



import numpy as np
from matplotlib import pyplot as plt



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_     ):
			return 1 / (1 + np.exp(-1 * x     ))
class  _snake_case	:




				def __init__(  self    :  Optional[Any],    __lowercase    :  str,    __lowercase    :  int=None,    __lowercase    :  List[Any]=None,    __lowercase    :  Any=False  ):
							lowercase__         = units
							lowercase__         = None
							lowercase__         = None
							lowercase__         = activation
							if learning_rate is None:
										lowercase__         = 0.3
							lowercase__         = learning_rate
							lowercase__         = is_input_layer




				def 							A__					(  self    :  int,    __lowercase    :  Dict  ):
							lowercase__         = np.asmatrix(np.random.normal(0,    0.5,    (self.units, back_units)  )  )
							lowercase__         = np.asmatrix(np.random.normal(0,    0.5,    self.units  )  ).T
							if self.activation is None:
										lowercase__         = sigmoid




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




				def 							A__					(  self    :  Dict,    __lowercase    :  Tuple  ):
							lowercase__         = xdata
							if self.is_input_layer:
										# input layer
										lowercase__         = xdata
										lowercase__         = xdata
										return xdata
							else:
										lowercase__         = np.dot(self.weight,    self.xdata  ) - self.bias
										lowercase__         = self.activation(self.wx_plus_b  )
										return self.output




				def 							A__					(  self    :  Tuple,    __lowercase    :  List[Any]  ):
							lowercase__         = self.cal_gradient()  # i * i 维
							lowercase__         = np.asmatrix(np.dot(gradient.T,    __lowercase  )  )

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

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




				def __init__(  self    :  Optional[int]  ):
							lowercase__         = []
							lowercase__         = []
							lowercase__         = plt.figure()
							lowercase__         = self.fig_loss.add_subplot(1,    1,    1  )




				def 							A__					(  self    :  Optional[int],    __lowercase    :  str  ):
							self.layers.append(__lowercase  )




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




				def 							A__					(  self    :  Tuple  ):
							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 							A__					(  self    :  List[Any],    __lowercase    :  List[Any],    __lowercase    :  Optional[int],    __lowercase    :  Any,    __lowercase    :  Union[str, Any]  ):
							lowercase__         = train_round
							lowercase__         = accuracy

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

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

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

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

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

										lowercase__         = all_loss / x_shape[0]
										self.train_mse.append(__lowercase  )

										self.plot_loss()

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




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




				def 							A__					(  self    :  Dict  ):
							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       (   ):
			lowercase__         = np.random.randn(10  ,     10     )
			lowercase__         = 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],
			    ]     )
			lowercase__         = BPNN()
			for i in (10, 20, 30, 2):
						model.add_layer(DenseLayer(SCREAMING_SNAKE_CASE__     )     )
			model.build()
			model.summary()
			model.train(xdata=SCREAMING_SNAKE_CASE__  ,     ydata=SCREAMING_SNAKE_CASE__  ,     train_round=100  ,     accuracy=0.01     )


if __name__ == "__main__":
							example()








import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_           =	get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class        A_					(       __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   ErnieMTokenizer
      __snake_case					       =   False
      __snake_case					       =   True
      __snake_case					       =   False
      __snake_case					       =   False




      def      _snake_case	(				self:       Optional[int]				):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __lowerCamelCase		:							List[str]							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)
       tokenizer.save_pretrained(self.tmpdirname				)




      def      _snake_case	(				self:       Dict						,      a:       List[Any]				):
       __lowerCamelCase		:							Optional[Any]							=						'this is a test'
       __lowerCamelCase		:							Optional[int]							=						'this is a test'
       return input_text, output_text




      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							List[str]							=						'<pad>'
       __lowerCamelCase		:							Union[str, Any]							=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a				)						,      a				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a				)						,      a				)




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]						,      '<pad>'				)
       self.assertEqual(vocab_keys[1]						,      '<unk>'				)
       self.assertEqual(vocab_keys[-1]						,      '▁eloquent'				)
       self.assertEqual(len(a				)						,      3_0000				)




      def      _snake_case	(				self:       Tuple				):
       self.assertEqual(self.get_tokenizer().vocab_size						,      3_0000				)




      def      _snake_case	(				self:       int				):
       if not self.test_rust_tokenizer:
        return

       __lowerCamelCase		:							Any							=						self.get_tokenizer()
       __lowerCamelCase		:							Optional[Any]							=						self.get_rust_tokenizer()

       __lowerCamelCase		:							Optional[int]							=						'I was born in 92000, and this is falsé.'

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize(a				)
       __lowerCamelCase		:							Dict							=						rust_tokenizer.tokenize(a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							List[str]							=						tokenizer.encode(a						,      add_special_tokens=a				)
       __lowerCamelCase		:							List[Any]							=						rust_tokenizer.encode(a						,      add_special_tokens=a				)
       self.assertListEqual(a						,      a				)

       __lowerCamelCase		:							str							=						self.get_rust_tokenizer()
       __lowerCamelCase		:							str							=						tokenizer.encode(a				)
       __lowerCamelCase		:							List[str]							=						rust_tokenizer.encode(a				)
       self.assertListEqual(a						,      a				)




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						ErnieMTokenizer(a						,      do_lower_case=a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							List[str]							=						tokenizer.tokenize('This is a test'				)
       self.assertListEqual(a						,      ['▁this', '▁is', '▁a', '▁test']				)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)						,      [48, 25, 21, 1289]				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']				)
       __lowerCamelCase		:							Any							=						tokenizer.convert_tokens_to_ids(a				)
       self.assertListEqual(a						,      [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]				)

       __lowerCamelCase		:							int							=						tokenizer.convert_ids_to_tokens(a				)
       self.assertListEqual(
           a						,      ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']						,      )




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Tuple							=						ErnieMTokenizer(a						,      unk_token='<unk>'						,      pad_token='<pad>'				)

       __lowerCamelCase		:							Tuple							=						tokenizer.encode('sequence builders'				)
       __lowerCamelCase		:							List[Any]							=						tokenizer.encode('multi-sequence build'				)

       __lowerCamelCase		:							Optional[Any]							=						tokenizer.build_inputs_with_special_tokens(a				)
       __lowerCamelCase		:							Tuple							=						tokenizer.build_inputs_with_special_tokens(a						,      a				)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]




      @slow
      def      _snake_case	(				self:       List[Any]				):
       # fmt: off
       __lowerCamelCase		:							int							=						{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a						,      model_name='susnato/ernie-m-base_pytorch'						,      sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ]						,      )

