



import numpy as np
from matplotlib import pyplot as plt

def 			a		( _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





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

class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					Optional[Any]			,			a_				:					Any			,			a_				:					int=None			,			a_				:					int=None			,			a_				:					Optional[Any]=False					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					Optional[int]			,			a_				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      np.asmatrix(np.random.normal(0			,			0.5			,			(self.units, back_units)					)					)
					__UpperCAmelCase       :	List[Any]						=      np.asmatrix(np.random.normal(0			,			0.5			,			self.units					)					).T
					if self.activation is None:
							__UpperCAmelCase       :	Dict						=      sigmoid




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					if self.activation == sigmoid:
							__UpperCAmelCase       :	int						=      np.dot(self.output			,			(1 - self.output).T					)
							__UpperCAmelCase       :	Optional[int]						=      np.diag(np.diag(a_					)					)
					else:
							__UpperCAmelCase       :	Tuple						=      1
					return gradient_activation




			def 					snake_case__	(  self				:					Any			,			a_				:					Dict					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      xdata
					if self.is_input_layer:
							# input layer
							__UpperCAmelCase       :	str						=      xdata
							__UpperCAmelCase       :	Tuple						=      xdata
							return xdata
					else:
							__UpperCAmelCase       :	Any						=      np.dot(self.weight			,			self.xdata					) - self.bias
							__UpperCAmelCase       :	Any						=      self.activation(self.wx_plus_b					)
							return self.output




			def 					snake_case__	(  self				:					List[Any]			,			a_				:					List[Any]					):
					'''simple docstring'''





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

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

					__UpperCAmelCase       :	Optional[int]						=      np.dot(gradient.T			,			self._gradient_weight.T					)
					__UpperCAmelCase       :	Optional[Any]						=      gradient * self._gradient_bias
					__UpperCAmelCase       :	int						=      np.dot(a_			,			self._gradient_x					).T
					# upgrade: the Negative gradient direction
					__UpperCAmelCase       :	Union[str, Any]						=      self.weight - self.learn_rate * self.gradient_weight
					__UpperCAmelCase       :	Optional[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[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      []
					__UpperCAmelCase       :	int						=      []
					__UpperCAmelCase       :	str						=      plt.figure()
					__UpperCAmelCase       :	str						=      self.fig_loss.add_subplot(1			,			1			,			1					)




			def 					snake_case__	(  self				:					Optional[int]			,			a_				:					str					):
					'''simple docstring'''





					self.layers.append(a_					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					Dict					):
					'''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				:					Tuple			,			a_				:					str			,			a_				:					Optional[int]			,			a_				:					Optional[Any]			,			a_				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	Union[str, Any]						=      train_round
					__UpperCAmelCase       :	Optional[int]						=      accuracy

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

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

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

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

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

							__UpperCAmelCase       :	Any						=      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				:					Tuple			,			a_				:					int			,			a_				:					Dict					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					Tuple					):
					'''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'''





		__UpperCAmelCase       :	Union[str, Any]						=      np.random.randn(10					,  10		)
		__UpperCAmelCase       :	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],
		    ]		)
		__UpperCAmelCase       :	Union[str, 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()










import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						UpperCAmelCase__     (     __UpperCamelCase		,unittest.TestCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			HerbertTokenizer
			UpperCamelCase     		=			HerbertTokenizerFast
			UpperCamelCase     		=			True




			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					super().setUp()

					# Use a simpler test file without japanese/chinese characters
					with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'			,			encoding='''utf-8'''					) as f_data:
							__UpperCAmelCase       :	List[str]						=      f_data.read().replace('''\n\n'''			,			'''\n'''					).strip()

					__UpperCAmelCase       :	Dict						=      [
					    '''<s>''',
					    '''</s>''',
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''w</w>''',
					    '''r</w>''',
					    '''t</w>''',
					    '''lo''',
					    '''low''',
					    '''er</w>''',
					    '''low</w>''',
					    '''lowest</w>''',
					    '''newer</w>''',
					    '''wider</w>''',
					    ''',</w>''',
					    '''<unk>''',
					]
					__UpperCAmelCase       :	Tuple						=      dict(zip(a_			,			range(len(a_					)					)					)					)
					__UpperCAmelCase       :	Optional[int]						=      ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

					__UpperCAmelCase       :	str						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']					)
					__UpperCAmelCase       :	List[str]						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']					)
					with open(self.vocab_file			,			'''w'''					) as fp:
							fp.write(json.dumps(a_					)					)
					with open(self.merges_file			,			'''w'''					) as fp:
							fp.write('''\n'''.join(a_					)					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      '''lower newer'''
					return input_text, output_text




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.tokenizer_class(vocab_file=self.vocab_file			,			merges_file=self.merges_file					)

					__UpperCAmelCase       :	Optional[int]						=      '''lower'''
					__UpperCAmelCase       :	Any						=      ['''low''', '''er</w>''']
					__UpperCAmelCase       :	Any						=      tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	int						=      tokens + ['''<unk>''']
					__UpperCAmelCase       :	Optional[Any]						=      [16, 17, 23]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					if not self.test_rust_tokenizer:
							return

					__UpperCAmelCase       :	str						=      self.get_tokenizer()
					__UpperCAmelCase       :	Optional[int]						=      self.get_rust_tokenizer()

					__UpperCAmelCase       :	Tuple						=      '''lower,newer'''

					__UpperCAmelCase       :	Optional[int]						=      tokenizer.tokenize(a_					)
					__UpperCAmelCase       :	Tuple						=      rust_tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Optional[Any]						=      tokenizer.encode(a_			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	Union[str, Any]						=      rust_tokenizer.encode(a_			,			add_special_tokens=a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Tuple						=      self.get_rust_tokenizer()
					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.encode(a_					)
					__UpperCAmelCase       :	str						=      rust_tokenizer.encode(a_					)
					self.assertListEqual(a_			,			a_					)




			@slow
			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''					)

					__UpperCAmelCase       :	Tuple						=      tokenizer.encode('''konstruowanie sekwencji'''			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	int						=      tokenizer.encode('''konstruowanie wielu sekwencji'''			,			add_special_tokens=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.build_inputs_with_special_tokens(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.build_inputs_with_special_tokens(a_			,			a_					)

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + text_a + [2]





