




"""simple docstring"""





import numpy as np
import skfuzzy as fuzz

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

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


A   =       logging.get_logger(__name__)

A   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""simple docstring"""




						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	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

						__UpperCAmelCase	: str		=					use_timm_backbone
						__UpperCAmelCase	: int		=					backbone_config
						__UpperCAmelCase	: Dict		=					num_channels
						__UpperCAmelCase	: Optional[Any]		=					num_queries
						__UpperCAmelCase	: List[Any]		=					d_model
						__UpperCAmelCase	: Union[str, Any]		=					encoder_ffn_dim
						__UpperCAmelCase	: int		=					encoder_layers
						__UpperCAmelCase	: Union[str, Any]		=					encoder_attention_heads
						__UpperCAmelCase	: str		=					decoder_ffn_dim
						__UpperCAmelCase	: Any		=					decoder_layers
						__UpperCAmelCase	: Dict		=					decoder_attention_heads
						__UpperCAmelCase	: List[Any]		=					dropout
						__UpperCAmelCase	: int		=					attention_dropout
						__UpperCAmelCase	: str		=					activation_dropout
						__UpperCAmelCase	: Union[str, Any]		=					activation_function
						__UpperCAmelCase	: Optional[int]		=					init_std
						__UpperCAmelCase	: Dict		=					init_xavier_std
						__UpperCAmelCase	: Tuple		=					encoder_layerdrop
						__UpperCAmelCase	: Any		=					decoder_layerdrop
						__UpperCAmelCase	: Tuple		=					encoder_layers
						__UpperCAmelCase	: Optional[int]		=					auxiliary_loss
						__UpperCAmelCase	: List[str]		=					position_embedding_type
						__UpperCAmelCase	: List[str]		=					backbone
						__UpperCAmelCase	: Optional[int]		=					use_pretrained_backbone
						__UpperCAmelCase	: Union[str, Any]		=					dilation
						# Hungarian matcher
						__UpperCAmelCase	: List[Any]		=					class_cost
						__UpperCAmelCase	: Tuple		=					bbox_cost
						__UpperCAmelCase	: int		=					giou_cost
						# Loss coefficients
						__UpperCAmelCase	: Any		=					mask_loss_coefficient
						__UpperCAmelCase	: Optional[Any]		=					dice_loss_coefficient
						__UpperCAmelCase	: Any		=					bbox_loss_coefficient
						__UpperCAmelCase	: int		=					giou_loss_coefficient
						__UpperCAmelCase	: List[Any]		=					eos_coefficient
						super().__init__(is_encoder_decoder=UpperCamelCase_      ,  **UpperCamelCase_)


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


