





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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor
     from transformers.image_transforms import get_image_size



class 				_UpperCamelCase       (						unittest.TestCase  ):

       def __init__(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[Any]=7       , _SCREAMING_SNAKE_CASE:			Any=3       , _SCREAMING_SNAKE_CASE:			List[Any]=18       , _SCREAMING_SNAKE_CASE:			Tuple=30       , _SCREAMING_SNAKE_CASE:			List[str]=400       , _SCREAMING_SNAKE_CASE:			Tuple=True       , _SCREAMING_SNAKE_CASE:			List[str]=1 / 255       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Tuple=8       , )							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	batch_size
              UpperCamelCase_ 							=	num_channels
              UpperCamelCase_ 							=	image_size
              UpperCamelCase_ 							=	min_resolution
              UpperCamelCase_ 							=	max_resolution
              UpperCamelCase_ 							=	do_rescale
              UpperCamelCase_ 							=	rescale_factor
              UpperCamelCase_ 							=	do_pad
              UpperCamelCase_ 							=	pad_size

       def lowercase			(		self:			Tuple		)							->			Optional[Any]:




              """simple docstring"""


              return {
                  "do_rescale": self.do_rescale,
                  "rescale_factor": self.rescale_factor,
                  "do_pad": self.do_pad,
                  "pad_size": self.pad_size,
              }



       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			List[Any]=False       , _SCREAMING_SNAKE_CASE:			List[str]=False       , _SCREAMING_SNAKE_CASE:			Any=False		)							->			List[str]:




              """simple docstring"""


              assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

              if equal_resolution:
                     UpperCamelCase_ 							=	[]
                     for i in range(self.batch_size		):
                            image_inputs.append(
                                np.random.randint(
                                    255       , size=(self.num_channels, self.max_resolution, self.max_resolution)       , dtype=np.uinta		)		)
              else:
                     UpperCamelCase_ 							=	[]
                     for i in range(self.batch_size		):
                            UpperCamelCase_							,			UpperCamelCase_ 							=	np.random.choice(np.arange(self.min_resolution       , self.max_resolution		)       , 2		)
                            image_inputs.append(np.random.randint(255       , size=(self.num_channels, width, height)       , dtype=np.uinta		)		)

              if not numpify and not torchify:
                     # PIL expects the channel dimension as last dimension
                     UpperCamelCase_ 							=	[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       , 0       , -1		)		) for x in image_inputs]

              if torchify:
                     UpperCamelCase_ 							=	[torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

              return image_inputs



@require_torch
@require_vision
class 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Union[str, Any]       = SwinaSRImageProcessor if is_vision_available() else None

       def lowercase			(		self:			Union[str, Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	SwinaSRImageProcessingTester(self		)

       @property
       def lowercase			(		self:			Optional[int]		)							->			Optional[int]:




              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()

       def lowercase			(		self:			Tuple		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class(**self.image_processor_dict		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "do_rescale"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "rescale_factor"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "do_pad"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       , "pad_size"		)		)

       def lowercase			(		self:			List[Any]		)							->			Tuple:




              """simple docstring"""


              pass

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_ 							=	get_image_size(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.image_processor_tester.pad_size

              UpperCamelCase_ 							=	(old_height // size + 1) * size - old_height
              UpperCamelCase_ 							=	(old_width // size + 1) * size - old_width
              return old_height + pad_height, old_width + pad_width

       def lowercase			(		self:			Dict		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class(**self.image_processor_dict		)
              # create random PIL images
              UpperCamelCase_ 							=	self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE       , Image.Image		)

              # Test not batched input
              UpperCamelCase_ 							=	image_processor(image_inputs[0]       , return_tensors="pt"		).pixel_values
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.calculate_expected_size(np.array(image_inputs[0]		)		)
              self.assertEqual(
                  encoded_images.shape       , (
                      1,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )       , )

       def lowercase			(		self:			Any		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class(**self.image_processor_dict		)
              # create random numpy tensors
              UpperCamelCase_ 							=	self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE       , numpify=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE       , np.ndarray		)

