




"""simple docstring"""

import numpy as np
from matplotlib import pyplot as plt

def 						lowercase_ (						_UpperCAmelCase		):




	"""simple docstring"""






	return 1 / (1 + np.exp(-1 * x		))

class     lowercase :







				def __init__(							self				:   Dict ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   str=None ,       _lowerCamelCase				:   Optional[int]=None ,       _lowerCamelCase				:   str=False    ):



					"""simple docstring"""

					A_					:			Any						=						units
					A_					:			str						=						None
					A_					:			str						=						None
					A_					:			Optional[int]						=						activation
					if learning_rate is None:
						A_					:			Dict						=						0.3
					A_					:			List[Any]						=						learning_rate
					A_					:			List[Any]						=						is_input_layer







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   str    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						np.asmatrix(np.random.normal(0 ,       0.5 ,       (self.units, back_units)    )    )
					A_					:			Tuple						=						np.asmatrix(np.random.normal(0 ,       0.5 ,       self.units    )    ).T
					if self.activation is None:
						A_					:			Optional[int]						=						sigmoid







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					if self.activation == sigmoid:
						A_					:			Tuple						=						np.dot(self.output ,       (1 - self.output).T    )
						A_					:			List[str]						=						np.diag(np.diag(_lowerCamelCase    )    )
					else:
						A_					:			Tuple						=						1
					return gradient_activation







				def    a_					(							self				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						xdata
					if self.is_input_layer:
						# input layer
						A_					:			Optional[Any]						=						xdata
						A_					:			Any						=						xdata
						return xdata
					else:
						A_					:			Optional[Any]						=						np.dot(self.weight ,       self.xdata    ) - self.bias
						A_					:			Optional[Any]						=						self.activation(self.wx_plus_b    )
						return self.output







				def    a_					(							self				:   Dict ,       _lowerCamelCase				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			int						=						self.cal_gradient()  # i * i 维
					A_					:			List[str]						=						np.asmatrix(np.dot(gradient.T ,       _lowerCamelCase    )    )

					A_					:			Dict						=						np.asmatrix(self.xdata    )
					A_					:			Dict						=						-1
					A_					:			List[str]						=						self.weight

					A_					:			List[Any]						=						np.dot(gradient.T ,       self._gradient_weight.T    )
					A_					:			Tuple						=						gradient * self._gradient_bias
					A_					:			List[Any]						=						np.dot(_lowerCamelCase ,       self._gradient_x    ).T
					# upgrade: the Negative gradient direction
					A_					:			List[Any]						=						self.weight - self.learn_rate * self.gradient_weight
					A_					:			Dict						=						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     lowercase :







				def __init__(							self				:   List[Any]    ):



					"""simple docstring"""

					A_					:			str						=						[]
					A_					:			str						=						[]
					A_					:			List[Any]						=						plt.figure()
					A_					:			Union[str, Any]						=						self.fig_loss.add_subplot(1 ,       1 ,       1    )







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



					"""simple docstring"""

					self.layers.append(_lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

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







				def    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					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    a_					(							self				:   List[str] ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   Optional[Any] ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					A_					:			List[str]						=						train_round
					A_					:			List[str]						=						accuracy

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

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

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

							A_				,	A_					:			Union[str, Any]						=						self.cal_loss(_ydata ,       _xdata    )
							A_					:			str						=						all_loss + loss

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

						A_					:			List[str]						=						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    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   Optional[int] ,       _lowerCamelCase				:   Union[str, Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						np.sum(np.power((ydata - ydata_) ,       2    )    )
					A_					:			Dict						=						2 * (ydata_ - ydata)
					# vector (shape is the same as _ydata.shape)
					return self.loss, self.loss_gradient







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					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 						lowercase_ (						):




	"""simple docstring"""






	A_					:			Dict						=						np.random.randn(10					, 10		)
	A_					:			Optional[int]						=						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_					:			List[Any]						=						BPNN()
	for i in (10, 20, 30, 2):
		model.add_layer(DenseLayer(_UpperCAmelCase		)		)
	model.build()
	model.summary()
	model.train(xdata=_UpperCAmelCase					, ydata=_UpperCAmelCase					, train_round=100					, accuracy=0.01		)


if __name__ == "__main__":
		example()











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





