





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 CLIPImageProcessor





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=7			,  __lowerCamelCase=3			,  __lowerCamelCase=1_8			,  __lowerCamelCase=3_0			,  __lowerCamelCase=4_0_0			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]			,  __lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,  __lowerCamelCase=True			,  )	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							size if size is not None else {"shortest_edge": 2_0}
            _SCREAMING_SNAKE_CASE				:		int  			=							crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _SCREAMING_SNAKE_CASE				:		str  			=							parent
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_channels
            _SCREAMING_SNAKE_CASE				:		Any  			=							image_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							min_resolution
            _SCREAMING_SNAKE_CASE				:		int  			=							max_resolution
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							do_resize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_center_crop
            _SCREAMING_SNAKE_CASE				:		Dict  			=							crop_size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_normalize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_mean
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_std
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							do_convert_rgb




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            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,
            }







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)	->   Tuple:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
                 for i in range(self.batch_size							):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5			,  size=(self.num_channels, self.max_resolution, self.max_resolution)			,  dtype=np.uinta							)							)
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							[]
                 for i in range(self.batch_size							):
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							np.random.choice(np.arange(self.min_resolution			,  self.max_resolution							)			,  2							)
                      image_inputs.append(np.random.randint(2_5_5			,  size=(self.num_channels, width, height)			,  dtype=np.uinta							)							)

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

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

            return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							CLIPImageProcessingTester(self							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 2_0}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 1_8, "width": 1_8}							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict			,  size=4_2			,  crop_size=8_4							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 4_2}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 8_4, "width": 8_4}							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            pass




       def 			UpperCamelCase_	(					self							)	->   str:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		int  			=							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				:		List[str]  			=							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							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random numpy tensors
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  numpify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  np.ndarray							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							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				:		Union[str, Any]  			=							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:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PyTorch tensors
            _SCREAMING_SNAKE_CASE				:		int  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  torchify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  torch.Tensor							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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				:		List[str]  			=							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"],
                )			,  )





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							CLIPImageProcessingTester(self			,  num_channels=4							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							3




       @property
       def 			UpperCamelCase_	(					self							)	->   Dict:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							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 			UpperCamelCase_	(					self							)	->   List[str]:
            pass







       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

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

            # Test batched
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )






'''simple docstring'''



class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''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 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase=0       )							->							int:







				'''simple docstring'''





				__lowercase   =							heap_size
				__lowercase   =							bottom_root
				__lowercase   =							min_node


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''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(_lowerCamelCase       ) - 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   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





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

							# 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       )							->							Union[str, Any]:







				'''simple docstring'''





				return self.min_node.val


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







				'''simple docstring'''





				return self.size == 0


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







				'''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=_lowerCamelCase   ,min_node=_lowerCamelCase   ,heap_size=_lowerCamelCase       )

				# 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(_lowerCamelCase       )

				return min_value


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							str:







				'''simple docstring'''





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


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







				'''simple docstring'''





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

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


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

	doctest.testmod()





