






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 BlipImageProcessor




class 							a_ (						unittest.TestCase		):


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=7			,			lowercase_=3			,			lowercase_=1_8			,			lowercase_=3_0			,			lowercase_=4_0_0			,			lowercase_=True			,			lowercase_=None			,			lowercase_=True			,			lowercase_=False			,			lowercase_=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,			lowercase_=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,			lowercase_=True			,			)					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     size if size is not None else {'height': 2_0, 'width': 2_0}
														lowerCAmelCase_										=     parent
														lowerCAmelCase_										=     batch_size
														lowerCAmelCase_										=     num_channels
														lowerCAmelCase_										=     image_size
														lowerCAmelCase_										=     min_resolution
														lowerCAmelCase_										=     max_resolution
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     do_normalize
														lowerCAmelCase_										=     image_mean
														lowerCAmelCase_										=     image_std
														lowerCAmelCase_										=     do_pad
														lowerCAmelCase_										=     do_convert_rgb

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

														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_convert_rgb": self.do_convert_rgb,
														    "do_pad": self.do_pad,
														}






							def      _lowercase       (		self			,			lowercase_=False			,			lowercase_=False			,			lowercase_=False   )					->					Dict:
														'''simple docstring'''

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

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

														if torchify:
																					lowerCAmelCase_										=     [torch.from_numpy(lowercase_   ) for x in image_inputs]

														return image_inputs




@require_torch
@require_vision
class 							a_ (						a_	,	unittest.TestCase		):


							'''simple docstring'''






							__a:	Any     =    BlipImageProcessor if is_vision_available() else None

							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     BlipImageProcessingTester(self   )

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

														return self.image_processor_tester.prepare_image_processor_dict()

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

														lowerCAmelCase_										=     self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowercase_			,			'do_resize'   )   )
														self.assertTrue(hasattr(lowercase_			,			'size'   )   )
														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      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														pass

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

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

														# Test not batched input
														lowerCAmelCase_										=     image_processor(image_inputs[0]			,			return_tensors='pt'   ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)

														# Test batched
														lowerCAmelCase_										=     image_processor(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.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     self.image_processing_class(**self.image_processor_dict   )
														# create random numpy tensors
														lowerCAmelCase_										=     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
														lowerCAmelCase_										=     image_processor(image_inputs[0]			,			return_tensors='pt'   ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)

														# Test batched
														lowerCAmelCase_										=     image_processor(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.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)






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

														lowerCAmelCase_										=     self.image_processing_class(**self.image_processor_dict   )
														# create random PyTorch tensors
														lowerCAmelCase_										=     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
														lowerCAmelCase_										=     image_processor(image_inputs[0]			,			return_tensors='pt'   ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)

														# Test batched
														lowerCAmelCase_										=     image_processor(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.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)




@require_torch
@require_vision
class 							a_ (						a_	,	unittest.TestCase		):


							'''simple docstring'''






							__a:	str     =    BlipImageProcessor if is_vision_available() else None

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     BlipImageProcessingTester(self			,			num_channels=4   )
														lowerCAmelCase_										=     3

							@property
							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														return self.image_processor_tester.prepare_image_processor_dict()

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowercase_			,			'do_resize'   )   )
														self.assertTrue(hasattr(lowercase_			,			'size'   )   )
														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      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														pass






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

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

														# Test not batched input
														lowerCAmelCase_										=     image_processor(image_inputs[0]			,			return_tensors='pt'   ).pixel_values
														self.assertEqual(
														    encoded_images.shape			,			(
														        1,
														        self.expected_encoded_image_num_channels,
														        self.image_processor_tester.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)

														# Test batched
														lowerCAmelCase_										=     image_processor(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.size['height'],
														        self.image_processor_tester.size['width'],
														    )			,			)











import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



