



"""simple docstring"""
import numpy
class     a		:



  def __init__(  self       :    Optional[int]				,						lowerCAmelCase       :    numpy.ndarray				,						lowerCAmelCase       :    numpy.ndarray						)   ->    None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=input_array

     # Random initial weights are assigned where first argument is the
     # number of nodes in previous layer and second argument is the
     # number of nodes in the next layer.

     # Random initial weights are assigned.
     # self.input_array.shape[1] is used to represent number of nodes in input layer.
     # First hidden layer consists of 4 nodes.
     SCREAMING_SNAKE_CASE_:       Tuple   	=numpy.random.rand(
         self.input_array.shape[1]				,						4						)

     # Random initial values for the first hidden layer.
     # First hidden layer has 4 nodes.
     # Second hidden layer has 3 nodes.
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=numpy.random.rand(
         4				,						3						)

     # Random initial values for the second hidden layer.
     # Second hidden layer has 3 nodes.
     # Output layer has 1 node.
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=numpy.random.rand(3				,						1						)

     # Real output values provided.
     SCREAMING_SNAKE_CASE_:       str   	=output_array

     # Predicted output values by the neural network.
     # Predicted_output array initially consists of zeroes.
     SCREAMING_SNAKE_CASE_:       Tuple   	=numpy.zeros(output_array.shape						)



  def 		lowerCamelCase__				(  self       :    Dict						)   ->    numpy.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Dict   	=sigmoid(
         numpy.dot(self.input_array				,						self.input_layer_and_first_hidden_layer_weights						)						)

     # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
     # connecting the first hidden set of nodes with the second hidden set of nodes.
     SCREAMING_SNAKE_CASE_:       int   	=sigmoid(
         numpy.dot(
             self.layer_between_input_and_first_hidden_layer				,						self.first_hidden_layer_and_second_hidden_layer_weights				,						)						)

     # layer_between_second_hidden_layer_and_output is the layer connecting
     # second hidden layer with the output node.
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=sigmoid(
         numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer				,						self.second_hidden_layer_and_output_layer_weights				,						)						)

     return self.layer_between_second_hidden_layer_and_output



  def 		lowerCamelCase__				(  self       :    Any						)   ->    None:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=numpy.dot(
         self.layer_between_first_hidden_layer_and_second_hidden_layer.T				,						2
         * (self.output_array - self.predicted_output)
         * sigmoid_derivative(self.predicted_output						)				,						)
     SCREAMING_SNAKE_CASE_:       Tuple   	=numpy.dot(
         self.layer_between_input_and_first_hidden_layer.T				,						numpy.dot(
             2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output						)				,						self.second_hidden_layer_and_output_layer_weights.T				,						)
         * sigmoid_derivative(
             self.layer_between_first_hidden_layer_and_second_hidden_layer						)				,						)
     SCREAMING_SNAKE_CASE_:       Optional[int]   	=numpy.dot(
         self.input_array.T				,						numpy.dot(
             numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output						)				,						self.second_hidden_layer_and_output_layer_weights.T				,						)
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer						)				,						self.first_hidden_layer_and_second_hidden_layer_weights.T				,						)
         * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer						)				,						)

     self.input_layer_and_first_hidden_layer_weights += (
         updated_input_layer_and_first_hidden_layer_weights
     )
     self.first_hidden_layer_and_second_hidden_layer_weights += (
         updated_first_hidden_layer_and_second_hidden_layer_weights
     )
     self.second_hidden_layer_and_output_layer_weights += (
         updated_second_hidden_layer_and_output_layer_weights
     )



  def 		lowerCamelCase__				(  self       :    Dict				,						lowerCAmelCase       :    numpy.ndarray				,						lowerCAmelCase       :    int				,						lowerCAmelCase       :    bool						)   ->    None:


     '''simple docstring'''




     for iteration in range(1				,						iterations + 1						):
        SCREAMING_SNAKE_CASE_:       int   	=self.feedforward()
        self.back_propagation()
        if give_loss:
           SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=numpy.mean(numpy.square(output - self.feedforward()						)						)
           print(f'''Iteration {iteration} Loss: {loss}'''						)





  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    numpy.ndarray						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=input_arr

     SCREAMING_SNAKE_CASE_:       List[Any]   	=sigmoid(
         numpy.dot(self.array				,						self.input_layer_and_first_hidden_layer_weights						)						)

     SCREAMING_SNAKE_CASE_:       Tuple   	=sigmoid(
         numpy.dot(
             self.layer_between_input_and_first_hidden_layer				,						self.first_hidden_layer_and_second_hidden_layer_weights				,						)						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=sigmoid(
         numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer				,						self.second_hidden_layer_and_output_layer_weights				,						)						)

     return int(self.layer_between_second_hidden_layer_and_output > 0.6						)







def    __magic_name__ (							lowercase       ):
   return 1 / (1 + numpy.exp(-value       ))





def    __magic_name__ (							lowercase       ):
   return (value) * (1 - (value))





def    __magic_name__ (							):
   SCREAMING_SNAKE_CASE_:       List[str]   	=numpy.array(
       (
           [0, 0, 0],
           [0, 0, 1],
           [0, 1, 0],
           [0, 1, 1],
           [1, 0, 0],
           [1, 0, 1],
           [1, 1, 0],
           [1, 1, 1],
       )							,	dtype=numpy.floataa							,	)

   # True output values for the given input values.
   SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,	dtype=numpy.floataa       )

   # Calling neural network class.
   SCREAMING_SNAKE_CASE_:       int   	=TwoHiddenLayerNeuralNetwork(
       input_array=A__							,	output_array=A__       )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=A__							,	iterations=10							,	give_loss=A__       )

   return neural_network.predict(numpy.array(([1, 1, 1])							,	dtype=numpy.floataa       )       )


if __name__ == "__main__":
   example()











"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			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_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict[str, any]:
				UpperCAmelCase_     : Tuple			     =					copy.deepcopy(self.__dict__						)
				if output["backbone_config"] is not None:
							UpperCAmelCase_     : Union[str, Any]			     =					self.backbone_config.to_dict()
				UpperCAmelCase_     : Any			     =					self.__class__.model_type
				return output







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



