


import numpy as np
from matplotlib import pyplot as plt







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_=None				,							a_=None				,							a_=False     ):
							lowerCamelCase_			:							int      =       units
							lowerCamelCase_			:							Tuple      =       None
							lowerCamelCase_			:							int      =       None
							lowerCamelCase_			:							Dict      =       activation
							if learning_rate is None:
								lowerCamelCase_			:							Optional[Any]      =       0.3
							lowerCamelCase_			:							Optional[Any]      =       learning_rate
							lowerCamelCase_			:							Optional[int]      =       is_input_layer




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Union[str, Any]      =       np.asmatrix(np.random.normal(0				,							0.5				,							(self.units, back_units)     )     )
							lowerCamelCase_			:							str      =       np.asmatrix(np.random.normal(0				,							0.5				,							self.units     )     ).T
							if self.activation is None:
								lowerCamelCase_			:							Dict      =       sigmoid




						def 					_UpperCamelCase			(   self     ):
							# activation function may be sigmoid or linear
							if self.activation == sigmoid:
								lowerCamelCase_			:							int      =       np.dot(self.output				,							(1 - self.output).T     )
								lowerCamelCase_			:							Tuple      =       np.diag(np.diag(a_     )     )
							else:
								lowerCamelCase_			:							str      =       1
							return gradient_activation




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




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Tuple      =       self.cal_gradient()  # i * i 维
							lowerCamelCase_			:							List[Any]      =       np.asmatrix(np.dot(gradient.T				,							a_     )     )

							lowerCamelCase_			:							Tuple      =       np.asmatrix(self.xdata     )
							lowerCamelCase_			:							Any      =       -1
							lowerCamelCase_			:							Optional[int]      =       self.weight

							lowerCamelCase_			:							Tuple      =       np.dot(gradient.T				,							self._gradient_weight.T     )
							lowerCamelCase_			:							Optional[int]      =       gradient * self._gradient_bias
							lowerCamelCase_			:							Dict      =       np.dot(a_				,							self._gradient_x     ).T
							# upgrade: the Negative gradient direction
							lowerCamelCase_			:							str      =       self.weight - self.learn_rate * self.gradient_weight
							lowerCamelCase_			:							Optional[int]      =       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     ):
							lowerCamelCase_			:							Optional[int]      =       []
							lowerCamelCase_			:							Optional[Any]      =       []
							lowerCamelCase_			:							Optional[int]      =       plt.figure()
							lowerCamelCase_			:							List[str]      =       self.fig_loss.add_subplot(1				,							1				,							1     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							self.layers.append(a_     )




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




						def 					_UpperCamelCase			(   self     ):
							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				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							str      =       train_round
							lowerCamelCase_			:							Union[str, Any]      =       accuracy

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

							lowerCamelCase_			:							Tuple      =       np.shape(a_     )
							for _ in range(a_     ):
								lowerCamelCase_			:							str      =       0
								for row in range(x_shape[0]     ):
									lowerCamelCase_			:							Union[str, Any]      =       np.asmatrix(xdata[row, :]     ).T
									lowerCamelCase_			:							int      =       np.asmatrix(ydata[row, :]     ).T

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

									lowerCamelCase_   ,lowerCamelCase_			:							str      =       self.cal_loss(_ydata				,							_xdata     )
									lowerCamelCase_			:							int      =       all_loss + loss

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

								lowerCamelCase_			:							Union[str, Any]      =       all_loss / x_shape[0]
								self.train_mse.append(a_     )

								self.plot_loss()

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




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




						def 					_UpperCamelCase			(   self     ):
							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     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							int      =       np.random.randn(10      ,					10)
	lowerCamelCase_			:							Tuple      =       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],
	    ])
	lowerCamelCase_			:							Union[str, Any]      =       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()





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




						def 					_UpperCamelCase			(   self     ):
							self.retriever.index.init_index()




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_				,							a_				,							a_				,							a_=None     ):
							if index is not None and index.is_initialized() and len(a_     ) > 0:
								raise ValueError(
								    "When using Ray for distributed fine-tuning, "
								    "you'll need to provide the paths instead, "
								    "as the dataset and the index are loaded "
								    "separately. More info in examples/rag/use_own_knowledge_dataset.py "     )

							super().__init__(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




						def 					_UpperCamelCase			(   self     ):
							logger.info("initializing retrieval"     )

							if len(self.retrieval_workers     ) > 0:
								ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
							else:
								# Non-distributed training. Load index into this same process.
								self.index.init_index()




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if len(self.retrieval_workers     ) > 0:
								# Select a random retrieval actor.
								lowerCamelCase_			:							List[Any]      =       self.retrieval_workers[random.randint(0				,							len(self.retrieval_workers     ) - 1     )]
								lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       ray.get(
								    random_worker.retrieve.remote(a_				,							a_     )     )
							else:
								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       self._main_retrieve(a_				,							a_     )
								lowerCamelCase_			:							Any      =       self.index.get_doc_dicts(a_     )
							return retrieved_doc_embeds, doc_ids, doc_dicts




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

							return cls(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							retrieval_workers=a_				,							index=a_				,							)




						def 					_UpperCamelCase			(   self     ):
							logger.info("re-loading the new dataset with embeddings"     )
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

							# build the index object again
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,							self.question_encoder_tokenizer				,							self.generator_tokenizer				,							a_     )
							        for worker in self.retrieval_workers
							    ]     )

