






import numpy as np
import skfuzzy as fuzz

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

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

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[str]						=      """deformable_detr"""
    a__	:						Union[str, Any]						=      {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }







    def __init__(	self , __lowercase=True , __lowercase=None , __lowercase=3 , __lowercase=300 , __lowercase=1_024 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=0.0 , __lowercase=True , __lowercase="relu" , __lowercase=256 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , __lowercase=True , __lowercase=False , __lowercase="sine" , __lowercase="resnet50" , __lowercase=True , __lowercase=False , __lowercase=4 , __lowercase=4 , __lowercase=4 , __lowercase=False , __lowercase=300 , __lowercase=False , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=1 , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=0.1 , __lowercase=0.25 , __lowercase=False , **__lowercase , )       ->	int:
        if backbone_config is not None and use_timm_backbone:
            raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                __UpperCamelCase     :str			  =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
            elif isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :str			  =		backbone_config.get('''model_type''')
                __UpperCamelCase     :Tuple			  =		CONFIG_MAPPING[backbone_model_type]
                __UpperCamelCase     :Any			  =		config_class.from_dict(__lowercase)
        __UpperCamelCase     :int			  =		use_timm_backbone
        __UpperCamelCase     :Dict			  =		backbone_config
        __UpperCamelCase     :Any			  =		num_channels
        __UpperCamelCase     :Optional[int]			  =		num_queries
        __UpperCamelCase     :Any			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		d_model
        __UpperCamelCase     :Tuple			  =		encoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		encoder_layers
        __UpperCamelCase     :List[Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Any			  =		decoder_ffn_dim
        __UpperCamelCase     :List[str]			  =		decoder_layers
        __UpperCamelCase     :int			  =		decoder_attention_heads
        __UpperCamelCase     :str			  =		dropout
        __UpperCamelCase     :Any			  =		attention_dropout
        __UpperCamelCase     :int			  =		activation_dropout
        __UpperCamelCase     :List[Any]			  =		activation_function
        __UpperCamelCase     :List[Any]			  =		init_std
        __UpperCamelCase     :List[Any]			  =		init_xavier_std
        __UpperCamelCase     :int			  =		encoder_layerdrop
        __UpperCamelCase     :str			  =		auxiliary_loss
        __UpperCamelCase     :Optional[Any]			  =		position_embedding_type
        __UpperCamelCase     :Union[str, Any]			  =		backbone
        __UpperCamelCase     :Any			  =		use_pretrained_backbone
        __UpperCamelCase     :str			  =		dilation
        # deformable attributes
        __UpperCamelCase     :Optional[Any]			  =		num_feature_levels
        __UpperCamelCase     :str			  =		encoder_n_points
        __UpperCamelCase     :int			  =		decoder_n_points
        __UpperCamelCase     :Union[str, Any]			  =		two_stage
        __UpperCamelCase     :Optional[Any]			  =		two_stage_num_proposals
        __UpperCamelCase     :Dict			  =		with_box_refine
        if two_stage is True and with_box_refine is False:
            raise ValueError('''If two_stage is True, with_box_refine must be True.''')
        # Hungarian matcher
        __UpperCamelCase     :Optional[int]			  =		class_cost
        __UpperCamelCase     :List[Any]			  =		bbox_cost
        __UpperCamelCase     :str			  =		giou_cost
        # Loss coefficients
        __UpperCamelCase     :Tuple			  =		mask_loss_coefficient
        __UpperCamelCase     :Tuple			  =		dice_loss_coefficient
        __UpperCamelCase     :int			  =		bbox_loss_coefficient
        __UpperCamelCase     :Any			  =		giou_loss_coefficient
        __UpperCamelCase     :Dict			  =		eos_coefficient
        __UpperCamelCase     :Optional[Any]			  =		focal_alpha
        __UpperCamelCase     :Optional[Any]			  =		disable_custom_kernels
        super().__init__(is_encoder_decoder=__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.encoder_attention_heads







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.d_model







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
            __UpperCamelCase     :Tuple			  =		self.backbone_config.to_dict()
        __UpperCamelCase     :List[Any]			  =		self.__class__.model_type
        return output





