


'''simple docstring'''



import numpy as np
from matplotlib import pyplot as plt



def       UpperCAmelCase__ (     UpperCAmelCase_   :    List[str]							)					->		Any:
     return 1 / (1 + np.exp(-1 * x							))





class        UpperCAmelCase_  :





    """simple docstring"""






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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					List[Any]:
         __lowerCamelCase       :     Union[str, Any]        =		np.asmatrix(np.random.normal(0      ,					0.5      ,					(self.units, back_units)     )     )
         __lowerCamelCase       :     Union[str, Any]        =		np.asmatrix(np.random.normal(0      ,					0.5      ,					self.units     )     ).T
         if self.activation is None:
              __lowerCamelCase       :     str        =		sigmoid







    def 				lowercase_    (							self     )    ->					str:







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







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







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

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

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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Optional[int]:
         self.layers.append(SCREAMING_SNAKE_CASE_     )







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







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







    def 				lowercase_    (							self     )    ->					List[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      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					List[Any]:
         __lowerCamelCase       :     int        =		train_round
         __lowerCamelCase       :     Optional[int]        =		accuracy

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

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

                   # forward propagation
                   for layer in self.layers:
                        __lowerCamelCase       :     Union[str, Any]        =		layer.forward_propagation(_xdata     )

                   __lowerCamelCase    ,	__lowerCamelCase       :     Optional[int]        =		self.cal_loss(_ydata      ,					_xdata     )
                   __lowerCamelCase       :     Optional[int]        =		all_loss + loss

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

              __lowerCamelCase       :     Optional[Any]        =		all_loss / x_shape[0]
              self.train_mse.append(SCREAMING_SNAKE_CASE_     )

              self.plot_loss()

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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Any        =		np.sum(np.power((ydata - ydata_)      ,					2     )     )
         __lowerCamelCase       :     Tuple        =		2 * (ydata_ - ydata)
         # vector (shape is the same as _ydata.shape)
         return self.loss, self.loss_gradient







    def 				lowercase_    (							self     )    ->					List[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       UpperCAmelCase__ (     )					->		Optional[Any]:
     __lowerCamelCase       :     List[Any]        =		np.random.randn(10				,   10							)
     __lowerCamelCase       :     List[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],
         ]							)
     __lowerCamelCase       :     Optional[Any]        =		BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(UpperCAmelCase_							)							)
     model.build()
     model.summary()
     model.train(xdata=UpperCAmelCase_				,   ydata=UpperCAmelCase_				,   train_round=1_00				,   accuracy=0.01							)


if __name__ == "__main__":
      example()










'''simple docstring'''



from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


A__		: Optional[int]												=  logging.get_logger(__name__)

A__		: Optional[Any]												=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: Optional[int]												=  {
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

A__		: Any												=  {
    """openai/clip-vit-base-patch32""": 77,
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Union[str, Any]												=      VOCAB_FILES_NAMES
    lowerCamelCase  : Optional[int]												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : str												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Union[str, Any]												=      ['input_ids', 'attention_mask']
    lowerCamelCase  : Optional[Any]												=      CLIPTokenizer







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|startoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					SCREAMING_SNAKE_CASE_="<|endoftext|>"      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Union[str, Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         if not isinstance(self.backend_tokenizer.pre_tokenizer      ,					pre_tokenizers.Sequence     ):
              raise ValueError(
                  'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
                  ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
                  ' to be compatible with this version.The easiest way to do so is'
                  ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
                  ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
                  ' transformers.'     )

         self._wrap_decode_method_backend_tokenizer()







    def 				lowercase_    (							self     )    ->					Optional[Any]:
         __lowerCamelCase       :     Union[str, Any]        =		self.backend_tokenizer.decode

         def new_decode_method(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     ):
              __lowerCamelCase       :     int        =		orig_decode_method(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     List[str]        =		text.replace(self.backend_tokenizer.model.end_of_word_suffix      ,					' '     ).strip()
              return text

         __lowerCamelCase       :     Union[str, Any]        =		new_decode_method







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     Optional[int]        =		[self.bos_token_id]
         __lowerCamelCase       :     Tuple        =		[self.eos_token_id]

         if token_ids_a is None:
              return bos_token + token_ids_a + eos_token
         return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id]
         __lowerCamelCase       :     Optional[Any]        =		[self.eos_token_id]

         if token_ids_a is None:
              return len(bos_token + token_ids_a + eos_token     ) * [0]
         return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     int        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






