code: 
  
  
  
  
  
  
  """simple docstring"""
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  snake_case   :   str    		=		logging.get_logger(__name__)
  
  snake_case   :   str    		=		{
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """mbart"""
      __UpperCAmelCase											=   ["""past_key_values"""]
      __UpperCAmelCase											=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
  
  
      def __init__(		self   :  str							,						UpperCamelCase_   :  Optional[Any]=5_0_2_6_5							,						UpperCamelCase_   :  List[str]=1_0_2_4							,						UpperCamelCase_   :  Union[str, Any]=1_2							,						UpperCamelCase_   :  List[Any]=4_0_9_6							,						UpperCamelCase_   :  Dict=1_6							,						UpperCamelCase_   :  Dict=1_2							,						UpperCamelCase_   :  List[str]=4_0_9_6							,						UpperCamelCase_   :  List[str]=1_6							,						UpperCamelCase_   :  Tuple=0.0							,						UpperCamelCase_   :  Dict=0.0							,						UpperCamelCase_   :  Optional[Any]=True							,						UpperCamelCase_   :  Union[str, Any]=True							,						UpperCamelCase_   :  Optional[int]="gelu"							,						UpperCamelCase_   :  List[str]=1_0_2_4							,						UpperCamelCase_   :  List[Any]=0.1							,						UpperCamelCase_   :  Union[str, Any]=0.0							,						UpperCamelCase_   :  str=0.0							,						UpperCamelCase_   :  Union[str, Any]=0.02							,						UpperCamelCase_   :  int=0.0							,						UpperCamelCase_   :  Dict=False							,						UpperCamelCase_   :  Optional[int]=1							,						UpperCamelCase_   :  Optional[Any]=0							,						UpperCamelCase_   :  Dict=2							,						UpperCamelCase_   :  Tuple=2							,						**UpperCamelCase_   :  Any							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						d_model
             __magic_name__										=						encoder_ffn_dim
             __magic_name__										=						encoder_layers
             __magic_name__										=						encoder_attention_heads
             __magic_name__										=						decoder_ffn_dim
             __magic_name__										=						decoder_layers
             __magic_name__										=						decoder_attention_heads
             __magic_name__										=						dropout
             __magic_name__										=						attention_dropout
             __magic_name__										=						activation_dropout
             __magic_name__										=						activation_function
             __magic_name__										=						init_std
             __magic_name__										=						encoder_layerdrop
             __magic_name__										=						decoder_layerdrop
             __magic_name__										=						classifier_dropout
             __magic_name__										=						use_cache
             __magic_name__										=						encoder_layers
             __magic_name__										=						scale_embedding  # scale factor will be sqrt(d_model) if True
             super().__init__(
                 pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						is_encoder_decoder=UpperCamelCase_							,						forced_eos_token_id=UpperCamelCase_							,						**UpperCamelCase_							,						)
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]   )
  
                    if self.use_past:
                           __magic_name__										=						{0: 'batch'}
                           __magic_name__										=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           __magic_name__										=						{0: 'batch', 1: 'decoder_sequence'}
                           __magic_name__										=						{0: 'batch', 1: 'decoder_sequence'}
  
                    if self.use_past:
                           self.fill_with_past_key_values_(UpperCamelCase_							,						direction='inputs'   )
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    __magic_name__										=						OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]   )
                    if self.use_past:
                           __magic_name__							,     __magic_name__										=						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    __magic_name__										=						OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                            ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                            ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                        ]   )
  
             return common_inputs
  
  
  
  
  
  
  
      @property
      def 							a__ (		self   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						super().outputs
             else:
                    __magic_name__										=						super(UpperCamelCase_							,						self   ).outputs
                    if self.use_past:
                           __magic_name__							,     __magic_name__										=						self.num_layers
                           for i in range(UpperCamelCase_   ):
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
                                  __magic_name__										=						{0: 'batch', 2: 'past_sequence + sequence'}
             return common_outputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
  
             # Generate decoder inputs
             __magic_name__										=						seq_length if not self.use_past else 1
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
             __magic_name__										=						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             __magic_name__										=						dict(**UpperCamelCase_							,						**UpperCamelCase_   )
  
             if self.use_past:
                    if not is_torch_available():
                           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
                    else:
                           import torch
                    __magic_name__							,     __magic_name__										=						common_inputs['input_ids'].shape
                    __magic_name__										=						common_inputs['decoder_input_ids'].shape[1]
                    __magic_name__							,     __magic_name__										=						self.num_attention_heads
                    __magic_name__										=						(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    __magic_name__										=						decoder_seq_length + 3
                    __magic_name__										=						(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )
  
                    __magic_name__										=						torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(UpperCamelCase_							,						UpperCamelCase_   )]							,						dim=1   )
  
                    __magic_name__										=						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    __magic_name__							,     __magic_name__										=						self.num_layers
                    __magic_name__										=						min(UpperCamelCase_							,						UpperCamelCase_   )
                    __magic_name__										=						max(UpperCamelCase_							,						UpperCamelCase_   ) - min_num_layers
                    __magic_name__										=						'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
  
                    for _ in range(UpperCamelCase_   ):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                                   torch.zeros(UpperCamelCase_   ),
                               )   )
                    # TODO: test this.
                    __magic_name__										=						encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                    for _ in range(UpperCamelCase_							,						UpperCamelCase_   ):
                           common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   ))   )
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
  
             if self.use_past:
                    if not is_torch_available():
                           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
                    else:
                           import torch
                    __magic_name__							,     __magic_name__										=						common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    __magic_name__										=						seqlen + 2
                    __magic_name__							,     __magic_name__										=						self.num_layers
                    __magic_name__							,     __magic_name__										=						self.num_attention_heads
                    __magic_name__										=						(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
  
                    __magic_name__										=						common_inputs['attention_mask'].dtype
                    __magic_name__										=						torch.cat(
                        [common_inputs['attention_mask'], torch.ones(UpperCamelCase_							,						UpperCamelCase_							,						dtype=UpperCamelCase_   )]							,						dim=1   )
                    __magic_name__										=						[
                        (torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   )) for _ in range(UpperCamelCase_   )
                    ]
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						compute_effective_axis_dimension(
                 UpperCamelCase_							,						fixed_dimension=OnnxConfig.default_fixed_batch							,						num_token_to_add=0   )
  
             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             __magic_name__										=						tokenizer.num_special_tokens_to_add(UpperCamelCase_   )
             __magic_name__										=						compute_effective_axis_dimension(
                 UpperCamelCase_							,						fixed_dimension=OnnxConfig.default_fixed_sequence							,						num_token_to_add=UpperCamelCase_   )
  
             # Generate dummy inputs according to compute batch and sequence
             __magic_name__										=						[' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
             __magic_name__										=						dict(tokenizer(UpperCamelCase_							,						return_tensors=UpperCamelCase_   )   )
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Dict							,						UpperCamelCase_   :  PreTrainedTokenizer							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  int = -1							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Optional[TensorType] = None							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
  
             elif self.task == "causal-lm":
                    __magic_name__										=						self._generate_dummy_inputs_for_causal_lm(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
             else:
                    __magic_name__										=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                        UpperCamelCase_							,						batch_size=UpperCamelCase_							,						seq_length=UpperCamelCase_							,						is_pair=UpperCamelCase_							,						framework=UpperCamelCase_   )
  
             return common_inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Dict							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.task in ["default", "seq2seq-lm"]:
                    __magic_name__										=						super()._flatten_past_key_values_(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
             else:
                    __magic_name__										=						super(UpperCamelCase_							,						self   )._flatten_past_key_values_(
                        UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_   )
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()