




import numpy as np
from matplotlib import pyplot as plt

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]						)					->			List[Any]:


      '''simple docstring'''




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






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   Optional[Any]	,  A__  :   List[str]=None	,  A__  :   Optional[Any]=None	,  A__  :   Optional[Any]=False			)				->       Optional[Any]:
           lowerCamelCase__        =				units
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				activation
           if learning_rate is None:
                 lowerCamelCase__        =				0.3
           lowerCamelCase__        =				learning_rate
           lowerCamelCase__        =				is_input_layer




     def  __snake_case   (    self  :   Any	,  A__  :   int			)				->       Dict:
           lowerCamelCase__        =				np.asmatrix(np.random.normal(0	,  0.5	,  (self.units, back_units)			)			)
           lowerCamelCase__        =				np.asmatrix(np.random.normal(0	,  0.5	,  self.units			)			).T
           if self.activation is None:
                 lowerCamelCase__        =				sigmoid




     def  __snake_case   (    self  :   Tuple			)				->       Optional[int]:
           # activation function may be sigmoid or linear
           if self.activation == sigmoid:
                 lowerCamelCase__        =				np.dot(self.output	,  (1 - self.output).T			)
                 lowerCamelCase__        =				np.diag(np.diag(A__			)			)
           else:
                 lowerCamelCase__        =				1
           return gradient_activation




     def  __snake_case   (    self  :   Tuple	,  A__  :   str			)				->       Any:
           lowerCamelCase__        =				xdata
           if self.is_input_layer:
                 # input layer
                 lowerCamelCase__        =				xdata
                 lowerCamelCase__        =				xdata
                 return xdata
           else:
                 lowerCamelCase__        =				np.dot(self.weight	,  self.xdata			) - self.bias
                 lowerCamelCase__        =				self.activation(self.wx_plus_b			)
                 return self.output




     def  __snake_case   (    self  :   List[str]	,  A__  :   Optional[Any]			)				->       int:
           lowerCamelCase__        =				self.cal_gradient()  # i * i 维
           lowerCamelCase__        =				np.asmatrix(np.dot(gradient.T	,  A__			)			)

           lowerCamelCase__        =				np.asmatrix(self.xdata			)
           lowerCamelCase__        =				-1
           lowerCamelCase__        =				self.weight

           lowerCamelCase__        =				np.dot(gradient.T	,  self._gradient_weight.T			)
           lowerCamelCase__        =				gradient * self._gradient_bias
           lowerCamelCase__        =				np.dot(A__	,  self._gradient_x			).T
           # upgrade: the Negative gradient direction
           lowerCamelCase__        =				self.weight - self.learn_rate * self.gradient_weight
           lowerCamelCase__        =				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			:
     """simple docstring"""

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




     def  __snake_case   (    self  :   List[Any]	,  A__  :   Dict			)				->       Optional[int]:
           self.layers.append(A__			)




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Optional[int]:
           for i, layer in enumerate(self.layers[:]			):
                 if i < 1:
                       lowerCamelCase__        =				True
                 else:
                       layer.initializer(self.layers[i - 1].units			)




     def  __snake_case   (    self  :   str			)				->       int:
           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  __snake_case   (    self  :   Any	,  A__  :   Dict	,  A__  :   Union[str, Any]	,  A__  :   Dict	,  A__  :   Any			)				->       Dict:
           lowerCamelCase__        =				train_round
           lowerCamelCase__        =				accuracy

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

           lowerCamelCase__        =				np.shape(A__			)
           for _ in range(A__			):
                 lowerCamelCase__        =				0
                 for row in range(x_shape[0]			):
                       lowerCamelCase__        =				np.asmatrix(xdata[row, :]			).T
                       lowerCamelCase__        =				np.asmatrix(ydata[row, :]			).T

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

                       lowerCamelCase__  ,		lowerCamelCase__        =				self.cal_loss(_ydata	,  _xdata			)
                       lowerCamelCase__        =				all_loss + loss

