


'''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     :						Optional[Any]	=     logging.get_logger(__name__)

__lowercase     :						int	=     {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "beit"




	def __init__(       self						,    __a=8192						,    __a=768						,    __a=12						,    __a=12						,    __a=3072						,    __a="gelu"						,    __a=0.0						,    __a=0.0						,    __a=0.02						,    __a=1E-1_2						,    __a=224						,    __a=16						,    __a=3						,    __a=False						,    __a=False						,    __a=False						,    __a=False						,    __a=0.1						,    __a=0.1						,    __a=True						,    __a=[3, 5, 7, 11]						,    __a=[1, 2, 3, 6]						,    __a=True						,    __a=0.4						,    __a=256						,    __a=1						,    __a=False						,    __a=255						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**__a							)

						__a      :	Any    =  vocab_size
						__a      :	Optional[Any]    =  hidden_size
						__a      :	Union[str, Any]    =  num_hidden_layers
						__a      :	Tuple    =  num_attention_heads
						__a      :	Tuple    =  intermediate_size
						__a      :	str    =  hidden_act
						__a      :	List[str]    =  hidden_dropout_prob
						__a      :	Tuple    =  attention_probs_dropout_prob
						__a      :	List[Any]    =  initializer_range
						__a      :	Optional[int]    =  layer_norm_eps

						__a      :	Any    =  image_size
						__a      :	Optional[Any]    =  patch_size
						__a      :	Dict    =  num_channels
						__a      :	List[str]    =  use_mask_token
						__a      :	Any    =  use_absolute_position_embeddings
						__a      :	List[str]    =  use_relative_position_bias
						__a      :	int    =  use_shared_relative_position_bias
						__a      :	int    =  layer_scale_init_value
						__a      :	Union[str, Any]    =  drop_path_rate
						__a      :	List[Any]    =  use_mean_pooling
						# decode head attributes (semantic segmentation)
						__a      :	List[str]    =  out_indices
						__a      :	Tuple    =  pool_scales
						# auxiliary head attributes (semantic segmentation)
						__a      :	Any    =  use_auxiliary_head
						__a      :	Union[str, Any]    =  auxiliary_loss_weight
						__a      :	List[Any]    =  auxiliary_channels
						__a      :	Union[str, Any]    =  auxiliary_num_convs
						__a      :	Dict    =  auxiliary_concat_input
						__a      :	Tuple    =  semantic_loss_ignore_index





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   version.parse("1.11" )




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 1E-4







'''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()



