code: 
  
  
  
  
  
  
  """simple docstring"""
  from __future__ import annotations
  
  import unittest
  
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
   from transformers.models.albert.modeling_tf_albert import (
       TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFAlbertForMaskedLM,
       TFAlbertForMultipleChoice,
       TFAlbertForPreTraining,
       TFAlbertForQuestionAnswering,
       TFAlbertForSequenceClassification,
       TFAlbertForTokenClassification,
       TFAlbertModel,
   )
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  str							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Dict=1_3							,						UpperCamelCase_   :  Dict=7							,						UpperCamelCase_   :  int=True							,						UpperCamelCase_   :  List[Any]=True							,						UpperCamelCase_   :  int=True							,						UpperCamelCase_   :  Tuple=True							,						UpperCamelCase_   :  Optional[Any]=9_9							,						UpperCamelCase_   :  Optional[Any]=1_6							,						UpperCamelCase_   :  Optional[int]=3_2							,						UpperCamelCase_   :  int=2							,						UpperCamelCase_   :  Tuple=4							,						UpperCamelCase_   :  Dict=3_7							,						UpperCamelCase_   :  List[Any]="gelu"							,						UpperCamelCase_   :  Any=0.1							,						UpperCamelCase_   :  List[Any]=0.1							,						UpperCamelCase_   :  Dict=5_1_2							,						UpperCamelCase_   :  Any=1_6							,						UpperCamelCase_   :  Tuple=2							,						UpperCamelCase_   :  Tuple=0.02							,						UpperCamelCase_   :  Optional[Any]=3							,						UpperCamelCase_   :  List[str]=4							,						UpperCamelCase_   :  Optional[Any]=None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						parent
             __magic_name__										=						1_3
             __magic_name__										=						7
             __magic_name__										=						True
             __magic_name__										=						True
             __magic_name__										=						True
             __magic_name__										=						True
             __magic_name__										=						9_9
             __magic_name__										=						1_6
             __magic_name__										=						3_2
             __magic_name__										=						2
             __magic_name__										=						4
             __magic_name__										=						3_7
             __magic_name__										=						'gelu'
             __magic_name__										=						0.1
             __magic_name__										=						0.1
             __magic_name__										=						5_1_2
             __magic_name__										=						1_6
             __magic_name__										=						2
             __magic_name__										=						0.02
             __magic_name__										=						3
             __magic_name__										=						4
             __magic_name__										=						None
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size   )
  
             __magic_name__										=						None
             if self.use_input_mask:
                    __magic_name__										=						random_attention_mask([self.batch_size, self.seq_length]   )
  
             __magic_name__										=						None
             if self.use_token_type_ids:
                    __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size   )
  
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						None
             if self.use_labels:
                    __magic_name__										=						ids_tensor([self.batch_size]							,						self.type_sequence_label_size   )
                    __magic_name__										=						ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels   )
                    __magic_name__										=						ids_tensor([self.batch_size]							,						self.num_choices   )
  
             __magic_name__										=						AlbertConfig(
                 vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						embedding_size=self.embedding_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						initializer_range=self.initializer_range							,						)
  
             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						TFAlbertModel(config=UpperCamelCase_   )
             # inputs = {'input_ids': input_ids,
             #           'attention_mask': input_mask,
             #           'token_type_ids': token_type_ids}
             # sequence_output, pooled_output = model(**inputs)
             __magic_name__										=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             __magic_name__										=						model(UpperCamelCase_   )
  
             __magic_name__										=						[input_ids, input_mask]
             __magic_name__										=						model(UpperCamelCase_   )
  
