


'''simple docstring'''



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 ):




	def __init__(       self						,    __a						,    __a=7						,    __a=3						,    __a=18						,    __a=30						,    __a=400						,    __a=True						,    __a=None						,    __a=True						,    __a=None						,    __a=True						,    ):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self							):

						'''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    (		lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   MobileViTImageProcessor if is_vision_available() else None




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  MobileViTImageProcessingTester(self							)




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  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							):

						'''simple docstring'''





						__a      :	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}							)

						__a      :	Tuple    =  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							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						__a      :	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
						__a      :	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
						__a      :	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'],
						    )						,    )




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						__a      :	List[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
						__a      :	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
						__a      :	int    =  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							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						__a      :	str    =  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
						__a      :	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
						__a      :	Optional[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'],
						    )						,    )







'''simple docstring'''



import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
	# Create universe of discourse in Python using linspace ()
	__lowercase     :						int	=     np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

	# Create two fuzzy sets by defining any membership function
	# (trapmf(), gbellmf(), gaussmf(), etc).
	__lowercase     :						Any	=     [0, 25, 50]
	__lowercase     :						int	=     [25, 50, 75]
	__lowercase     :						List[str]	=     fuzz.membership.trimf(X, abca)
	__lowercase     :						Any	=     fuzz.membership.trimf(X, abca)

	# Compute the different operations using inbuilt functions.
	__lowercase     :						List[Any]	=     np.ones(75)
	__lowercase     :						Any	=     np.zeros((75,))
	# 1. Union = max(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_or(X, young, X, middle_aged)[1]
	# 2. Intersection = min(µA(x), µB(x))
	__lowercase     :						int	=     fuzz.fuzzy_and(X, young, X, middle_aged)[1]
	# 3. Complement (A) = (1- min(µA(x))
	__lowercase     :						str	=     fuzz.fuzzy_not(young)
	# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
	__lowercase     :						List[Any]	=     fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
	# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
	__lowercase     :						Optional[Any]	=     young + middle_aged - (young * middle_aged)
	# 6. Algebraic Product = (µA(x) * µB(x))
	__lowercase     :						str	=     young * middle_aged
	# 7. Bounded Sum = min[1,(µA(x), µB(x))]
	__lowercase     :						Optional[Any]	=     fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
	# 8. Bounded difference = min[0,(µA(x), µB(x))]
	__lowercase     :						Union[str, Any]	=     fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

	# max-min composition
	# max-product composition

	# Plot each set A, set B and each operation result using plot() and subplot().
	from matplotlib import pyplot as plt

	plt.figure()

	plt.subplot(4, 3, 1)
	plt.plot(X, young)
	plt.title('Young')
	plt.grid(True)

	plt.subplot(4, 3, 2)
	plt.plot(X, middle_aged)
	plt.title('Middle aged')
	plt.grid(True)

	plt.subplot(4, 3, 3)
	plt.plot(X, union)
	plt.title('union')
	plt.grid(True)

	plt.subplot(4, 3, 4)
	plt.plot(X, intersection)
	plt.title('intersection')
	plt.grid(True)

	plt.subplot(4, 3, 5)
	plt.plot(X, complement_a)
	plt.title('complement_a')
	plt.grid(True)

	plt.subplot(4, 3, 6)
	plt.plot(X, difference)
	plt.title('difference a/b')
	plt.grid(True)

	plt.subplot(4, 3, 7)
	plt.plot(X, alg_sum)
	plt.title('alg_sum')
	plt.grid(True)

	plt.subplot(4, 3, 8)
	plt.plot(X, alg_product)
	plt.title('alg_product')
	plt.grid(True)

	plt.subplot(4, 3, 9)
	plt.plot(X, bdd_sum)
	plt.title('bdd_sum')
	plt.grid(True)

	plt.subplot(4, 3, 10)
	plt.plot(X, bdd_difference)
	plt.title('bdd_difference')
	plt.grid(True)

	plt.subplots_adjust(hspace=0.5)
	plt.show()