              # Test not batched input
              UpperCamelCase_ 							=	image_processor(image_inputs[0]       , return_tensors="pt"		).pixel_values
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.calculate_expected_size(image_inputs[0]		)
              self.assertEqual(
                  encoded_images.shape       , (
                      1,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )       , )



       def lowercase			(		self:			str		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.image_processing_class(**self.image_processor_dict		)
              # create random PyTorch tensors
              UpperCamelCase_ 							=	self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE       , torchify=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE       , torch.Tensor		)

              # Test not batched input
              UpperCamelCase_ 							=	image_processor(image_inputs[0]       , return_tensors="pt"		).pixel_values
              UpperCamelCase_							,			UpperCamelCase_ 							=	self.calculate_expected_size(image_inputs[0]		)
              self.assertEqual(
                  encoded_images.shape       , (
                      1,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )       , )




'''simple docstring'''







class 					__a   :

						def __init__(		self	:						str    ,			lowercase__	:						List[str])						->List[str]:

							"""simple docstring"""



							_lowercase							=     val
							# Number of nodes in left subtree
							_lowercase							=     0
							_lowercase							=     None
							_lowercase							=     None
							_lowercase							=     None


						def 	_UpperCAmelCase							(		self	:						List[Any]    ,			lowercase__	:						Union[str, Any])						->str:

							"""simple docstring"""



							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
								_lowercase							=     self.right
								_lowercase							=     None
								if self.right:
									_lowercase							=     other
								_lowercase							=     other
								_lowercase							=     self.left_tree_size * 2 + 1
								return self
							else:
								_lowercase							=     other.right
								_lowercase							=     None
								if other.right:
									_lowercase							=     self
								_lowercase							=     self
								_lowercase							=     other.left_tree_size * 2 + 1
								return other






class 					__a   :

						def __init__(		self	:						Optional[Any]    ,			lowercase__	:						List[Any]=None    ,			lowercase__	:						Any=None    ,			lowercase__	:						Any=0)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     heap_size
							_lowercase							=     bottom_root
							_lowercase							=     min_node

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Union[str, Any])						->Any:

							"""simple docstring"""



							if other.size == 0:
								return None
							if self.size == 0:
								_lowercase							=     other.size
								_lowercase							=     other.bottom_root
								_lowercase							=     other.min_node
								return None
							# Update size
							_lowercase							=     self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
								_lowercase							=     other.min_node
							# Merge

							# Order roots by left_subtree_size
							_lowercase							=     []
							_lowercase		,		_lowercase							=     self.bottom_root, other.bottom_root
							while i or j:
								if i and ((not j) or i.left_tree_size < j.left_tree_size):
									combined_roots_list.append((i, True))
									_lowercase							=     i.parent
								else:
									combined_roots_list.append((j, False))
									_lowercase							=     j.parent
        # Insert links between them
							for i in range(len(lowercase__) - 1):
								if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
									_lowercase							=     combined_roots_list[i + 1][0]
									_lowercase							=     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							_lowercase							=     combined_roots_list[0][0]
							while i.parent:
								if (
								    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
								) or (
								    i.left_tree_size == i.parent.left_tree_size
								    and i.left_tree_size != i.parent.parent.left_tree_size
								):
									# Neighbouring Nodes
									_lowercase							=     i.left
									_lowercase							=     i.parent.parent

									# Merging trees
									_lowercase							=     i.merge_trees(i.parent)

									# Updating links
									_lowercase							=     previous_node
									_lowercase							=     next_node
									if previous_node:
										_lowercase							=     i
									if next_node:
										_lowercase							=     i
								else:
									_lowercase							=     i.parent
        # Updating self.bottom_root
							while i.left:
								_lowercase							=     i.left
							_lowercase							=     i

							# Update other
							_lowercase							=     self.size
							_lowercase							=     self.bottom_root
							_lowercase							=     self.min_node

							# Return the merged heap
							return self

						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						List[Any])						->List[str]:

							"""simple docstring"""



							if self.size == 0:
								_lowercase							=     Node(lowercase__)
								_lowercase							=     1
								_lowercase							=     self.bottom_root
							else:
								# Create new node
								_lowercase							=     Node(lowercase__)

