

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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							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 :   Any					  =							parent
				SCREAMING_SNAKE_CASE :   List[str]					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							num_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   str					  =							min_resolution
				SCREAMING_SNAKE_CASE :   List[Any]					  =							max_resolution
				SCREAMING_SNAKE_CASE :   int					  =							do_resize
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							size
				SCREAMING_SNAKE_CASE :   Any					  =							do_center_crop
				SCREAMING_SNAKE_CASE :   int					  =							crop_size
				SCREAMING_SNAKE_CASE :   int					  =							do_flip_channel_order






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Union[str, 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 _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MobileViTImageProcessor if is_vision_available() else None
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							MobileViTImageProcessingTester(self						)
			@property
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[str]:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(a   ,   "do_resize"						)						)
				self.assertTrue(hasattr(a   ,   "size"						)						)
				self.assertTrue(hasattr(a   ,   "do_center_crop"						)						)
				self.assertTrue(hasattr(a   ,   "center_crop"						)						)
				self.assertTrue(hasattr(a   ,   "do_flip_channel_order"						)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							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 :   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				:			Dict						)				->							int:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							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 :   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 :   str					  =							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				:			Dict						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE :   Tuple					  =							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 :   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(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				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   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 :   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 :   List[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"],
				    )   ,   )


from math import factorial, pi

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_sin() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_sin() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   int					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1) for r in range(_a))

def 			lowerCamelCase__			(       _a				,      _a = 30):

	if not isinstance(_a				,      (int, float)):
		raise ValueError("maclaurin_cos() requires either an int or float for theta")

	if not isinstance(_a				,      _a) or accuracy <= 0:
		raise ValueError("maclaurin_cos() requires a positive int for accuracy")

	SCREAMING_SNAKE_CASE :   str					  =							float(_a)
	SCREAMING_SNAKE_CASE :   Any					  =							theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r) for r in range(_a))


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			print(maclaurin_sin(10))
			print(maclaurin_sin(-10))
			print(maclaurin_sin(10, 15))
			print(maclaurin_sin(-10, 15))

			print(maclaurin_cos(5))
			print(maclaurin_cos(-5))
			print(maclaurin_cos(10, 15))
			print(maclaurin_cos(-10, 15))