.assertIsInstance(lowerCamelCase_ ,     Image.Image	)

          # Test not batched input
          SCREAMING_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
          SCREAMING_SNAKE_CASE_	=      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     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          SCREAMING_SNAKE_CASE_	=      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
          SCREAMING_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
          SCREAMING_SNAKE_CASE_	=      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     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          SCREAMING_SNAKE_CASE_	=      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
          SCREAMING_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
          SCREAMING_SNAKE_CASE_	=      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

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

SCREAMING_SNAKE_CASE						=    TypeVar('T', bound=bool)



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     T      					=				value
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None







				@property
				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					return self._value







				@staticmethod
				def 				__UpperCAmelCase       (			lowerCamelCase_     :							RandomizedHeapNode[T] | None      ,       lowerCamelCase_     :							RandomizedHeapNode[T] | None    ):

					"""simple docstring"""






					if not roota:
						return roota

					if not roota:
						return roota

					if roota.value > roota.value:
						_lowercase					,   _lowercase			:     List[Any]      					=				roota, roota

					if random.choice([True, False]    ):
						_lowercase					,   _lowercase			:     Optional[int]      					=				roota.right, roota.left

					_lowercase			:     Tuple      					=				RandomizedHeapNode.merge(roota.left      ,       lowerCamelCase_    )

					return roota



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							Iterable[T] | None = ()    ):

					"""simple docstring"""






					_lowercase			:     RandomizedHeapNode[T] | None      					=				None

					if data:
						for item in data:
							self.insert(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     str      					=				RandomizedHeapNode.merge(self._root      ,       RandomizedHeapNode(lowerCamelCase_    )    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.top()

					if self._root is None:
						return None

					_lowercase			:     List[Any]      					=				RandomizedHeapNode.merge(self._root.left      ,       self._root.right    )

					return result







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					if not self._root:
						raise IndexError('Can\'t get top element for the empty heap.'    )
					return self._root.value







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				None







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

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				[]
					while self:
						result.append(self.pop()    )

					return result







				def __bool__(			self     :							List[str]    ):

					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()



