


'''simple docstring'''






import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
 # Create universe of discourse in Python using linspace ()
 lowercase							=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							=[0, 25, 50]
 lowercase							=[25, 50, 75]
 lowercase							=fuzz.membership.trimf(X, abca)
 lowercase							=fuzz.membership.trimf(X, abca)

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






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="levit"






       def __init__(      self  ,							snake_case=2_2_4  ,							snake_case=3  ,							snake_case=3  ,							snake_case=2  ,							snake_case=1  ,							snake_case=1_6  ,							snake_case=[1_2_8, 2_5_6, 3_8_4]  ,							snake_case=[4, 8, 1_2]  ,							snake_case=[4, 4, 4]  ,							snake_case=[1_6, 1_6, 1_6]  ,							snake_case=0  ,							snake_case=[2, 2, 2]  ,							snake_case=[2, 2, 2]  ,							snake_case=0.02  ,							**snake_case  ,							)						->  Any:




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase       :       List[str]							   =image_size
          _UpperCAmelCase       :       str							   =num_channels
          _UpperCAmelCase       :       int							   =kernel_size
          _UpperCAmelCase       :       Any							   =stride
          _UpperCAmelCase       :       Tuple							   =padding
          _UpperCAmelCase       :       Optional[int]							   =hidden_sizes
          _UpperCAmelCase       :       List[str]							   =num_attention_heads
          _UpperCAmelCase       :       List[Any]							   =depths
          _UpperCAmelCase       :       Any							   =key_dim
          _UpperCAmelCase       :       List[Any]							   =drop_path_rate
          _UpperCAmelCase       :       int							   =patch_size
          _UpperCAmelCase       :       Tuple							   =attention_ratio
          _UpperCAmelCase       :       Any							   =mlp_ratio
          _UpperCAmelCase       :       Optional[Any]							   =initializer_range
          _UpperCAmelCase       :       str							   =[
              ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=version.parse("1.11"    )






       @property
       def       lowerCAmelCase		(      self)						->  Mapping[str, Mapping[int, str]]:




          '''simple docstring'''
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ])






       @property
       def       lowerCAmelCase		(      self)						->  float:




          '''simple docstring'''
          return 1E-4

