






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{'vocab_file': 'sentencepiece.model'}

SCREAMING_SNAKE_CASE 							=						{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

SCREAMING_SNAKE_CASE 							=						{
    'google/rembert': 256,
}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			str =					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			int =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(	self							,  _A							,  _A=False							,  _A=True							,  _A=True							,  _A="[CLS]"							,  _A="[SEP]"							,  _A="[UNK]"							,  _A="[SEP]"							,  _A="[PAD]"							,  _A="[CLS]"							,  _A="[MASK]"							,  **_A							,  ) ->		Optional[int]:

     """simple docstring"""



     super().__init__(
         do_lower_case=__a							,  remove_space=__a							,  keep_accents=__a							,  bos_token=__a							,  eos_token=__a							,  unk_token=__a							,  sep_token=__a							,  pad_token=__a							,  cls_token=__a							,  mask_token=__a							,  **__a							,  )

     _UpperCAmelCase				:      List[str]			  =				do_lower_case
     _UpperCAmelCase				:      Dict			  =				remove_space
     _UpperCAmelCase				:      Any			  =				keep_accents
     _UpperCAmelCase				:      int			  =				vocab_file

     _UpperCAmelCase				:      Tuple			  =				spm.SentencePieceProcessor()
     self.sp_model.Load(__a)


 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     return len(self.sp_model)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				{self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
     vocab.update(self.added_tokens_encoder)
     return vocab


 def __getstate__(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.__dict__.copy()
     _UpperCAmelCase				:      int			  =				None
     return state


 def __setstate__(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				d
     _UpperCAmelCase				:      Dict			  =				spm.SentencePieceProcessor()
     self.sp_model.Load(self.vocab_file)


 def  snake_case__				(	self							,  _A							,  _A=False) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				self.sp_model.EncodeAsPieces(__a)
     return pieces


 def  snake_case__				(	self							,  _A) ->		Union[str, Any]:

     """simple docstring"""



     return self.sp_model.PieceToId(__a)


 def  snake_case__				(	self							,  _A) ->		Dict:

     """simple docstring"""



     return self.sp_model.IdToPiece(__a)


 def  snake_case__				(	self							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.sp_model.decode_pieces(__a)
     return out_string


 def  snake_case__				(	self							,  _A							,  _A = None) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				[self.sep_token_id]
     _UpperCAmelCase				:      str			  =				[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


 def  snake_case__				(	self							,  _A							,  _A = None							,  _A = False) ->		List[int]:

     """simple docstring"""



     if already_has_special_tokens:
         if token_ids_a is not None:
             raise ValueError(
                 '''You should not supply a second sequence if the provided sequence of '''
                 '''ids is already formatted with special tokens for the model.''')
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
         return [1] + ([0] * len(__a)) + [1] + ([0] * len(__a)) + [1]
     return [1] + ([0] * len(__a)) + [1]


 def  snake_case__				(	self							,  _A							,  _A = None) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				[self.sep_token_id]
     _UpperCAmelCase				:      str			  =				[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep) * [0]
     return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


 def  snake_case__				(	self							,  _A							,  _A = None) ->		Tuple[str]:

     """simple docstring"""



     if not os.path.isdir(__a):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(__a))
         return
     _UpperCAmelCase				:      str			  =				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,)












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						datasets.load_iris()
  SCREAMING_SNAKE_CASE 							=						iris.data[:, :2]
  SCREAMING_SNAKE_CASE 							=						(iris.target != 0) * 1

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




