






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()












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
   )




