

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 ChineseCLIPImageProcessor

class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=3					,					lowercase=18					,					lowercase=30					,					lowercase=400					,					lowercase=True					,					lowercase=None					,					lowercase=True					,					lowercase=None					,					lowercase=True					,					lowercase=[0.4814_5466, 0.457_8275, 0.4082_1073]					,					lowercase=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,					lowercase=True					,					)     ->  Dict:
							'''simple docstring'''






							A__						=	size if size is not None else {"height": 224, "width": 224}
							A__						=	crop_size if crop_size is not None else {"height": 18, "width": 18}
							A__						=	parent
							A__						=	batch_size
							A__						=	num_channels
							A__						=	image_size
							A__						=	min_resolution
							A__						=	max_resolution
							A__						=	do_resize
							A__						=	size
							A__						=	do_center_crop
							A__						=	crop_size
							A__						=	do_normalize
							A__						=	image_mean
							A__						=	image_std
							A__						=	do_convert_rgb


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''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,
							}


			def    UpperCamelCase						(			self					,					lowercase=False					,					lowercase=False					,					lowercase=False				)     ->  Union[str, Any]:
							'''simple docstring'''






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

							if equal_resolution:
											A__						=	[]
											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:
											A__						=	[]
											for i in range(self.batch_size				):
															A__			, A__						=	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
											A__						=	[Image.fromarray(np.moveaxis(lowercase					,					0					,					-1				)				) for x in image_inputs]

							if torchify:
											A__						=	[torch.from_numpy(lowercase				) for x in image_inputs]

							return image_inputs

@require_torch
@require_vision
class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				ChineseCLIPImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	ChineseCLIPImageProcessingTester(self					,					do_center_crop=lowercase				)


			@property
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_resize"				)				)
							self.assertTrue(hasattr(lowercase					,					"size"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_normalize"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_mean"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_std"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_convert_rgb"				)				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size					,					{"height": 224, "width": 224}				)
							self.assertEqual(image_processor.crop_size					,					{"height": 18, "width": 18}				)

							A__						=	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				)     ->  Optional[int]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






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

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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				)     ->  List[str]:
							'''simple docstring'''






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

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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				)     ->  Optional[int]:
							'''simple docstring'''






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

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				ChineseCLIPImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	ChineseCLIPImageProcessingTester(self					,					num_channels=4					,					do_center_crop=lowercase				)
							A__						=	3


			@property
			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowercase					,					"do_resize"				)				)
							self.assertTrue(hasattr(lowercase					,					"size"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"center_crop"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_normalize"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_mean"				)				)
							self.assertTrue(hasattr(lowercase					,					"image_std"				)				)
							self.assertTrue(hasattr(lowercase					,					"do_convert_rgb"				)				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






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

							# Test not batched input
							A__						=	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
							A__						=	image_processing(lowercase					,					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"],
							    )					,					)




class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       name
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       val



			def __str__( self  :     Dict):
									return F"{self.__class__.__name__}({self.name}, {self.val})"




			def __lt__( self  :     List[str]   ,						lowerCAmelCase__  :     Any):
									return self.val < other.val







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: int  		=       {}
									SCREAMING_SNAKE_CASE_: Any  		=       self.build_heap(lowerCAmelCase__)



			def __getitem__( self  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.get_value(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return (idx - 1) // 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									return idx * 2 + 1



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return idx * 2 + 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									return self.heap_dict[key]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_parent_idx(lowerCAmelCase__)

									for idx, i in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       idx
															SCREAMING_SNAKE_CASE_: str  		=       i.val

									for i in range(lowerCAmelCase__   ,						-1   ,						-1):
															self.sift_down(lowerCAmelCase__   ,						lowerCAmelCase__)
									return array



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									while True:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_left_child_idx(lowerCAmelCase__)  # noqa: E741
															SCREAMING_SNAKE_CASE_: Dict  		=       self.get_right_child_idx(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       idx
															if l < len(lowerCAmelCase__) and array[l] < array[idx]:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       l
															if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
																					SCREAMING_SNAKE_CASE_: str  		=       r

															if smallest != idx:
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       array[smallest], array[idx]
																					(
																					    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    
																					): Optional[Any]  		=       (
																					    self.idx_of_element[array[smallest]],
																					    self.idx_of_element[array[idx]],
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       smallest
															else:
																					break



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_parent_idx(lowerCAmelCase__)
									while p >= 0 and self.heap[p] > self.heap[idx]:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.heap[idx], self.heap[p]
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       p
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_parent_idx(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.heap[0]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.heap[-1], self.heap[0]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

									SCREAMING_SNAKE_CASE_: int  		=       self.heap.pop()
									del self.idx_of_element[x]
									self.sift_down(0   ,						self.heap)
									return x



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									self.heap.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       len(self.heap) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       node.val
									self.sift_up(len(self.heap) - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return len(self.heap) == 0




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									SCREAMING_SNAKE_CASE_: Any  		=       new_value
									SCREAMING_SNAKE_CASE_: Tuple  		=       new_value
									self.sift_up(self.idx_of_element[node])







lowerCAmelCase							:  int   	=						Node("""R""", -1)
lowerCAmelCase							:  str   	=						Node("""B""", 6)
lowerCAmelCase							:  str   	=						Node("""A""", 3)
lowerCAmelCase							:  List[str]   	=						Node("""X""", 1)
lowerCAmelCase							:  Union[str, Any]   	=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase							:  Optional[Any]   	=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()






