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 MobileViTImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	Optional[int]     , a_     :	Union[str, Any]     , a_     :	str=7     , a_     :	List[str]=3     , a_     :	Optional[int]=18     , a_     :	Union[str, Any]=30     , a_     :	int=400     , a_     :	Dict=True     , a_     :	Dict=None     , a_     :	Optional[int]=True     , a_     :	Optional[int]=None     , a_     :	List[Any]=True     , )->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size if size is not None else {"""shortest_edge""": 20}
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		parent
       SCREAMING_SNAKE_CASE__				:				Any			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_size
       SCREAMING_SNAKE_CASE__				:				int			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				int			=		do_resize
       SCREAMING_SNAKE_CASE__				:				List[str]			=		size
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_center_crop
       SCREAMING_SNAKE_CASE__				:				str			=		crop_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		do_flip_channel_order




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }




@require_torch
@require_vision
class       snake_case					(      _UpperCAmelCase   ,						unittest.TestCase						):
  lowercase_			=     MobileViTImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		MobileViTImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	str				)->       Union[str, Any]:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Optional[int]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(lowercase__     , 'do_resize'				)				)
       self.assertTrue(hasattr(lowercase__     , 'size'				)				)
       self.assertTrue(hasattr(lowercase__     , 'do_center_crop'				)				)
       self.assertTrue(hasattr(lowercase__     , 'center_crop'				)				)
       self.assertTrue(hasattr(lowercase__     , 'do_flip_channel_order'				)				)




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class.from_dict(self.image_processor_dict				)
       self.assertEqual(image_processor.size     , {'shortest_edge': 20}				)
       self.assertEqual(image_processor.crop_size     , {'height': 18, 'width': 18}				)

       SCREAMING_SNAKE_CASE__				:				int			=		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 	__lowercase(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	Optional[int]				)->       str:




       """simple docstring"""
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				int			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=lowercase__				)
       for image in image_inputs:
            self.assertIsInstance(lowercase__     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				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
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_processing(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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )     , )




  def 	__lowercase(      self     :	List[str]				)->       Any:




       """simple docstring"""
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=lowercase__     , numpify=lowercase__				)
       for image in image_inputs:
            self.assertIsInstance(lowercase__     , np.ndarray				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		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(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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )     , )




  def 	__lowercase(      self     :	Union[str, Any]				)->       str:




       """simple docstring"""
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__				:				Dict			=		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=lowercase__     , torchify=lowercase__				)
       for image in image_inputs:
            self.assertIsInstance(lowercase__     , torch.Tensor				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				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
       SCREAMING_SNAKE_CASE__				:				Any			=		image_processing(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.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )     , )




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		angle_in_radians
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		3
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		-1

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

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

     return round(lowercase__     ,  lowercase__ )


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



