


import numpy as np
from matplotlib import pyplot as plt






def 						_A						(						_lowercase				)			->				List[str]:


     """simple docstring"""


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



class 					__lowerCamelCase	:







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






            '''simple docstring'''




            __UpperCamelCase				=	units
            __UpperCamelCase				=	None
            __UpperCamelCase				=	None
            __UpperCamelCase				=	activation
            if learning_rate is None:
                 __UpperCamelCase				=	0.3
            __UpperCamelCase				=	learning_rate
            __UpperCamelCase				=	is_input_layer







       def 						snake_case_					(	self:				Tuple,A_:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	np.asmatrix(np.random.normal(0,0.5,(self.units, back_units)			)			)
            __UpperCamelCase				=	np.asmatrix(np.random.normal(0,0.5,self.units			)			).T
            if self.activation is None:
                 __UpperCamelCase				=	sigmoid







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            if self.activation == sigmoid:
                 __UpperCamelCase				=	np.dot(self.output,(1 - self.output).T			)
                 __UpperCamelCase				=	np.diag(np.diag(_A			)			)
            else:
                 __UpperCamelCase				=	1
            return gradient_activation







       def 						snake_case_					(	self:				Tuple,A_:				List[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	xdata
            if self.is_input_layer:
                 # input layer
                 __UpperCamelCase				=	xdata
                 __UpperCamelCase				=	xdata
                 return xdata
            else:
                 __UpperCamelCase				=	np.dot(self.weight,self.xdata			) - self.bias
                 __UpperCamelCase				=	self.activation(self.wx_plus_b			)
                 return self.output







       def 						snake_case_					(	self:				Union[str, Any],A_:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.cal_gradient()  # i * i 维
            __UpperCamelCase				=	np.asmatrix(np.dot(gradient.T,_A			)			)

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

            __UpperCamelCase				=	np.dot(gradient.T,self._gradient_weight.T			)
            __UpperCamelCase				=	gradient * self._gradient_bias
            __UpperCamelCase				=	np.dot(_A,self._gradient_x			).T
            # upgrade: the Negative gradient direction
            __UpperCamelCase				=	self.weight - self.learn_rate * self.gradient_weight
            __UpperCamelCase				=	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 					__lowerCamelCase	:







       def __init__(	self:				Any			):






            '''simple docstring'''




            __UpperCamelCase				=	[]
            __UpperCamelCase				=	[]
            __UpperCamelCase				=	plt.figure()
            __UpperCamelCase				=	self.fig_loss.add_subplot(1,1,1			)







       def 						snake_case_					(	self:				int,A_:				List[str]			):






            '''simple docstring'''




            self.layers.append(_A			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




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







       def 						snake_case_					(	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 						snake_case_					(	self:				Any,A_:				Tuple,A_:				Union[str, Any],A_:				Any,A_:				List[str]			):






            '''simple docstring'''




            __UpperCamelCase				=	train_round
            __UpperCamelCase				=	accuracy

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

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

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

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

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

                 __UpperCamelCase				=	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:				List[Any],A_:				List[Any],A_:				Tuple			):






            '''simple docstring'''




            __UpperCamelCase				=	np.sum(np.power((ydata - ydata_),2			)			)
            __UpperCamelCase				=	2 * (ydata_ - ydata)
            # vector (shape is the same as _ydata.shape)
            return self.loss, self.loss_gradient







       def 						snake_case_					(	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						(						)			->				Tuple:


     """simple docstring"""


     __UpperCamelCase				=	np.random.randn(10			,       10				)
     __UpperCamelCase				=	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],
         ]				)
     __UpperCamelCase				=	BPNN()
     for i in (10, 20, 30, 2):
          model.add_layer(DenseLayer(_lowercase				)				)
     model.build()
     model.summary()
     model.train(xdata=_lowercase			,       ydata=_lowercase			,       train_round=1_00			,       accuracy=0.01				)


if __name__ == "__main__":
    example()






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      SpeechaTextaTokenizer
       _lowercase    							=      False







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            super().setUp()

            __UpperCamelCase				=	'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '			)
            __UpperCamelCase				=	['he re</w> 123', 'here a 1456']
            __UpperCamelCase				=	dict(zip(A_,range(len(A_			)			)			)			)

            __UpperCamelCase				=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            __UpperCamelCase				=	tempfile.mkdtemp()
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']			)
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file']			)

            with open(self.vocab_file,'w',encoding='utf-8'			) as fp:
                 fp.write(json.dumps(A_			) + '\n'			)

            with open(self.merges_file,'w'			) as fp:
                 fp.write('\n'.join(A_			)			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __UpperCamelCase				=	list(self.get_tokenizer().get_vocab().keys()			)

            self.assertEqual(vocab_keys[0],'<s>'			)
            self.assertEqual(vocab_keys[1],'<pad>'			)
            self.assertEqual(vocab_keys[-1],'vocab'			)
            self.assertEqual(len(A_			),14			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size,14			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            # make sure @@ is correctly concatenated
            __UpperCamelCase				=	[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            __UpperCamelCase				=	tokenizer.decode(A_			)

            self.assertTrue(output_string == 'herecouple words ofthe'			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(A_			)
                 os.remove(os.path.join(A_,'merges.txt'			)			)

                 # load tokenizer without merges file should not throw an error
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(A_			)
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            self.assertIsNotNone(A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 return

            __UpperCamelCase				=	inspect.signature(self.tokenizer_class.__init__			)
            __UpperCamelCase				=	self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(A_,tokenizer.init_kwargs			)


