



"""simple docstring"""




import numpy as np
from matplotlib import pyplot as plt


def   lowercase__     (			_UpperCAmelCase				)     ->			Union[str, Any]:






						'''simple docstring'''


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


class   a__		:


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


			def        lowercase				(							self      :							Any,     lowerCAmelCase      :							str			)    ->       List[str]:
									lowercase  :      Optional[Any]					=   np.asmatrix(np.random.normal(0,     0.5,     (self.units, back_units)			)			)
									lowercase  :      Optional[int]					=   np.asmatrix(np.random.normal(0,     0.5,     self.units			)			).T
									if self.activation is None:
															lowercase  :      str					=   sigmoid


			def        lowercase				(							self      :							int			)    ->       Optional[int]:






									if self.activation == sigmoid:
															lowercase  :      Dict					=   np.dot(self.output,     (1 - self.output).T			)
															lowercase  :      Tuple					=   np.diag(np.diag(lowercase_			)			)
									else:
															lowercase  :      Tuple					=   1
									return gradient_activation


			def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							Any			)    ->       List[Any]:
									lowercase  :      Optional[Any]					=   xdata
									if self.is_input_layer:
															# input layer
															lowercase  :      Optional[Any]					=   xdata
															lowercase  :      Dict					=   xdata
															return xdata
									else:
															lowercase  :      Optional[Any]					=   np.dot(self.weight,     self.xdata			) - self.bias
															lowercase  :      Any					=   self.activation(self.wx_plus_b			)
															return self.output






			def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       int:
									lowercase  :      Tuple					=   self.cal_gradient()  # i * i 维
									lowercase  :      Union[str, Any]					=   np.asmatrix(np.dot(gradient.T,     lowercase_			)			)

									lowercase  :      Optional[int]					=   np.asmatrix(self.xdata			)
									lowercase  :      Optional[int]					=   -1
									lowercase  :      List[str]					=   self.weight

									lowercase  :      Tuple					=   np.dot(gradient.T,     self._gradient_weight.T			)
									lowercase  :      int					=   gradient * self._gradient_bias
									lowercase  :      Union[str, Any]					=   np.dot(lowercase_,     self._gradient_x			).T
									# upgrade: the Negative gradient direction
									lowercase  :      Any					=   self.weight - self.learn_rate * self.gradient_weight
									lowercase  :      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   a__		:


			def __init__(							self      :							Any			)    ->       List[Any]:
									lowercase  :      str					=   []
									lowercase  :      Any					=   []
									lowercase  :      str					=   plt.figure()
									lowercase  :      Any					=   self.fig_loss.add_subplot(1,     1,     1			)


			def        lowercase				(							self      :							int,     lowerCAmelCase      :							Dict			)    ->       str:
									self.layers.append(lowercase_			)


			def        lowercase				(							self      :							str			)    ->       Optional[Any]:






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


			def        lowercase				(							self      :							Any			)    ->       Optional[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        lowercase				(							self      :							str,     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							List[str]			)    ->       List[Any]:
									lowercase  :      Union[str, Any]					=   train_round
									lowercase  :      List[str]					=   accuracy

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

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

																					# forward propagation
																					for layer in self.layers:
																											lowercase  :      Optional[int]					=   layer.forward_propagation(_xdata			)

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

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

															lowercase  :      Dict					=   all_loss / x_shape[0]
															self.train_mse.append(lowercase_			)

															self.plot_loss()

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


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






			def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:






									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__     (			)     ->			Optional[Any]:






						'''simple docstring'''


						lowercase  :      Union[str, Any]					=   np.random.randn(10	,      10				)
						lowercase  :      Optional[Any]					=   np.asarray(
						    [
						        [0.8, 0.4],
						        [0.4, 0.3],
						        [0.3_4, 0.4_5],
						        [0.6_7, 0.3_2],
						        [0.8_8, 0.6_7],
						        [0.7_8, 0.7_7],
						        [0.5_5, 0.6_6],
						        [0.5_5, 0.4_3],
						        [0.5_4, 0.1],
						        [0.1, 0.5],
						    ]				)
						lowercase  :      Union[str, Any]					=   BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(_A				)				)
						model.build()
						model.summary()
						model.train(xdata=_A	,      ydata=_A	,      train_round=1_00	,      accuracy=0.0_1				)


if __name__ == "__main__":
					example()





"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

         if audio is None and text is None:
               raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
