




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   ):




    """simple docstring"""

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


    def        a				(	self					:					str       )				->	Tuple:
         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	(   A_     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				PoolFormerImageProcessor if is_vision_available() else None


    def        a				(	self					:					Tuple       )				->	Union[str, Any]:
         lowerCAmelCase__          =			PoolFormerImageProcessingTester(self       )


    @property
    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()


    def        a				(	self					:					int       )				->	List[str]:
         lowerCAmelCase__          =			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        a				(	self					:					int       )				->	int:
         lowerCAmelCase__          =			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__          =			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        a				(	self					:					Union[str, Any]       )				->	str:
         pass


    def        a				(	self					:					int       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         lowerCAmelCase__          =			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__          =			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__          =			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        a				(	self					:					Tuple       )				->	List[Any]:
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         lowerCAmelCase__          =			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__          =			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__          =			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        a				(	self					:					Union[str, Any]       )				->	Any:
         lowerCAmelCase__          =			self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         lowerCAmelCase__          =			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__          =			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__          =			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 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()
