




"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A				=							logging.get_logger(__name__)

A				=							{
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}



class   a__					(						__magic_name__				,     __magic_name__ ):
			lowercase_		=						"dinat"

			lowercase_		=						{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}


			def __init__(   self   :     Optional[int]   ,	UpperCamelCase_   :     int=4   ,	UpperCamelCase_   :     str=3   ,	UpperCamelCase_   :     Optional[Any]=64   ,	UpperCamelCase_   :     Union[str, Any]=[3, 4, 6, 5]   ,	UpperCamelCase_   :     Union[str, Any]=[2, 4, 8, 16]   ,	UpperCamelCase_   :     List[Any]=7   ,	UpperCamelCase_   :     List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]   ,	UpperCamelCase_   :     Tuple=3.0   ,	UpperCamelCase_   :     List[Any]=True   ,	UpperCamelCase_   :     Dict=0.0   ,	UpperCamelCase_   :     Union[str, Any]=0.0   ,	UpperCamelCase_   :     int=0.1   ,	UpperCamelCase_   :     int="gelu"   ,	UpperCamelCase_   :     Dict=0.02   ,	UpperCamelCase_   :     List[Any]=1e-5   ,	UpperCamelCase_   :     List[str]=0.0   ,	UpperCamelCase_   :     int=None   ,	UpperCamelCase_   :     Optional[Any]=None   ,	**UpperCamelCase_   :     List[Any]   ,	):


						"""simple docstring"""




						super().__init__(**UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, Any]												=       patch_size
						__UpperCAmelCase						:			int												=       num_channels
						__UpperCAmelCase						:			List[str]												=       embed_dim
						__UpperCAmelCase						:			List[Any]												=       depths
						__UpperCAmelCase						:			List[str]												=       len(UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       num_heads
						__UpperCAmelCase						:			Union[str, Any]												=       kernel_size
						__UpperCAmelCase						:			Dict												=       dilations
						__UpperCAmelCase						:			Optional[int]												=       mlp_ratio
						__UpperCAmelCase						:			Tuple												=       qkv_bias
						__UpperCAmelCase						:			Dict												=       hidden_dropout_prob
						__UpperCAmelCase						:			Tuple												=       attention_probs_dropout_prob
						__UpperCAmelCase						:			str												=       drop_path_rate
						__UpperCAmelCase						:			int												=       hidden_act
						__UpperCAmelCase						:			Dict												=       layer_norm_eps
						__UpperCAmelCase						:			List[Any]												=       initializer_range
						# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
						# this indicates the channel dimension after the last stage of the model
						__UpperCAmelCase						:			Dict												=       int(embed_dim * 2 ** (len(UpperCamelCase_) - 1))
						__UpperCAmelCase						:			Dict												=       layer_scale_init_value
						__UpperCAmelCase						:			int												=       ["stem"] + [F"stage{idx}" for idx in range(1   ,	len(UpperCamelCase_) + 1)]
						__UpperCAmelCase						,  __UpperCAmelCase						:			str												=       get_aligned_output_features_output_indices(
						    out_features=UpperCamelCase_   ,	out_indices=UpperCamelCase_   ,	stage_names=self.stage_names)







import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							SCREAMING_SNAKE_CASE__   			=   np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

							# Create two fuzzy sets by defining any membership function
							# (trapmf(), gbellmf(), gaussmf(), etc).
							SCREAMING_SNAKE_CASE__   			=   [0, 2_5, 5_0]
							SCREAMING_SNAKE_CASE__   			=   [2_5, 5_0, 7_5]
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)

							# Compute the different operations using inbuilt functions.
							SCREAMING_SNAKE_CASE__   			=   np.ones(7_5)
							SCREAMING_SNAKE_CASE__   			=   np.zeros((7_5,))
							# 1. Union = max(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, young, X, middle_aged)[1]
							# 2. Intersection = min(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, middle_aged)[1]
							# 3. Complement (A) = (1- min(µA(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_not(young)
							# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
							# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
							SCREAMING_SNAKE_CASE__   			=   young + middle_aged - (young * middle_aged)
							# 6. Algebraic Product = (µA(x) * µB(x))
							SCREAMING_SNAKE_CASE__   			=   young * middle_aged
							# 7. Bounded Sum = min[1,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
							# 8. Bounded difference = min[0,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   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, 1_0)
							plt.plot(X, bdd_difference)
							plt.title('''bdd_difference''')
							plt.grid(True)

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