             __magic_name__										=						model(UpperCamelCase_   )
  
             self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)   )
             self.parent.assertEqual(result.pooler_output.shape							,						(self.batch_size, self.hidden_size)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_labels
             __magic_name__										=						TFAlbertForPreTraining(config=UpperCamelCase_   )
             __magic_name__										=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertEqual(result.prediction_logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )
             self.parent.assertEqual(result.sop_logits.shape							,						(self.batch_size, self.num_labels)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						TFAlbertForMaskedLM(config=UpperCamelCase_   )
             __magic_name__										=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_labels
             __magic_name__										=						TFAlbertForSequenceClassification(config=UpperCamelCase_   )
             __magic_name__										=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						TFAlbertForQuestionAnswering(config=UpperCamelCase_   )
             __magic_name__										=						{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length)   )
             self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length)   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_choices
             __magic_name__										=						TFAlbertForMultipleChoice(config=UpperCamelCase_   )
             __magic_name__										=						tf.tile(tf.expand_dims(UpperCamelCase_							,						1   )							,						(1, self.num_choices, 1)   )
             __magic_name__										=						tf.tile(tf.expand_dims(UpperCamelCase_							,						1   )							,						(1, self.num_choices, 1)   )
             __magic_name__										=						tf.tile(tf.expand_dims(UpperCamelCase_							,						1   )							,						(1, self.num_choices, 1)   )
             __magic_name__										=						{
                 'input_ids': multiple_choice_inputs_ids,
                 'attention_mask': multiple_choice_input_mask,
                 'token_type_ids': multiple_choice_token_type_ids,
             }
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertListEqual(list(result['logits'].shape   )							,						[self.batch_size, self.num_choices]   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.num_labels
             __magic_name__										=						TFAlbertForTokenClassification(config=UpperCamelCase_   )
             __magic_name__										=						{
                 'input_ids': input_ids,
                 'attention_mask': input_mask,
                 'token_type_ids': token_type_ids,
             }
             __magic_name__										=						model(UpperCamelCase_   )
             self.parent.assertListEqual(list(result['logits'].shape   )							,						[self.batch_size, self.seq_length, self.num_labels]   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.prepare_config_and_inputs()
             (
                 (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     (
                 __magic_name__
             )							,     
             )										=						config_and_inputs
             __magic_name__										=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
             return config, inputs_dict
  
  
  
  @require_tf
  class 					UpperCamelCase__   (							a_       ,  a_       ,  unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   (
          (
              TFAlbertModel,
              TFAlbertForPreTraining,
              TFAlbertForMaskedLM,
              TFAlbertForSequenceClassification,
              TFAlbertForQuestionAnswering,
              TFAlbertForTokenClassification,
              TFAlbertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase											=   (
          {
              """feature-extraction""": TFAlbertModel,
              """fill-mask""": TFAlbertForMaskedLM,
              """question-answering""": TFAlbertForQuestionAnswering,
              """text-classification""": TFAlbertForSequenceClassification,
              """token-classification""": TFAlbertForTokenClassification,
              """zero-shot""": TFAlbertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCAmelCase											=   False
      __UpperCAmelCase											=   False
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  int=False   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						super()._prepare_for_class(UpperCamelCase_							,						UpperCamelCase_							,						return_labels=UpperCamelCase_   )
  
             if return_labels:
                    if model_class in get_values(UpperCamelCase_   ):
                           __magic_name__										=						tf.zeros(self.model_tester.batch_size							,						dtype=tf.intaa   )
  
             return inputs_dict
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						TFAlbertModelTester(self   )
             __magic_name__										=						ConfigTester(self							,						config_class=UpperCamelCase_							,						hidden_size=3_7   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.config_tester.run_common_tests()
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_model(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_for_pretraining(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_for_masked_lm(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_for_multiple_choice(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_for_sequence_classification(*UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_albert_for_question_answering(*UpperCamelCase_   )
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __magic_name__										=						TFAlbertModel.from_pretrained(UpperCamelCase_   )
                    self.assertIsNotNone(UpperCamelCase_   )
  
  
  
  @require_tf
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @slow
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						TFAlbertForPreTraining.from_pretrained('albert-base-v2'   )
             __magic_name__										=						tf.constant([[0, 1, 2, 3, 4, 5]]   )
             __magic_name__										=						model(UpperCamelCase_   )[0]
  
             __magic_name__										=						[1, 6, 3_0_0_0_0]
             self.assertEqual(output.shape							,						UpperCamelCase_   )
  
