'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]  )   ->				List[Any]:







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



class   UpperCamelCase					:







    """simple docstring"""





    def __init__(					self     :						Dict							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						List[str]=None							,							UpperCAmelCase_     :						str=None							,							UpperCAmelCase_     :						str=False):


         """simple docstring"""







         a					:							str							= units
         a					:							Tuple							= None
         a					:							Tuple							= None
         a					:							List[Any]							= activation
         if learning_rate is None:
              a					:							List[str]							= 0.3
         a					:							Any							= learning_rate
         a					:							Optional[Any]							= is_input_layer





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						Any):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						int							,							UpperCAmelCase_     :						List[Any]):


         """simple docstring"""







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







    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]							,							UpperCAmelCase_     :						Any):


         """simple docstring"""







         a					:							Union[str, Any]							= self.cal_gradient()  # i * i 维
         a					:							List[Any]							= np.asmatrix(np.dot(gradient.T							,							UpperCAmelCase_))

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

         a					:							Union[str, Any]							= np.dot(gradient.T							,							self._gradient_weight.T)
         a					:							Optional[int]							= gradient * self._gradient_bias
         a					:							str							= np.dot(UpperCAmelCase_							,							self._gradient_x).T
         # upgrade: the Negative gradient direction
         a					:							List[Any]							= self.weight - self.learn_rate * self.gradient_weight
         a					:							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   UpperCamelCase					:







    """simple docstring"""





    def __init__(					self     :						Optional[Any]):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						Optional[Any]):


         """simple docstring"""







         self.layers.append(UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """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    SCREAMING_SNAKE_CASE_     (					self     :						Dict							,							UpperCAmelCase_     :						List[str]							,							UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						int):


         """simple docstring"""







         a					:							List[str]							= train_round
         a					:							Union[str, Any]							= accuracy

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

         a					:							int							= np.shape(UpperCAmelCase_)
         for _ in range(UpperCAmelCase_):
              a					:							str							= 0
              for row in range(x_shape[0]):
                   a					:							Dict							= np.asmatrix(xdata[row, :]).T
                   a					:							Optional[int]							= np.asmatrix(ydata[row, :]).T

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

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

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

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

              self.plot_loss()

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





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						Union[str, Any]):


         """simple docstring"""







         a					:							Any							= 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    SCREAMING_SNAKE_CASE_     (					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 SCREAMING_SNAKE_CASE__  (				)   ->				str:







     """simple docstring"""
     a					:							Dict							= np.random.randn(10				,	10  )
     a					:							Union[str, Any]							= 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					:							int							= BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(snake_case  )  )
     model.build()
     model.summary()
     model.train(xdata=snake_case				,	ydata=snake_case				,	train_round=100				,	accuracy=0.01  )


if __name__ == "__main__":
      example()


'''simple docstring'''





import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







     """simple docstring"""
     a							,			a					:							int							= [], []
     for title, text in zip(documents['title']				,	documents['text']  ):
          if text is not None:
               for passage in split_text(snake_case  ):
                    titles.append(title if title is not None else ''  )
                    texts.append(snake_case  )
     return {"title": titles, "text": texts}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

     def embed(
         snake_case       :				dict				,	snake_case       :				DPRContextEncoder				,	snake_case       :				DPRContextEncoderTokenizerFast				,	snake_case       :				Any  ) -> dict:
          a					:							Tuple							= ctx_tokenizer(
              documents['title']				,	documents['text']				,	truncation=snake_case				,	padding='longest'				,	return_tensors='pt'  )['input_ids']
          a					:							Any							= ctx_encoder(input_ids.to(device=snake_case  )				,	return_dict=snake_case  ).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     a					:							Union[str, Any]							= Features(
         {'text': Value('string'  ), 'title': Value('string'  ), 'embeddings': Sequence(Value('float32'  )  )}  )  # optional, save as float32 instead of float64 to save space

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

     for shard_address in glob(str(snake_case  ) + '/*/'  ):
          data_shard_list.append(load_from_disk(snake_case  )  )

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

