
import numpy as np
from matplotlib import pyplot as plt


def     a__			(    snake_case    ):





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





class   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    Union[str, Any]      ,		_A    :    str      ,		_A    :    str=None      ,		_A    :    int=None      ,		_A    :    Dict=False				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    Optional[int]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       if self.activation == sigmoid:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						np.dot(self.output      ,		(1 - self.output).T				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.diag(np.diag(_A				)				)
       else:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						1
       return gradient_activation



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    List[Any]				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    Optional[int]				):







       """simple docstring"""




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

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

       __SCREAMING_SNAKE_CASE     :					List[str]			=						np.dot(gradient.T      ,		self._gradient_weight.T				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						gradient * self._gradient_bias
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						np.dot(_A      ,		self._gradient_x				).T
       # upgrade: the Negative gradient direction
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.weight - self.learn_rate * self.gradient_weight
       __SCREAMING_SNAKE_CASE     :					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    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[]
       __SCREAMING_SNAKE_CASE     :					Dict			=						[]
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						plt.figure()
       __SCREAMING_SNAKE_CASE     :					int			=						self.fig_loss.add_subplot(1      ,		1      ,		1				)



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[str]				):







       """simple docstring"""




       self.layers.append(_A				)



     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """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 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Tuple      ,		_A    :    Union[str, Any]      ,		_A    :    Any      ,		_A    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						train_round
       __SCREAMING_SNAKE_CASE     :					int			=						accuracy

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

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

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

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

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

         __SCREAMING_SNAKE_CASE     :					int			=						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    :    List[Any]      ,		_A    :    List[Any]      ,		_A    :    Tuple				):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """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__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Dict			=						np.random.randn(10   ,	10    )
  __SCREAMING_SNAKE_CASE     :					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],
      ]    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						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()


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



     def __init__(							self    :    Optional[Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''				)
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''				)

       super().__init__(_A      ,		_A				)



     def __call__(							self    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if self.image_processor.apply_ocr and (boxes is not None):
         raise ValueError(
             '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

       if self.image_processor.apply_ocr and (word_labels is not None):
         raise ValueError(
             '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
         raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''				)

       # first, apply the image processor
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.image_processor(images=_A      ,		return_tensors=_A				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
         if isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						self.tokenizer(
           text=text if text is not None else features['''words''']      ,		text_pair=text_pair if text_pair is not None else None      ,		boxes=boxes if boxes is not None else features['''boxes''']      ,		word_labels=_A      ,		add_special_tokens=_A      ,		padding=_A      ,		truncation=_A      ,		max_length=_A      ,		stride=_A      ,		pad_to_multiple_of=_A      ,		return_token_type_ids=_A      ,		return_attention_mask=_A      ,		return_overflowing_tokens=_A      ,		return_special_tokens_mask=_A      ,		return_offsets_mapping=_A      ,		return_length=_A      ,		verbose=_A      ,		return_tensors=_A      ,		**_A      ,		)

       # add pixel values
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       for sample_idx in overflow_to_sample_mapping:
         images_with_overflow.append(images[sample_idx]				)

       if len(_A				) != len(_A				):
         raise ValueError(
             '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
             F''' {len(_A				)} and {len(_A				)}'''				)

       return images_with_overflow



     def 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       return self.tokenizer.batch_decode(*_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




       return self.tokenizer.decode(*_A      ,		**_A				)



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """simple docstring"""




       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      ,		_A      ,		)
       return self.image_processor