								# Update size
								self.size += 1

								# update min_node
								if val < self.min_node.val:
									_lowercase							=     new_node
								# Put new_node as a bottom_root in heap
								_lowercase							=     new_node
								_lowercase							=     self.bottom_root
								_lowercase							=     new_node

								# Consecutively merge roots with same left_tree_size
								while (
								    self.bottom_root.parent
								    and self.bottom_root.left_tree_size
								    == self.bottom_root.parent.left_tree_size
								):
									# Next node
									_lowercase							=     self.bottom_root.parent.parent

									# Merge
									_lowercase							=     self.bottom_root.merge_trees(self.bottom_root.parent)

									# Update Links
									_lowercase							=     next_node
									_lowercase							=     None
									if next_node:
										_lowercase							=     self.bottom_root

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->List[str]:

							"""simple docstring"""



							return self.min_node.val

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							return self.size == 0

						def 	_UpperCAmelCase							(		self	:						str)						->List[str]:

							"""simple docstring"""



							_lowercase							=     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
								# Update size
								_lowercase							=     0

								# Update bottom root
								_lowercase							=     None

								# Update min_node
								_lowercase							=     None

								return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
								# Update size
								self.size -= 1

								# Update bottom root
								_lowercase							=     self.bottom_root.parent
								_lowercase							=     None

								# Update min_node
								_lowercase							=     self.bottom_root
								_lowercase							=     self.bottom_root.parent
								while i:
									if i.val < self.min_node.val:
										_lowercase							=     i
									_lowercase							=     i.parent
								return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							_lowercase							=     self.min_node.right
							_lowercase							=     None
							_lowercase							=     bottom_of_new
							_lowercase							=     1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
								_lowercase							=     size_of_new * 2 + 1
								_lowercase							=     bottom_of_new.left
								if bottom_of_new.val < min_of_new.val:
									_lowercase							=     bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
								_lowercase							=     size_of_new
								_lowercase							=     bottom_of_new
								_lowercase							=     min_of_new
								# print("Single root, multiple nodes case")
								return min_value
							# Remaining cases
							# Construct heap of right subtree
							_lowercase							=     BinomialHeap(
							    bottom_root=lowercase__    ,			min_node=lowercase__    ,			heap_size=lowercase__)

							# Update size
							_lowercase							=     self.size - 1 - size_of_new

							# Neighbour nodes
							_lowercase							=     self.min_node.left
							_lowercase							=     self.min_node.parent

							# Initialize new bottom_root and min_node
							_lowercase							=     previous_node or next_node
							_lowercase							=     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
								_lowercase							=     next_node

								# Update bottom_root and search for min_node below
								_lowercase							=     previous_node
								_lowercase							=     previous_node
								while self.bottom_root.left:
									_lowercase							=     self.bottom_root.left
									if self.bottom_root.val < self.min_node.val:
										_lowercase							=     self.bottom_root
							if next_node:
								_lowercase							=     previous_node

								# Search for new min_node above min_node
								_lowercase							=     next_node
								while i:
									if i.val < self.min_node.val:
										_lowercase							=     i
									_lowercase							=     i.parent
        # Merge heaps
							self.merge_heaps(lowercase__)

							return min_value

						def 	_UpperCAmelCase							(		self	:						List[str])						->str:

							"""simple docstring"""



							_lowercase							=     self.bottom_root
							while top_root.parent:
								_lowercase							=     top_root.parent
							# preorder
							_lowercase							=     []
							self.__traversal(lowercase__    ,			lowercase__)
							return heap_pre_order

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Union[str, Any]    ,			lowercase__	:						List[Any]    ,			lowercase__	:						Optional[int]=0)						->int:

							"""simple docstring"""



							if curr_node:
								preorder.append((curr_node.val, level))
								self.__traversal(curr_node.left    ,			lowercase__    ,			level + 1)
								self.__traversal(curr_node.right    ,			lowercase__    ,			level + 1)
							else:
								preorder.append(("""#""", level))


						def __str__(		self	:						Optional[Any])						->str:

							"""simple docstring"""



							if self.is_empty():
								return ""
							_lowercase							=     self.pre_order()

							return "\n".join(("""-""" * level + str(lowercase__)) for value, level in preorder_heap)


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()




