






'''simple docstring'''







import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							UpperCamelCase    :      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).
							UpperCamelCase    :      Dict  			=		[0, 25, 50]
							UpperCamelCase    :      Optional[int]  			=		[25, 50, 75]
							UpperCamelCase    :      List[Any]  			=		fuzz.membership.trimf(X, abca)
							UpperCamelCase    :      Union[str, Any]  			=		fuzz.membership.trimf(X, abca)

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









'''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 LevitImageProcessor




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,):
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  min_resolution
								lowerCamelCase__         =  max_resolution
								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean
								lowerCamelCase__         =  image_std






		def 			UpperCamelCase_					(      self       ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					LevitImageProcessor if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  LevitImageProcessingTester(self       )






		@property
		def 			UpperCamelCase_					(      self       ):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_mean"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_std"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_normalize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_resize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_center_crop"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""size"""       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 18}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 18, """width""": 18}       )

								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 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,numpify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,torchify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,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(_lowerCAmelCase   ,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"""],
								    )   ,)

