






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			int =					["input_ids", "attention_mask"]


 def __init__(	self							,  _A="</s>"							,  _A="<unk>"							,  _A="<pad>"							,  _A=125							,  _A=None							,  **_A							,  ) ->		None:

     """simple docstring"""



     if extra_ids > 0 and additional_special_tokens is None:
         _UpperCAmelCase				:      Optional[int]			  =				[f'''<extra_id_{i}>''' for i in range(_A)]
     elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra_id special tokens
         _UpperCAmelCase				:      Optional[int]			  =				len(set(filter(lambda _A: bool('''extra_id''' in str(_A))							,  _A)))
         if extra_tokens != extra_ids:
             raise ValueError(
                 f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                 ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                 ''' extra_ids tokens''')

     _UpperCAmelCase				:      Tuple			  =				AddedToken(_A							,  lstrip=_A							,  rstrip=_A) if isinstance(_A							,  _A) else pad_token
     _UpperCAmelCase				:      Union[str, Any]			  =				AddedToken(_A							,  lstrip=_A							,  rstrip=_A) if isinstance(_A							,  _A) else eos_token
     _UpperCAmelCase				:      Union[str, Any]			  =				AddedToken(_A							,  lstrip=_A							,  rstrip=_A) if isinstance(_A							,  _A) else unk_token

     super().__init__(
         eos_token=_A							,  unk_token=_A							,  pad_token=_A							,  extra_ids=_A							,  additional_special_tokens=_A							,  **_A							,  )

     _UpperCAmelCase				:      Union[str, Any]			  =				extra_ids

     _UpperCAmelCase				:      Any			  =				2**8  # utf is 8 bits

     # define special tokens dict
     _UpperCAmelCase				:      Dict[int, str]			  =				{
         self.pad_token: 0,
         self.eos_token: 1,
         self.unk_token: 2,
     }
     _UpperCAmelCase				:      List[Any]			  =				len(self.special_tokens_encoder)
     _UpperCAmelCase				:      Dict			  =				len(_A)
     for i, token in enumerate(_A):
         _UpperCAmelCase				:      Optional[Any]			  =				self.vocab_size + i - n
     _UpperCAmelCase				:      Dict[str, int]			  =				{v: k for k, v in self.special_tokens_encoder.items()}


 @property
 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


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

     """simple docstring"""



     if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_A							,  token_ids_a=_A							,  already_has_special_tokens=_A)

     # normal case: some special tokens
     if token_ids_a is None:
         return ([0] * len(_A)) + [1]
     return ([0] * len(_A)) + [1] + ([0] * len(_A)) + [1]


 def  snake_case__				(	self							,  _A) ->		List[int]:

     """simple docstring"""



     if len(_A) > 0 and token_ids[-1] == self.eos_token_id:
         warnings.warn(
             f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
             ''' eos tokens being added.''')
         return token_ids
     else:
         return token_ids + [self.eos_token_id]


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				[self.eos_token_id]

     if token_ids_a is None:
         return len(token_ids_a + eos) * [0]
     return len(token_ids_a + eos + token_ids_a + eos) * [0]


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

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				self._add_eos_if_not_present(_A)
     if token_ids_a is None:
         return token_ids_a
     else:
         _UpperCAmelCase				:      List[str]			  =				self._add_eos_if_not_present(_A)
         return token_ids_a + token_ids_a


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

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				[chr(_A) for i in text.encode('''utf-8''')]
     return tokens


 def  snake_case__				(	self							,  _A) ->		Dict:

     """simple docstring"""



     if token in self.special_tokens_encoder:
         _UpperCAmelCase				:      Optional[Any]			  =				self.special_tokens_encoder[token]
     elif token in self.added_tokens_encoder:
         _UpperCAmelCase				:      Tuple			  =				self.added_tokens_encoder[token]
     elif len(_A) != 1:
         _UpperCAmelCase				:      Any			  =				self.unk_token_id
     else:
         _UpperCAmelCase				:      Dict			  =				ord(_A) + self._num_special_tokens
     return token_id


 def  snake_case__				(	self							,  _A) ->		Any:

     """simple docstring"""



     if index in self.special_tokens_decoder:
         _UpperCAmelCase				:      List[Any]			  =				self.special_tokens_decoder[index]
     else:
         _UpperCAmelCase				:      int			  =				chr(index - self._num_special_tokens)
     return token


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

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				B''''''
     for token in tokens:
         if token in self.special_tokens_decoder:
             _UpperCAmelCase				:      str			  =				self.special_tokens_decoder[token].encode('''utf-8''')
         elif token in self.added_tokens_decoder:
             _UpperCAmelCase				:      List[str]			  =				self.special_tokens_decoder[token].encode('''utf-8''')
         elif token in self.special_tokens_encoder:
             _UpperCAmelCase				:      Optional[int]			  =				token.encode('''utf-8''')
         elif token in self.added_tokens_encoder:
             _UpperCAmelCase				:      int			  =				token.encode('''utf-8''')
         else:
             _UpperCAmelCase				:      Union[str, Any]			  =				bytes([ord(_A)])
         bstring += tok_string
     _UpperCAmelCase				:      int			  =				bstring.decode('''utf-8'''							,  errors='''ignore''')
     return string


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

     """simple docstring"""



     return ()












# 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()




