
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  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=7				,       UpperCamelCase__=3				,       UpperCamelCase__=18				,       UpperCamelCase__=30				,       UpperCamelCase__=400				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=True				,       UpperCamelCase__=None				,       UpperCamelCase__=True				,       UpperCamelCase__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,       UpperCamelCase__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,       UpperCamelCase__=True				,       ):
					A__  :	Any         =							size if size is not None else {'''height''': 224, '''width''': 224}
					A__  :	Optional[Any]         =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
					A__  :	Optional[Any]         =							parent
					A__  :	Optional[Any]         =							batch_size
					A__  :	List[str]         =							num_channels
					A__  :	Tuple         =							image_size
					A__  :	Union[str, Any]         =							min_resolution
					A__  :	List[str]         =							max_resolution
					A__  :	Union[str, Any]         =							do_resize
					A__  :	int         =							size
					A__  :	Tuple         =							do_center_crop
					A__  :	List[Any]         =							crop_size
					A__  :	List[str]         =							do_normalize
					A__  :	List[Any]         =							image_mean
					A__  :	Any         =							image_std
					A__  :	Union[str, Any]         =							do_convert_rgb





	def   __snake_case				(			self		):
					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   __snake_case				(			self				,       UpperCamelCase__=False				,       UpperCamelCase__=False				,       UpperCamelCase__=False		):
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
									A__  :	Union[str, Any]         =							[]
									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__  :	int         =							[]
									for i in range(self.batch_size		):
													A__       ,							A__  :	Optional[int]         =							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__  :	Tuple         =							[Image.fromarray(np.moveaxis(UpperCamelCase__				,       0				,       -1		)		) for x in image_inputs]

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

					return image_inputs







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







	'''simple docstring'''
	_lowerCAmelCase							       =				ChineseCLIPImageProcessor if is_vision_available() else None





	def   __snake_case				(			self		):
					A__  :	Dict         =							ChineseCLIPImageProcessingTester(self				,       do_center_crop=UpperCamelCase__		)





	@property
	def   __snake_case				(			self		):
					return self.image_processor_tester.prepare_image_processor_dict()





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.image_processing_class(**self.image_processor_dict		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_resize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''size'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_normalize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_mean'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_std'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_convert_rgb'''		)		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							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__  :	int         =							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   __snake_case				(			self		):
					pass





	def   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	List[str]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PIL images
					A__  :	str         =							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       Image.Image		)

					# Test not batched input
					A__  :	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
					A__  :	Optional[Any]         =							image_processing(UpperCamelCase__				,       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		):
					# Initialize image_processing
					A__  :	Union[str, Any]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random numpy tensors
					A__  :	Dict         =							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__				,       numpify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       np.ndarray		)

					# Test not batched input
					A__  :	Optional[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
					A__  :	int         =							image_processing(UpperCamelCase__				,       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		):
					# Initialize image_processing
					A__  :	Tuple         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PyTorch tensors
					A__  :	Dict         =							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       torch.Tensor		)

					# Test not batched input
					A__  :	Optional[Any]         =							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__  :	Optional[Any]         =							image_processing(UpperCamelCase__				,       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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				ChineseCLIPImageProcessor if is_vision_available() else None





	def   __snake_case				(			self		):
					A__  :	int         =							ChineseCLIPImageProcessingTester(self				,       num_channels=4				,       do_center_crop=UpperCamelCase__		)
					A__  :	Optional[Any]         =							3





	@property
	def   __snake_case				(			self		):
					return self.image_processor_tester.prepare_image_processor_dict()





	def   __snake_case				(			self		):
					A__  :	Any         =							self.image_processing_class(**self.image_processor_dict		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_resize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''size'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''center_crop'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_normalize'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_mean'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''image_std'''		)		)
					self.assertTrue(hasattr(UpperCamelCase__				,       '''do_convert_rgb'''		)		)





	def   __snake_case				(			self		):
					pass





	def   __snake_case				(			self		):
					# Initialize image_processing
					A__  :	Optional[Any]         =							self.image_processing_class(**self.image_processor_dict		)
					# create random PIL images
					A__  :	List[Any]         =							self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__		)
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__				,       Image.Image		)

					# Test not batched input
					A__  :	Optional[int]         =							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__  :	Tuple         =							image_processing(UpperCamelCase__				,       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'''],
					    )				,       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				A__  :	Optional[Any]         =							len(__UpperCamelCase       )

				for i in range(n - 1       ):
								for j in range(i + 1							,						__UpperCamelCase       ):
												if arr[i] > arr[j]:
																num_inversions += 1

				return num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Tuple:

				"""simple docstring"""







				if len(__UpperCamelCase       ) <= 1:
								return arr, 0
				A__  :	Optional[int]         =							len(__UpperCamelCase       ) // 2
				A__  :	List[str]         =							arr[0:mid]
				A__  :	Union[str, Any]         =							arr[mid:]

				A__       ,							A__  :	List[Any]         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							_count_cross_inversions(__UpperCamelCase							,						__UpperCamelCase       )

				A__  :	Any         =							inversion_p + inversions_q + cross_inversions
				return c, num_inversions

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						List[Any]       ) ->       Dict:

				"""simple docstring"""







				A__  :	str         =							[]
				A__  :	Tuple         =							0
				while i < len(__UpperCamelCase       ) and j < len(__UpperCamelCase       ):
								if p[i] > q[j]:
												# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
												# These are all inversions. The claim emerges from the
												# property that P is sorted.
												num_inversion += len(__UpperCamelCase       ) - i
												r.append(q[j]       )
												j += 1
								else:
												r.append(p[i]       )
												i += 1

				if i < len(__UpperCamelCase       ):
								r.extend(p[i:]       )
				else:
								r.extend(q[j:]       )

				return r, num_inversion

def 				SCREAMING_SNAKE_CASE     (      ) ->       Tuple:

				"""simple docstring"""







				A__  :	List[str]         =							[10, 2, 1, 5, 5, 2, 11]

				# this arr has 8 inversions:
				# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

				A__  :	int         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	int         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 8

				print('''number of inversions = '''							,						__UpperCamelCase       )

				# testing an array with zero inversion (a sorted arr_1)

				arr_a.sort()
				A__  :	Optional[Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Dict         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )

				# an empty list should also have zero inversions
				A__  :	Union[str, Any]         =							[]
				A__  :	Union[str, Any]         =							count_inversions_bf(__UpperCamelCase       )
				A__       ,							A__  :	Any         =							count_inversions_recursive(__UpperCamelCase       )

				assert num_inversions_bf == num_inversions_recursive == 0
				print('''number of inversions = '''							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()