import copy
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/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'conditional_detr'
							_UpperCAmelCase						    =     ['past_key_values']
							_UpperCAmelCase						    =     {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(				self     :    Union[str, Any]	,					__snake_case     :    Any=True	,					__snake_case     :    Tuple=None	,					__snake_case     :    Union[str, Any]=3	,					__snake_case     :    Dict=300	,					__snake_case     :    Tuple=6	,					__snake_case     :    Union[str, Any]=2048	,					__snake_case     :    List[Any]=8	,					__snake_case     :    List[str]=6	,					__snake_case     :    int=2048	,					__snake_case     :    Any=8	,					__snake_case     :    Any=0.0	,					__snake_case     :    Any=0.0	,					__snake_case     :    List[str]=True	,					__snake_case     :    Dict="relu"	,					__snake_case     :    int=256	,					__snake_case     :    Union[str, Any]=0.1	,					__snake_case     :    Dict=0.0	,					__snake_case     :    Tuple=0.0	,					__snake_case     :    Any=0.0_2	,					__snake_case     :    Union[str, Any]=1.0	,					__snake_case     :    List[Any]=False	,					__snake_case     :    Optional[Any]="sine"	,					__snake_case     :    List[Any]="resnet50"	,					__snake_case     :    str=True	,					__snake_case     :    Optional[int]=False	,					__snake_case     :    Optional[Any]=2	,					__snake_case     :    Optional[Any]=5	,					__snake_case     :    int=2	,					__snake_case     :    int=1	,					__snake_case     :    Dict=1	,					__snake_case     :    Tuple=2	,					__snake_case     :    Union[str, Any]=5	,					__snake_case     :    Any=2	,					__snake_case     :    Optional[Any]=0.2_5	,					**__snake_case     :    List[Any]	,					):
										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.'''		)
																lowerCamelCase						:Any						  =     CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
													elif isinstance(__snake_case	,					__snake_case		):
																lowerCamelCase						:Dict						  =     backbone_config.get('''model_type'''		)
																lowerCamelCase						:Optional[int]						  =     CONFIG_MAPPING[backbone_model_type]
																lowerCamelCase						:Any						  =     config_class.from_dict(__snake_case		)

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







							@property
							def 				snake_case  (				self     :    List[str]		):
										return self.encoder_attention_heads







							@property
							def 				snake_case  (				self     :    int		):
										return self.d_model







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:Dict						  =     copy.deepcopy(self.__dict__		)
										if self.backbone_config is not None:
													lowerCamelCase						:Any						  =     self.backbone_config.to_dict()
										lowerCamelCase						:List[Any]						  =     self.__class__.model_type
										return output


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     version.parse('1.11'				)







							@property
							def 				snake_case  (				self     :    List[str]		):
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										        ('''pixel_mask''', {0: '''batch'''}),
										    ]		)







							@property
							def 				snake_case  (				self     :    Any		):
										return 1e-5







							@property
							def 				snake_case  (				self     :    Any		):
										return 12




import numpy
class   _lowerCAmelCase		:







							def __init__(				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:Dict						  =     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.
										lowerCamelCase						:Dict						  =     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.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    4	,					3		)

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

										# Real output values provided.
										lowerCamelCase						:Union[str, Any]						  =     output_array

										# Predicted output values by the neural network.
										# Predicted_output array initially consists of zeroes.
										lowerCamelCase						:List[str]						  =     numpy.zeros(output_array.shape		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Any						  =     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.
										lowerCamelCase						:Any						  =     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.
										lowerCamelCase						:Dict						  =     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 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, 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		)	,					)
										lowerCamelCase						:Dict						  =     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		)	,					)
										lowerCamelCase						: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 				snake_case  (				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    int	,					__snake_case     :    bool		):
										for iteration in range(1	,					iterations + 1		):
													lowerCamelCase						:Union[str, Any]						  =     self.feedforward()
													self.back_propagation()
													if give_loss:
																lowerCamelCase						:Tuple						  =     numpy.mean(numpy.square(output - self.feedforward()		)		)
																print(F"Iteration {iteration} Loss: {loss}"		)







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:int						  =     input_arr

										lowerCamelCase						:Union[str, Any]						  =     sigmoid(
										    numpy.dot(self.array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										lowerCamelCase						:Optional[Any]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										lowerCamelCase						:Optional[int]						  =     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  _lowerCamelCase					(   a_				: numpy.ndarray):
			return 1 / (1 + numpy.exp(-value))





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return (value) * (1 - (value))





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     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.
			lowerCamelCase						:int						  =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,			dtype=numpy.floataa)

			# Calling neural network class.
			lowerCamelCase						:List[Any]						  =     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()



