"""simple docstring"""



import numpy as np
from matplotlib import pyplot as plt

def    _A(		lowerCAmelCase       ):
							return 1 / (1 + np.exp(-1 * x       ))
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=False       ):







										'''simple docstring'''


										A__					:    str														= units
										A__					:    Union[str, Any]														= None
										A__					:    Optional[Any]														= None
										A__					:    int														= activation
										if learning_rate is None:
																	A__					:    Union[str, Any]														= 0.3
										A__					:    int														= learning_rate
										A__					:    str														= is_input_layer





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if self.activation == sigmoid:
																	A__					:    Optional[int]														= np.dot(self.output      ,			(1 - self.output).T       )
																	A__					:    List[Any]														= np.diag(np.diag(snake_case_       )       )
										else:
																	A__					:    int														= 1
										return gradient_activation





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.cal_gradient()  # i * i 维
										A__					:    Dict														= np.asmatrix(np.dot(gradient.T      ,			snake_case_       )       )

										A__					:    Union[str, Any]														= np.asmatrix(self.xdata       )
										A__					:    Dict														= -1
										A__					:    str														= self.weight

										A__					:    Union[str, Any]														= np.dot(gradient.T      ,			self._gradient_weight.T       )
										A__					:    Any														= gradient * self._gradient_bias
										A__					:    Optional[int]														= np.dot(snake_case_      ,			self._gradient_x       ).T
										# upgrade: the Negative gradient direction
										A__					:    str														= 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 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    int														= []
										A__					:    List[str]														= plt.figure()
										A__					:    int														= self.fig_loss.add_subplot(1      ,			1      ,			1       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										self.layers.append(snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= train_round
										A__					:    str														= accuracy

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

										A__					:    Optional[Any]														= np.shape(snake_case_       )
										for _ in range(snake_case_       ):
																	A__					:    Optional[Any]														= 0
																	for row in range(x_shape[0]       ):
																								A__					:    Tuple														= np.asmatrix(xdata[row, :]       ).T
																								A__					:    int														= np.asmatrix(ydata[row, :]       ).T

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

																								A__					:    Optional[Any]														= self.cal_loss(_ydata      ,			_xdata       )
																								A__					:    Optional[int]														= all_loss + loss

																								# back propagation: the input_layer does not upgrade
																								for layer in self.layers[:0:-1]:
																															A__					:    Union[str, Any]														= layer.back_propagation(snake_case_       )

																	A__					:    Any														= all_loss / x_shape[0]
																	self.train_mse.append(snake_case_       )

																	self.plot_loss()

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= np.sum(np.power((ydata - ydata_)      ,			2       )       )
										A__					:    int														= 2 * (ydata_ - ydata)
										# vector (shape is the same as _ydata.shape)
										return self.loss, self.loss_gradient





			def 		lowerCamelCase				( self       ):







										'''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    _A(		):
							A__					:    Union[str, Any]														= np.random.randn(10 ,     10       )
							A__					:    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__					:    Optional[int]														= BPNN()
							for i in (10, 20, 30, 2):
														model.add_layer(DenseLayer(lowerCAmelCase       )       )
							model.build()
							model.summary()
							model.train(xdata=lowerCAmelCase ,     ydata=lowerCAmelCase ,     train_round=100 ,     accuracy=0.01       )


if __name__ == "__main__":
				example()






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





