import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Dict ,			_lowercase    :       str = "▁" ,			_lowercase    :       bool = True ,			_lowercase    :       Union[str, AddedToken] = "<unk>" ,			_lowercase    :       Union[str, AddedToken] = "</s>" ,			_lowercase    :       Union[str, AddedToken] = "<pad>" ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {
         """pad""": {"""id""": 0, """token""": pad_token},
         """eos""": {"""id""": 1, """token""": eos_token},
         """unk""": {"""id""": 2, """token""": unk_token},
     }

     SCREAMING_SNAKE_CASE__    							=   [None] * len(self.special_tokens   )
     for token_dict in self.special_tokens.values():
       SCREAMING_SNAKE_CASE__    							=   token_dict["""token"""]

     SCREAMING_SNAKE_CASE__    							=   Tokenizer(Unigram()   )

     SCREAMING_SNAKE_CASE__    							=   normalizers.Sequence(
         [
             normalizers.Nmt(),
             normalizers.NFKC(),
             normalizers.Replace(Regex(""" {2,}"""   ) ,			""" """   ),
             normalizers.Lowercase(),
         ]   )
     SCREAMING_SNAKE_CASE__    							=   pre_tokenizers.Sequence(
         [
             pre_tokenizers.Metaspace(replacement=_lowercase ,			add_prefix_space=_lowercase   ),
             pre_tokenizers.Digits(individual_digits=_lowercase   ),
             pre_tokenizers.Punctuation(),
         ]   )
     SCREAMING_SNAKE_CASE__    							=   decoders.Metaspace(replacement=_lowercase ,			add_prefix_space=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   TemplateProcessing(
         single=f"""$A {self.special_tokens['eos']['token']}""" ,			special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])] ,			)

     SCREAMING_SNAKE_CASE__    							=   {
         """model""": """SentencePieceUnigram""",
         """replacement""": replacement,
         """add_prefix_space""": add_prefix_space,
     }

     super().__init__(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       Union[str, List[str]] ,			_lowercase    :       int = 80_00 ,			_lowercase    :       bool = True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   trainers.UnigramTrainer(
         vocab_size=_lowercase ,			special_tokens=self.special_tokens_list ,			show_progress=_lowercase ,			)

     if isinstance(_lowercase ,			_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   [files]
     self._tokenizer.train(_lowercase ,			trainer=_lowercase   )

     self.add_unk_id()




   def 			__a		(					self    :       int ,			_lowercase    :       Union[Iterator[str], Iterator[Iterator[str]]] ,			_lowercase    :       int = 80_00 ,			_lowercase    :       bool = True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   trainers.UnigramTrainer(
         vocab_size=_lowercase ,			special_tokens=self.special_tokens_list ,			show_progress=_lowercase ,			)

     self._tokenizer.train_from_iterator(_lowercase ,			trainer=_lowercase   )

     self.add_unk_id()




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   json.loads(self._tokenizer.to_str()   )

     SCREAMING_SNAKE_CASE__    							=   self.special_tokens["""unk"""]["""id"""]

     SCREAMING_SNAKE_CASE__    							=   Tokenizer.from_str(json.dumps(_lowercase   )   )

# 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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # 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''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
