






'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   return 1 / (1 + np.exp(-1 * x							))


class     _UpperCAmelCase  :




    def __init__(     self					:    str					,       a					:    Any					,       a					:    Dict=None					,       a					:    Optional[Any]=None					,       a					:    Tuple=False     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       units
       lowercase_		:      str          =       None
       lowercase_		:      str          =       None
       lowercase_		:      int          =       activation
       if learning_rate is None:
          lowercase_		:      List[Any]          =       0.3
       lowercase_		:      List[str]          =       learning_rate
       lowercase_		:      Any          =       is_input_layer




    def 			lowerCAmelCase__			(     self					:    Optional[int]					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       np.asmatrix(np.random.normal(0					,       0.5					,       (self.units, back_units)     )     )
       lowercase_		:      List[Any]          =       np.asmatrix(np.random.normal(0					,       0.5					,       self.units     )     ).T
       if self.activation is None:
          lowercase_		:      Union[str, Any]          =       sigmoid




    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       if self.activation == sigmoid:
          lowercase_		:      Tuple          =       np.dot(self.output					,       (1 - self.output).T     )
          lowercase_		:      Optional[int]          =       np.diag(np.diag(a     )     )
       else:
          lowercase_		:      Tuple          =       1
       return gradient_activation




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      str          =       xdata
       if self.is_input_layer:
          # input layer
          lowercase_		:      Any          =       xdata
          lowercase_		:      Tuple          =       xdata
          return xdata
       else:
          lowercase_		:      Optional[Any]          =       np.dot(self.weight					,       self.xdata     ) - self.bias
          lowercase_		:      str          =       self.activation(self.wx_plus_b     )
          return self.output





    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    Any     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       self.cal_gradient()  # i * i 维
       lowercase_		:      Union[str, Any]          =       np.asmatrix(np.dot(gradient.T					,       a     )     )

       lowercase_		:      int          =       np.asmatrix(self.xdata     )
       lowercase_		:      List[Any]          =       -1
       lowercase_		:      str          =       self.weight

       lowercase_		:      Tuple          =       np.dot(gradient.T					,       self._gradient_weight.T     )
       lowercase_		:      Optional[Any]          =       gradient * self._gradient_bias
       lowercase_		:      Union[str, Any]          =       np.dot(a					,       self._gradient_x     ).T
       # upgrade: the Negative gradient direction
       lowercase_		:      List[str]          =       self.weight - self.learn_rate * self.gradient_weight
       lowercase_		:      Union[str, Any]          =       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  :




    def __init__(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       []
       lowercase_		:      Dict          =       []
       lowercase_		:      Tuple          =       plt.figure()
       lowercase_		:      Union[str, Any]          =       self.fig_loss.add_subplot(1					,       1					,       1     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    Any     ):






       '''simple docstring'''






       self.layers.append(a     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       for i, layer in enumerate(self.layers[:]     ):
          if i < 1:
             lowercase_		:      Optional[int]          =       True
          else:
             layer.initializer(self.layers[i - 1].units     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''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 			lowerCAmelCase__			(     self					:    Optional[Any]					,       a					:    str					,       a					:    Union[str, Any]					,       a					:    Union[str, Any]					,       a					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       train_round
       lowercase_		:      List[Any]          =       accuracy

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

       lowercase_		:      Dict          =       np.shape(a     )
       for _ in range(a     ):
          lowercase_		:      Optional[Any]          =       0
          for row in range(x_shape[0]     ):
             lowercase_		:      int          =       np.asmatrix(xdata[row, :]     ).T
             lowercase_		:      str          =       np.asmatrix(ydata[row, :]     ).T

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

             lowercase_      ,						lowercase_		:      Optional[int]          =       self.cal_loss(_ydata					,       _xdata     )
             lowercase_		:      Any          =       all_loss + loss

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

          lowercase_		:      Dict          =       all_loss / x_shape[0]
          self.train_mse.append(a     )

          self.plot_loss()

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




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    Any					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       np.sum(np.power((ydata - ydata_)					,       2     )     )
       lowercase_		:      Tuple          =       2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient





    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''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 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Any          =       np.random.randn(10  ,						10							)
   lowercase_		:      Dict          =       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_		:      List[str]          =       BPNN()
   for i in (10, 20, 30, 2):
      model.add_layer(DenseLayer(_UpperCamelCase							)							)
   model.build()
   model.summary()
   model.train(xdata=_UpperCamelCase  ,						ydata=_UpperCamelCase  ,						train_round=100  ,						accuracy=0.01							)


if __name__ == "__main__":
 example()












'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 UpperCamelCase__	=     None


UpperCamelCase__	=     logging.get_logger(__name__)