             __magic_name__										=						tf.constant(
                 [
                     [
                         [4.595_668, 0.74_462_754, -1.818_147],
                         [4.5_954_347, 0.7_454_184, -1.8_188_258],
                         [4.5_954_905, 0.7_448_235, -1.8_182_316],
                     ]
                 ]   )
             tf.debugging.assert_near(output[:, :3, :3]							,						UpperCamelCase_							,						atol=1e-4   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  def        A	(							__snake_case:   List[Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         return 1 / (1 + np.exp(-1 * x     ))
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Tuple							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Optional[int]=None							,						UpperCamelCase_   :  List[str]=None							,						UpperCamelCase_   :  Tuple=False   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						units
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						activation
             if learning_rate is None:
                    __magic_name__										=						0.3
             __magic_name__										=						learning_rate
             __magic_name__										=						is_input_layer
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						(self.units, back_units)   )   )
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						self.units   )   ).T
             if self.activation is None:
                    __magic_name__										=						sigmoid
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.activation == sigmoid:
                    __magic_name__										=						np.dot(self.output							,						(1 - self.output).T   )
                    __magic_name__										=						np.diag(np.diag(UpperCamelCase_   )   )
             else:
                    __magic_name__										=						1
             return gradient_activation
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						xdata
             if self.is_input_layer:
                    # input layer
                    __magic_name__										=						xdata
                    __magic_name__										=						xdata
                    return xdata
             else:
                    __magic_name__										=						np.dot(self.weight							,						self.xdata   ) - self.bias
                    __magic_name__										=						self.activation(self.wx_plus_b   )
                    return self.output
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.cal_gradient()  # i * i 维
             __magic_name__										=						np.asmatrix(np.dot(gradient.T							,						UpperCamelCase_   )   )
  
             __magic_name__										=						np.asmatrix(self.xdata   )
             __magic_name__										=						-1
             __magic_name__										=						self.weight
  
             __magic_name__										=						np.dot(gradient.T							,						self._gradient_weight.T   )
             __magic_name__										=						gradient * self._gradient_bias
             __magic_name__										=						np.dot(UpperCamelCase_							,						self._gradient_x   ).T
             # upgrade: the Negative gradient direction
             __magic_name__										=						self.weight - self.learn_rate * self.gradient_weight
             __magic_name__										=						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   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[]
             __magic_name__										=						[]
             __magic_name__										=						plt.figure()
             __magic_name__										=						self.fig_loss.add_subplot(1							,						1							,						1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.layers.append(UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             for i, layer in enumerate(self.layers[:]   ):
                    if i < 1:
                           __magic_name__										=						True
                    else:
                           layer.initializer(self.layers[i - 1].units   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''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 							a__ (		self   :  str							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						train_round
             __magic_name__										=						accuracy
  
             self.ax_loss.hlines(self.accuracy							,						0							,						self.train_round * 1.1   )
  
             __magic_name__										=						np.shape(UpperCamelCase_   )
             for _ in range(UpperCamelCase_   ):
                    __magic_name__										=						0
                    for row in range(x_shape[0]   ):
                           __magic_name__										=						np.asmatrix(xdata[row, :]   ).T
                           __magic_name__										=						np.asmatrix(ydata[row, :]   ).T
  
                           # forward propagation
                           for layer in self.layers:
                                  __magic_name__										=						layer.forward_propagation(_xdata   )
  
                           __magic_name__							,     __magic_name__										=						self.cal_loss(_ydata							,						_xdata   )
                           __magic_name__										=						all_loss + loss
  
                           # back propagation: the input_layer does not upgrade
                           for layer in self.layers[:0:-1]:
                                  __magic_name__										=						layer.back_propagation(UpperCamelCase_   )
  
                    __magic_name__										=						all_loss / x_shape[0]
                    self.train_mse.append(UpperCamelCase_   )
  
                    self.plot_loss()
  
                    if mse < self.accuracy:
                           print('----达到精度----'   )
                           return mse
             return None
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.sum(np.power((ydata - ydata_)							,						2   )   )
             __magic_name__										=						2 * (ydata_ - ydata)
             # vector (shape is the same as _ydata.shape)
             return self.loss, self.loss_gradient
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''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	(							)						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						np.random.randn(1_0   ,    1_0     )
         __magic_name__										=						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],
             ]     )
         __magic_name__										=						BPNN()
         for i in (1_0, 2_0, 3_0, 2):
                model.add_layer(DenseLayer(__snake_case     )     )
         model.build()
         model.summary()
         model.train(xdata=__snake_case   ,    ydata=__snake_case   ,    train_round=1_0_0   ,    accuracy=0.01     )
  
  
  if __name__ == "__main__":
   example()