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

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




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Tuple:
           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 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				np.random.randn(10     ,				10						)
      lowerCamelCase__        =				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__        =				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=1_00     ,				accuracy=0.01						)


if __name__ == "__main__":
  example()





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__SCREAMING_SNAKE_CASE    :   Dict       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   str       ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE    :   int       ={
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =VOCAB_FILES_NAMES
     _snake_case:	List[str]     =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:	str     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:	str     =["input_ids", "attention_mask"]
     _snake_case:	Union[str, Any]     =LongformerTokenizer




     def __init__(    self  :   str	,  A__  :   Optional[Any]=None	,  A__  :   Dict=None	,  A__  :   Any=None	,  A__  :   Optional[Any]="replace"	,  A__  :   Tuple="<s>"	,  A__  :   Any="</s>"	,  A__  :   Dict="</s>"	,  A__  :   Any="<s>"	,  A__  :   Optional[Any]="<unk>"	,  A__  :   Tuple="<pad>"	,  A__  :   Union[str, Any]="<mask>"	,  A__  :   List[str]=False	,  A__  :   List[str]=True	,  **A__  :   Optional[int]	,  )				->       List[str]:
           super().__init__(
               A__	,  A__	,  tokenizer_file=A__	,  errors=A__	,  bos_token=A__	,  eos_token=A__	,  sep_token=A__	,  cls_token=A__	,  unk_token=A__	,  pad_token=A__	,  mask_token=A__	,  add_prefix_space=A__	,  trim_offsets=A__	,  **A__	,  )

           lowerCamelCase__        =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                 lowerCamelCase__        =				getattr(A__	,  pre_tok_state.pop('type'			)			)
                 lowerCamelCase__        =				add_prefix_space
                 lowerCamelCase__        =				pre_tok_class(**A__			)

           lowerCamelCase__        =				add_prefix_space

           lowerCamelCase__        =				'post_processor'
           lowerCamelCase__        =				getattr(self.backend_tokenizer	,  A__	,  A__			)
           if tokenizer_component_instance:
                 lowerCamelCase__        =				json.loads(tokenizer_component_instance.__getstate__()			)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       lowerCamelCase__        =				tuple(state['sep']			)
                 if "cls" in state:
                       lowerCamelCase__        =				tuple(state['cls']			)

                 lowerCamelCase__        =				False

                 if state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                       lowerCamelCase__        =				add_prefix_space
                       lowerCamelCase__        =				True

                 if state.get('trim_offsets'	,  A__			) != trim_offsets:
                       lowerCamelCase__        =				trim_offsets
                       lowerCamelCase__        =				True

                 if changes_to_apply:
                       lowerCamelCase__        =				getattr(A__	,  state.pop('type'			)			)
                       lowerCamelCase__        =				component_class(**A__			)
                       setattr(self.backend_tokenizer	,  A__	,  A__			)




     @property
     def  __snake_case   (    self  :   Tuple			)				->       str:
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'			)
                 return None
           return str(self._mask_token			)




     @mask_token.setter
     def  __snake_case   (    self  :   Dict	,  A__  :   Tuple			)				->       int:
           lowerCamelCase__        =				AddedToken(A__	,  lstrip=A__	,  rstrip=A__			) if isinstance(A__	,  A__			) else value
           lowerCamelCase__        =				value




     def  __snake_case   (    self  :   Any	,  *A__  :   Any	,  **A__  :   Any			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)
           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Tuple	,  *A__  :   str	,  **A__  :   int			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)

           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Optional[str] = None			)				->       Tuple[str]:
           lowerCamelCase__        =				self._tokenizer.model.save(A__	,  name=A__			)
           return tuple(A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[str]=None			)				->       Dict:
           lowerCamelCase__        =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   List[int]	,  A__  :   Optional[List[int]] = None			)				->       List[int]:
           lowerCamelCase__        =				[self.sep_token_id]
           lowerCamelCase__        =				[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep			) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]