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, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import PoolFormerImageProcessor
class   _lowerCAmelCase		(     unittest.TestCase				):







							def __init__(				self     :    Any	,					__snake_case     :    List[Any]	,					__snake_case     :    List[Any]=7	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Dict=30	,					__snake_case     :    Optional[Any]=400	,					__snake_case     :    List[Any]=True	,					__snake_case     :    Optional[Any]=None	,					__snake_case     :    int=0.9	,					__snake_case     :    Tuple=None	,					__snake_case     :    Dict=True	,					__snake_case     :    Optional[int]=[0.5, 0.5, 0.5]	,					__snake_case     :    Tuple=[0.5, 0.5, 0.5]	,					):
										lowerCamelCase						:List[str]						  =     size if size is not None else {'''shortest_edge''': 30}
										lowerCamelCase						:Tuple						  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
										lowerCamelCase						:Optional[int]						  =     parent
										lowerCamelCase						:List[Any]						  =     batch_size
										lowerCamelCase						:int						  =     num_channels
										lowerCamelCase						:int						  =     min_resolution
										lowerCamelCase						:int						  =     max_resolution
										lowerCamelCase						:int						  =     do_resize_and_center_crop
										lowerCamelCase						:List[Any]						  =     size
										lowerCamelCase						:Union[str, Any]						  =     crop_pct
										lowerCamelCase						:int						  =     crop_size
										lowerCamelCase						:int						  =     do_normalize
										lowerCamelCase						:List[Any]						  =     image_mean
										lowerCamelCase						:Optional[int]						  =     image_std







							def 				snake_case  (				self     :    str		):
										return {
										    "size": self.size,
										    "do_resize_and_center_crop": self.do_resize_and_center_crop,
										    "crop_pct": self.crop_pct,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}


@require_torch
@require_vision
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     PoolFormerImageProcessor if is_vision_available() else None







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:Optional[int]						  =     PoolFormerImageProcessingTester(self		)







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







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Tuple						  =     self.image_processing_class(**self.image_processor_dict		)
										self.assertTrue(hasattr(__snake_case	,					'''do_resize_and_center_crop'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''size'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''crop_pct'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''do_normalize'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''image_mean'''		)		)
										self.assertTrue(hasattr(__snake_case	,					'''image_std'''		)		)







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Union[str, Any]						  =     self.image_processing_class.from_dict(self.image_processor_dict		)
										self.assertEqual(image_processor.size	,					{'''shortest_edge''': 30}		)
										self.assertEqual(image_processor.crop_size	,					{'''height''': 30, '''width''': 30}		)

										lowerCamelCase						:Optional[Any]						  =     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     :    List[Any]		):
										pass







							def 				snake_case  (				self     :    Optional[int]		):
										# Initialize image_processing
										lowerCamelCase						:Optional[Any]						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random PIL images
										lowerCamelCase						:str						  =     prepare_image_inputs(self.image_processor_tester	,					equal_resolution=__snake_case		)
										for image in image_inputs:
													self.assertIsInstance(__snake_case	,					Image.Image		)

										# Test not batched input
										lowerCamelCase						:Tuple						  =     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
										lowerCamelCase						:Tuple						  =     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 				snake_case  (				self     :    Tuple		):
										# Initialize image_processing
										lowerCamelCase						:Dict						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random numpy tensors
										lowerCamelCase						:int						  =     prepare_image_inputs(self.image_processor_tester	,					equal_resolution=__snake_case	,					numpify=__snake_case		)
										for image in image_inputs:
													self.assertIsInstance(__snake_case	,					np.ndarray		)

										# Test not batched input
										lowerCamelCase						: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
										lowerCamelCase						:Optional[Any]						  =     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 				snake_case  (				self     :    int		):
										# Initialize image_processing
										lowerCamelCase						:int						  =     self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										lowerCamelCase						:Union[str, Any]						  =     prepare_image_inputs(self.image_processor_tester	,					equal_resolution=__snake_case	,					torchify=__snake_case		)
										for image in image_inputs:
													self.assertIsInstance(__snake_case	,					torch.Tensor		)

										# Test not batched input
										lowerCamelCase						:Union[str, 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
										lowerCamelCase						:Tuple						  =     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'''],
										    )	,					)




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



