code: 
  
  
  
  
  
  
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Dict		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return 1 / (1 + np.exp(-1 * x		))
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=None   ,    __SCREAMING_SNAKE_CASE			:    List[str]=None   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=False			)			->       Optional[Any]:
             __UpperCAmelCase    							=units
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=activation
             if learning_rate is None:
                    __UpperCAmelCase    							=0.3
             __UpperCAmelCase    							=learning_rate
             __UpperCAmelCase    							=is_input_layer
  
  
  
      def _a       (							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Optional[int]:
             __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 _a       (							self			:    Dict			)			->       Union[str, Any]:
             # activation function may be sigmoid or linear
             if self.activation == sigmoid:
                    __UpperCAmelCase    							=np.dot(self.output   ,    (1 - self.output).T			)
                    __UpperCAmelCase    							=np.diag(np.diag(__SCREAMING_SNAKE_CASE			)			)
             else:
                    __UpperCAmelCase    							=1
             return gradient_activation
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       List[str]:
             __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 _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Optional[Any]:
             __UpperCAmelCase    							=self.cal_gradient()  # i * i 维
             __UpperCAmelCase    							=np.asmatrix(np.dot(gradient.T   ,    __SCREAMING_SNAKE_CASE			)			)
  
             __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(__SCREAMING_SNAKE_CASE   ,    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 							_A :
      """simple docstring"""
  
      def __init__(							self			:    Union[str, Any]			)			->       Any:
             __UpperCAmelCase    							=[]
             __UpperCAmelCase    							=[]
             __UpperCAmelCase    							=plt.figure()
             __UpperCAmelCase    							=self.fig_loss.add_subplot(1   ,    1   ,    1			)
  
  
  
      def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]			)			->       Optional[Any]:
             self.layers.append(__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Tuple			)			->       List[str]:
             for i, layer in enumerate(self.layers[:]			):
                    if i < 1:
                           __UpperCAmelCase    							=True
                    else:
                           layer.initializer(self.layers[i - 1].units			)
  
  
  
      def _a       (							self			:    Tuple			)			->       List[str]:
             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			:    int   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]			)			->       Union[str, Any]:
             __UpperCAmelCase    							=train_round
             __UpperCAmelCase    							=accuracy
  
             self.ax_loss.hlines(self.accuracy   ,    0   ,    self.train_round * 1.1			)
  
             __UpperCAmelCase    							=np.shape(__SCREAMING_SNAKE_CASE			)
             for _ in range(__SCREAMING_SNAKE_CASE			):
                    __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							,   __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(__SCREAMING_SNAKE_CASE			)
  
                    __UpperCAmelCase    							=all_loss / x_shape[0]
                    self.train_mse.append(__SCREAMING_SNAKE_CASE			)
  
                    self.plot_loss()
  
                    if mse < self.accuracy:
                           print("""----达到精度----"""			)
                           return mse
             return None
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[Any]			)			->       int:
             __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 _a       (							self			:    Optional[int]			)			->       Tuple:
             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 			lowercase__  (				)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=np.random.randn(10     ,			10		)
         __UpperCAmelCase    							=np.asarray(
             [
                 [0.8, 0.4],
                 [0.4, 0.3],
                 [0.3_4, 0.4_5],
                 [0.6_7, 0.3_2],
                 [0.8_8, 0.6_7],
                 [0.7_8, 0.7_7],
                 [0.5_5, 0.6_6],
                 [0.5_5, 0.4_3],
                 [0.5_4, 0.1],
                 [0.1, 0.5],
             ]		)
         __UpperCAmelCase    							=BPNN()
         for i in (10, 20, 30, 2):
                model.add_layer(DenseLayer(A_		)		)
         model.build()
         model.summary()
         model.train(xdata=A_     ,			ydata=A_     ,			train_round=100     ,			accuracy=0.0_1		)
  
  
  if __name__ == "__main__":
     example()
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import argparse
  import os
  import pathlib
  
  import fairseq
  import torch
  from fairseq.modules import TransformerSentenceEncoderLayer
  from packaging import version
  
  from transformers import (
      DataaVecTextConfig,
      DataaVecTextForMaskedLM,
      DataaVecTextForSequenceClassification,
      DataaVecTextModel,
  )
  from transformers.models.bert.modeling_bert import (
      BertIntermediate,
      BertLayer,
      BertOutput,
      BertSelfAttention,
      BertSelfOutput,
  )
  
  # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
  # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
  from transformers.utils import logging
  
  
  if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")
  
  
  logging.set_verbosity_info()
  __A  							=  logging.get_logger(__name__)
  
  __A  							=  "Hello world! cécé herlolip"
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
         __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
             A_     ,			checkpoint_file=A_		)
         dataavec.eval()  # disable dropout
         __UpperCAmelCase    							=dataavec.models[0]
         __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
         __UpperCAmelCase    							=DataaVecTextConfig(
             vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=dataavec_model.args.encoder_embed_dim     ,			num_hidden_layers=dataavec_model.args.encoder_layers     ,			num_attention_heads=dataavec_model.args.encoder_attention_heads     ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1e-5     ,			)
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
         print("""Our BERT config:"""     ,			A_		)
  
         __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
         model.eval()
  
         # Now let's copy all the weights.
         # Embeddings
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
         __UpperCAmelCase    							=torch.zeros_like(
             model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
         __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias
  
         for i in range(config.num_hidden_layers		):
                # Encoder: start of layer
                __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
                __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]
  
                # self attention
                __UpperCAmelCase    							=layer.attention.self
                assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
                assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                    (config.hidden_size, config.hidden_size)		), (
                    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
                )
  
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias
  
                # self-attention output
                __UpperCAmelCase    							=layer.attention.output
                assert (
                    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
                ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias
  
                # intermediate
                __UpperCAmelCase    							=layer.intermediate
                assert (
                    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
  
                # output
                __UpperCAmelCase    							=layer.output
                assert (
                    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
                ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
                __UpperCAmelCase    							=dataavec_layer.fca.weight
                __UpperCAmelCase    							=dataavec_layer.fca.bias
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
                __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
                # end of layer
  
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].dense.bias
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.bias
         else:
                # LM Head
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.dense.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.layer_norm.bias
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.weight
                __UpperCAmelCase    							=dataavec_model.encoder.lm_head.bias
  
         # Let's check that we get the same results.
         __UpperCAmelCase    							=dataavec.encode(A_		).unsqueeze(0		)  # batch of size 1
  
         __UpperCAmelCase    							=model(A_		)[0]
         if classification_head:
                __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
         else:
                __UpperCAmelCase    							=dataavec_model(A_		)[0]
         print(our_output.shape     ,			their_output.shape		)
         __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
         print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
         __UpperCAmelCase    							=torch.allclose(A_     ,			A_     ,			atol=1e-3		)
         print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
         if not success:
                raise Exception("""Something went wRoNg"""		)
  
         pathlib.Path(A_		).mkdir(parents=A_     ,			exist_ok=A_		)
         print(F'''Saving model to {pytorch_dump_folder_path}'''		)
         model.save_pretrained(A_		)
  
  
  if __name__ == "__main__":
     __A  							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--classification_head", action="store_true", help="Whether to convert a final classification head."
     )
     __A  							=  parser.parse_args()
     convert_dataavec_checkpoint_to_pytorch(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )
  
  
  
  
  