UpperCamelCase__	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__	=     {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__	=     {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
UpperCamelCase__	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[str]						    =      VOCAB_FILES_NAMES
    __lowerCamelCase:				int						    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:				List[str]						    =      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:				List[str]						    =      ['input_ids', 'attention_mask']
    __lowerCamelCase:				str						    =      MBartaaTokenizer

    __lowerCamelCase:				List[int]						    =      []
    __lowerCamelCase:				List[int]						    =      []




    def __init__(     self					:    Optional[Any]					,       a					:    Any=None					,       a					:    List[Any]=None					,       a					:    Optional[int]=None					,       a					:    Dict=None					,       a					:    int="</s>"					,       a					:    Optional[Any]="</s>"					,       a					:    List[Any]="<s>"					,       a					:    Any="<unk>"					,       a					:    Optional[Any]="<pad>"					,       a					:    List[str]="<mask>"					,       **a					:    Optional[Any]					,       ):






       '''simple docstring'''






       lowercase_		:      int          =       AddedToken(a					,       lstrip=a					,       rstrip=a     ) if isinstance(a					,       a     ) else mask_token

       lowercase_		:      List[Any]          =       kwargs.get("additional_special_tokens"					,       []     )
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           a					,       src_lang=a					,       tgt_lang=a					,       tokenizer_file=a					,       eos_token=a					,       sep_token=a					,       cls_token=a					,       unk_token=a					,       pad_token=a					,       mask_token=a					,       **a					,       )

       lowercase_		:      Any          =       vocab_file
       lowercase_		:      int          =       False if not self.vocab_file else True

       lowercase_		:      Optional[Any]          =       {
           lang_code: self.convert_tokens_to_ids(a     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       lowercase_		:      int          =       src_lang if src_lang is not None else "en_XX"
       lowercase_		:      Tuple          =       tgt_lang
       lowercase_		:      List[Any]          =       self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang     )




    @property
    def 			lowerCAmelCase__			(     self					:    List[Any]     ):






       '''simple docstring'''






       return self._src_lang




    @src_lang.setter
    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       new_src_lang
       self.set_src_lang_special_tokens(self._src_lang     )




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    List[int]					,       a					:    Optional[List[int]] = None     ):






       '''simple docstring'''






       if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    List[str]					,       a					:    str = "en_XX"					,       a					:    Optional[List[str]] = None					,       a					:    str = "ro_RO"					,       **a					:    Union[str, Any]					,       ):






       '''simple docstring'''






       lowercase_		:      Dict          =       src_lang
       lowercase_		:      Dict          =       tgt_lang
       return super().prepare_seqaseq_batch(a					,       a					,       **a     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       return self.set_src_lang_special_tokens(self.src_lang     )




    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       return self.set_tgt_lang_special_tokens(self.tgt_lang     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      Optional[int]          =       [self.cur_lang_code_id]
       lowercase_		:      List[Any]          =       [self.eos_token_id]

       lowercase_		:      Optional[Any]          =       self.convert_ids_to_tokens(self.prefix_tokens     )
       lowercase_		:      Union[str, Any]          =       self.convert_ids_to_tokens(self.suffix_tokens     )

       lowercase_		:      Optional[Any]          =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,       pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,       self.prefix_tokens + self.suffix_tokens     )     )					,       )




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      str          =       [self.cur_lang_code_id]
       lowercase_		:      List[str]          =       [self.eos_token_id]

       lowercase_		:      Dict          =       self.convert_ids_to_tokens(self.prefix_tokens     )
       lowercase_		:      Dict          =       self.convert_ids_to_tokens(self.suffix_tokens     )

       lowercase_		:      int          =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,       pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,       self.prefix_tokens + self.suffix_tokens     )     )					,       )




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    int					,       a					:    str					,       a					:    Optional[str]					,       a					:    Optional[str]					,       **a					:    str     ):






       '''simple docstring'''






       if src_lang is None or tgt_lang is None:
          raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
       lowercase_		:      Any          =       src_lang
       lowercase_		:      int          =       self(a					,       add_special_tokens=a					,       return_tensors=a					,       **a     )
       lowercase_		:      Any          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      Optional[Any]          =       tgt_lang_id
       return inputs





    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    str					,       a					:    Optional[str] = None     ):






       '''simple docstring'''






       if not self.can_save_slow_tokenizer:
          raise ValueError(
              "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
              "tokenizer."     )

       if not os.path.isdir(a     ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
          return
       lowercase_		:      Any          =       os.path.join(
           a					,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

       if os.path.abspath(self.vocab_file     ) != os.path.abspath(a     ):
          copyfile(self.vocab_file					,       a     )

       return (out_vocab_file,)




