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 		a__						(					unittest.TestCase					):

					def __init__(    self      :       Any,_A      :       List[Any],_A      :       List[Any]=7,_A      :       Tuple=3,_A      :       Optional[Any]=30,_A      :       Optional[Any]=400,_A      :       Union[str, Any]=True,_A      :       Optional[int]=None,_A      :       str=0.9,_A      :       str=None,_A      :       str=True,_A      :       int=[0.5, 0.5, 0.5],_A      :       Dict=[0.5, 0.5, 0.5],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    size if size is not None else {"shortest_edge": 30}
							SCREAMING_SNAKE_CASE_  :		int				      =    crop_size if crop_size is not None else {"height": 30, "width": 30}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Any				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		int				      =    num_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    min_resolution
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_resolution
							SCREAMING_SNAKE_CASE_  :		int				      =    do_resize_and_center_crop
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    crop_pct
							SCREAMING_SNAKE_CASE_  :		str				      =    crop_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_normalize
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_mean
							SCREAMING_SNAKE_CASE_  :		Any				      =    image_std






					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							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 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							PoolFormerImageProcessor if is_vision_available() else None

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    PoolFormerImageProcessingTester(self    )

					@property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(_A,"do_resize_and_center_crop"    )    )
							self.assertTrue(hasattr(_A,"size"    )    )
							self.assertTrue(hasattr(_A,"crop_pct"    )    )
							self.assertTrue(hasattr(_A,"do_normalize"    )    )
							self.assertTrue(hasattr(_A,"image_mean"    )    )
							self.assertTrue(hasattr(_A,"image_std"    )    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    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}    )

							SCREAMING_SNAKE_CASE_  :		List[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 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    prepare_image_inputs(self.image_processor_tester,equal_resolution=_A    )
							for image in image_inputs:
									self.assertIsInstance(_A,Image.Image    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		Optional[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
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    image_processing(_A,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 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    prepare_image_inputs(self.image_processor_tester,equal_resolution=_A,numpify=_A    )
							for image in image_inputs:
									self.assertIsInstance(_A,np.ndarray    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		List[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
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_processing(_A,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 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							SCREAMING_SNAKE_CASE_  :		Dict				      =    prepare_image_inputs(self.image_processor_tester,equal_resolution=_A,torchify=_A    )
							for image in image_inputs:
									self.assertIsInstance(_A,torch.Tensor    )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		List[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
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_processing(_A,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 math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
