


'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt







def 			a_							(  UpperCamelCase_   ):
							return 1 / (1 + np.exp(-1 * x   ))






class 				__lowerCAmelCase      :

						"""simple docstring"""






						def __init__(     self       :			Dict   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Dict=None   ,     lowerCAmelCase       :			Union[str, Any]=False  ):
													A_ 						=		units
													A_ 						=		None
													A_ 						=		None
													A_ 						=		activation
													if learning_rate is None:
																				A_ 						=		0.3
													A_ 						=		learning_rate
													A_ 						=		is_input_layer






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			List[str]  ):
													A_ 						=		np.asmatrix(np.random.normal(0   ,     0.5   ,     (self.units, back_units)  )  )
													A_ 						=		np.asmatrix(np.random.normal(0   ,     0.5   ,     self.units  )  ).T
													if self.activation is None:
																				A_ 						=		sigmoid






						def 							_UpperCAmelCase					(     self       :			Dict  ):







													if self.activation == sigmoid:
																				A_ 						=		np.dot(self.output   ,     (1 - self.output).T  )
																				A_ 						=		np.diag(np.diag(lowerCAmelCase  )  )
													else:
																				A_ 						=		1
													return gradient_activation






						def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			str  ):
													A_ 						=		xdata
													if self.is_input_layer:
																				# input layer
																				A_ 						=		xdata
																				A_ 						=		xdata
																				return xdata
													else:
																				A_ 						=		np.dot(self.weight   ,     self.xdata  ) - self.bias
																				A_ 						=		self.activation(self.wx_plus_b  )
																				return self.output






						def 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			Dict  ):
													A_ 						=		self.cal_gradient()  # i * i 维
													A_ 						=		np.asmatrix(np.dot(gradient.T   ,     lowerCAmelCase  )  )

													A_ 						=		np.asmatrix(self.xdata  )
													A_ 						=		-1
													A_ 						=		self.weight

													A_ 						=		np.dot(gradient.T   ,     self._gradient_weight.T  )
													A_ 						=		gradient * self._gradient_bias
													A_ 						=		np.dot(lowerCAmelCase   ,     self._gradient_x  ).T
													# upgrade: the Negative gradient direction
													A_ 						=		self.weight - self.learn_rate * self.gradient_weight
													A_ 						=		self.bias - self.learn_rate * self.gradient_bias.T
													# updates the weights and bias according to learning rate (0.3 if undefined)
													return self.gradient






class 				__lowerCAmelCase      :

						"""simple docstring"""






						def __init__(     self       :			Tuple  ):
													A_ 						=		[]
													A_ 						=		[]
													A_ 						=		plt.figure()
													A_ 						=		self.fig_loss.add_subplot(1   ,     1   ,     1  )






						def 							_UpperCAmelCase					(     self       :			str   ,     lowerCAmelCase       :			Optional[Any]  ):
													self.layers.append(lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			int  ):







													for i, layer in enumerate(self.layers[:]  ):
																				if i < 1:
																											A_ 						=		True
																				else:
																											layer.initializer(self.layers[i - 1].units  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):







													for i, layer in enumerate(self.layers[:]  ):
																				print(F"------- layer {i} -------"  )
																				print("weight.shape "   ,     np.shape(layer.weight  )  )
																				print("bias.shape "   ,     np.shape(layer.bias  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			List[str]  ):
													A_ 						=		train_round
													A_ 						=		accuracy

													self.ax_loss.hlines(self.accuracy   ,     0   ,     self.train_round * 1.1  )

													A_ 						=		np.shape(lowerCAmelCase  )
													for _ in range(lowerCAmelCase  ):
																				A_ 						=		0
																				for row in range(x_shape[0]  ):
																											A_ 						=		np.asmatrix(xdata[row, :]  ).T
																											A_ 						=		np.asmatrix(ydata[row, :]  ).T

																											# forward propagation
																											for layer in self.layers:
																																		A_ 						=		layer.forward_propagation(_xdata  )

																											A_						,				A_ 						=		self.cal_loss(_ydata   ,     _xdata  )
																											A_ 						=		all_loss + loss

																											# back propagation: the input_layer does not upgrade
																											for layer in self.layers[:0:-1]:
																																		A_ 						=		layer.back_propagation(lowerCAmelCase  )

																				A_ 						=		all_loss / x_shape[0]
																				self.train_mse.append(lowerCAmelCase  )

																				self.plot_loss()

																				if mse < self.accuracy:
																											print("----达到精度----"  )
																											return mse
													return None






						def 							_UpperCAmelCase					(     self       :			Dict   ,     lowerCAmelCase       :			Optional[int]   ,     lowerCAmelCase       :			Any  ):
													A_ 						=		np.sum(np.power((ydata - ydata_)   ,     2  )  )
													A_ 						=		2 * (ydata_ - ydata)
													# vector (shape is the same as _ydata.shape)
													return self.loss, self.loss_gradient






						def 							_UpperCAmelCase					(     self       :			List[str]  ):







													if self.ax_loss.lines:
																				self.ax_loss.lines.remove(self.ax_loss.lines[0]  )
													self.ax_loss.plot(self.train_mse   ,     "r-"  )
													plt.ion()
													plt.xlabel("step"  )
													plt.ylabel("loss"  )
													plt.show()
													plt.pause(0.1  )







def 			a_							(  ):
							A_ 						=		np.random.randn(1_0	,     1_0   )
							A_ 						=		np.asarray(
							    [
							        [0.8, 0.4],
							        [0.4, 0.3],
							        [0.34, 0.45],
							        [0.67, 0.32],
							        [0.88, 0.67],
							        [0.78, 0.77],
							        [0.55, 0.66],
							        [0.55, 0.43],
							        [0.54, 0.1],
							        [0.1, 0.5],
							    ]   )
							A_ 						=		BPNN()
							for i in (1_0, 2_0, 3_0, 2):
														model.add_layer(DenseLayer(__snake_case   )   )
							model.build()
							model.summary()
							model.train(xdata=__snake_case	,     ydata=__snake_case	,     train_round=1_0_0	,     accuracy=0.01   )


if __name__ == "__main__":
							example()



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													if text is None and images is None:
																				raise ValueError("You have to specify either text or images. Both cannot be none."  )

													if text is not None:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





