



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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Any=7				,       SCREAMING_SNAKE_CASE :     Dict=3				,       SCREAMING_SNAKE_CASE :     Any=18				,       SCREAMING_SNAKE_CASE :     List[str]=30				,       SCREAMING_SNAKE_CASE :     Tuple=400				,       SCREAMING_SNAKE_CASE :     Any=True				,       SCREAMING_SNAKE_CASE :     List[str]=None				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=True				,       SCREAMING_SNAKE_CASE :     Optional[Any]=None				,       SCREAMING_SNAKE_CASE :     List[Any]=True				,       SCREAMING_SNAKE_CASE :     Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,       SCREAMING_SNAKE_CASE :     Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,       SCREAMING_SNAKE_CASE :     Tuple=True				,       ):
									lowercase__					:   Dict													=					size if size is not None else {"height": 224, "width": 224}
									lowercase__					:   str													=					crop_size if crop_size is not None else {"height": 18, "width": 18}
									lowercase__					:   int													=					parent
									lowercase__					:   str													=					batch_size
									lowercase__					:   Dict													=					num_channels
									lowercase__					:   Tuple													=					image_size
									lowercase__					:   Dict													=					min_resolution
									lowercase__					:   Optional[Any]													=					max_resolution
									lowercase__					:   Optional[Any]													=					do_resize
									lowercase__					:   int													=					size
									lowercase__					:   Dict													=					do_center_crop
									lowercase__					:   Union[str, Any]													=					crop_size
									lowercase__					:   int													=					do_normalize
									lowercase__					:   Tuple													=					image_mean
									lowercase__					:   Any													=					image_std
									lowercase__					:   List[Any]													=					do_convert_rgb







					def 			snake_case					(							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 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]=False				,       SCREAMING_SNAKE_CASE :     Dict=False				,       SCREAMING_SNAKE_CASE :     Optional[Any]=False					):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													lowercase__					:   Tuple													=					[]
													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:
													lowercase__					:   List[Any]													=					[]
													for i in range(self.batch_size					):
																	lowercase__							,							lowercase__					:   Optional[Any]													=					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
													lowercase__					:   Optional[int]													=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				,       0				,       -1					)					) for x in image_inputs]

									if torchify:
													lowercase__					:   Any													=					[torch.from_numpy(SCREAMING_SNAKE_CASE					) for x in image_inputs]

									return image_inputs






@require_torch
@require_vision
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		ChineseCLIPImageProcessor if is_vision_available() else None







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Optional[Any]													=					ChineseCLIPImageProcessingTester(self				,       do_center_crop=SCREAMING_SNAKE_CASE					)







					@property
					def 			snake_case					(							self :     List[Any]					):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Optional[int]													=					self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_resize"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "size"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_center_crop"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "center_crop"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_normalize"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_mean"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_std"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_convert_rgb"					)					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   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}					)

									lowercase__					:   str													=					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 :     Union[str, Any]					):
									pass







					def 			snake_case					(							self :     Union[str, Any]					):
									# Initialize image_processing
									lowercase__					:   Dict													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									lowercase__					:   str													=					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
									lowercase__					:   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
									lowercase__					:   List[Any]													=					image_processing(SCREAMING_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 			snake_case					(							self :     int					):
									# Initialize image_processing
									lowercase__					:   int													=					self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									lowercase__					:   Optional[int]													=					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
									lowercase__					:   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
									lowercase__					:   Dict													=					image_processing(SCREAMING_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 			snake_case					(							self :     Any					):
									# Initialize image_processing
									lowercase__					:   Any													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									lowercase__					:   Optional[Any]													=					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
									lowercase__					:   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
									lowercase__					:   Union[str, Any]													=					image_processing(SCREAMING_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 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		ChineseCLIPImageProcessor if is_vision_available() else None







					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Optional[Any]													=					ChineseCLIPImageProcessingTester(self				,       num_channels=4				,       do_center_crop=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					3







					@property
					def 			snake_case					(							self :     int					):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   Tuple													=					self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_resize"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "size"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_center_crop"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "center_crop"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_normalize"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_mean"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "image_std"					)					)
									self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				,       "do_convert_rgb"					)					)







					def 			snake_case					(							self :     Dict					):
									pass







					def 			snake_case					(							self :     Optional[int]					):
									# Initialize image_processing
									lowercase__					:   Dict													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									lowercase__					:   Dict													=					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
									lowercase__					:   Optional[Any]													=					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
									lowercase__					:   Dict													=					image_processing(SCREAMING_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"],
									    )				,       )









def  __lowerCamelCase (       lowerCamelCase__ = 1_000						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   int													=					1, 1
				lowercase__					:   List[Any]													=					[]
				for i in range(1					,			n + 1						):
								lowercase__					:   Dict													=					prev_numerator + 2 * prev_denominator
								lowercase__					:   Tuple													=					prev_numerator + prev_denominator
								if len(str(lowerCamelCase__						)						) > len(str(lowerCamelCase__						)						):
												result.append(lowerCamelCase__						)
								lowercase__					:   int													=					numerator
								lowercase__					:   int													=					denominator

				return len(lowerCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')




