
"""simple docstring"""





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 __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					List[Any]=7       ,   _snake_case						:					str=3       ,   _snake_case						:					Tuple=18       ,   _snake_case						:					int=30       ,   _snake_case						:					Optional[Any]=400       ,   _snake_case						:					Any=True       ,   _snake_case						:					List[Any]=None       ,   _snake_case						:					Union[str, Any]=True       ,   _snake_case						:					Union[str, Any]=None       ,   _snake_case						:					Dict=True       ,   _snake_case						:					List[Any]=[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]       ,   _snake_case						:					Tuple=[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]       ,   _snake_case						:					Optional[Any]=True       ,   )      -> Optional[int]:







								"""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      lowerCamelCase__     (				self						:					Any	)      -> Union[str, Any]:







								"""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      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					Union[str, Any]=False       ,   _snake_case						:					Union[str, Any]=False       ,   _snake_case						:					int=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(_snake_case       ,   0       ,   -1	)	) for x in image_inputs]

								if torchify:
													A_				 =						[torch.from_numpy(_snake_case	) for x in image_inputs]

								return image_inputs





@require_torch
@require_vision
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				ChineseCLIPImageProcessor if is_vision_available() else None




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						ChineseCLIPImageProcessingTester(self       ,   do_center_crop=_snake_case	)




			@property
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()




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







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "do_resize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_center_crop"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "center_crop"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_normalize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "image_mean"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "image_std"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_convert_rgb"	)	)




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







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					str	)      -> Dict:







								"""simple docstring"""
								# Initialize image_processing
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								A_				 =						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   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(_snake_case       ,   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      lowerCamelCase__     (				self						:					Optional[int]	)      -> str:







								"""simple docstring"""
								# Initialize image_processing
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								A_				 =						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case       ,   numpify=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   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(_snake_case       ,   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      lowerCamelCase__     (				self						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								# Initialize image_processing
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								A_				 =						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case       ,   torchify=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   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(_snake_case       ,   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	      =				ChineseCLIPImageProcessor if is_vision_available() else None




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						ChineseCLIPImageProcessingTester(self       ,   num_channels=4       ,   do_center_crop=_snake_case	)
								A_				 =						3




			@property
			def      lowerCamelCase__     (				self						:					str	)      -> Optional[Any]:







								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "do_resize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_center_crop"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "center_crop"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_normalize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "image_mean"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "image_std"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_convert_rgb"	)	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> int:







								"""simple docstring"""
								pass






			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								# Initialize image_processing
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								A_				 =						self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   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(_snake_case       ,   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"""





import baseaa
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaaencode(string.encode("utf-8"				)				)
def 							A_     (__a				):

					'''simple docstring'''





					return baseaa.aaadecode(__a				).decode("utf-8"				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




