


'''simple docstring'''



import os
import sys
import unittest


__lowercase     :						Any	=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowercase     :						Optional[Any]	=     os.path.join(git_repo_path, 'src', 'diffusers')





class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  find_backend('    if not is_torch_available():'							)
						self.assertEqual(__a						,    'torch'							)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						__a      :	str    =  find_backend('    if not (is_torch_available() and is_transformers_available()):'							)
						self.assertEqual(__a						,    'torch_and_transformers'							)

						# double_backend_with_underscore = find_backend(
						#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
						# )
						# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

						__a      :	Optional[int]    =  find_backend(
						    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'							)
						self.assertEqual(__a						,    'torch_and_transformers_and_onnx'							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn('torch'						,    __a							)
						self.assertIn('torch_and_transformers'						,    __a							)
						self.assertIn('flax_and_transformers'						,    __a							)
						self.assertIn('torch_and_transformers_and_onnx'						,    __a							)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn('UNet2DModel'						,    objects['torch']							)
						self.assertIn('FlaxUNet2DConditionModel'						,    objects['flax']							)
						self.assertIn('StableDiffusionPipeline'						,    objects['torch_and_transformers']							)
						self.assertIn('FlaxStableDiffusionPipeline'						,    objects['flax_and_transformers']							)
						self.assertIn('LMSDiscreteScheduler'						,    objects['torch_and_scipy']							)
						self.assertIn('OnnxStableDiffusionPipeline'						,    objects['torch_and_transformers_and_onnx']							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  create_dummy_object('CONSTANT'						,    '\'torch\''							)
						self.assertEqual(__a						,    '\nCONSTANT = None\n'							)

						__a      :	Any    =  create_dummy_object('function'						,    '\'torch\''							)
						self.assertEqual(
						    __a						,    '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'							)

						__a      :	List[Any]    =  '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
						__a      :	Union[str, Any]    =  create_dummy_object('FakeClass'						,    '\'torch\''							)
						self.assertEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
						__a      :	Optional[int]    =  create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}							)
						self.assertEqual(dummy_files['torch']						,    __a							)








'''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()



