import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							OpenAIGPTTokenizer
   lowerCAmelCase_														=							OpenAIGPTTokenizerFast
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     SCREAMING_SNAKE_CASE__    							=   [
         """l""",
         """o""",
         """w""",
         """e""",
         """r""",
         """s""",
         """t""",
         """i""",
         """d""",
         """n""",
         """w</w>""",
         """r</w>""",
         """t</w>""",
         """lo""",
         """low""",
         """er</w>""",
         """low</w>""",
         """lowest</w>""",
         """newer</w>""",
         """wider</w>""",
         """<unk>""",
     ]
     SCREAMING_SNAKE_CASE__    							=   dict(zip(_lowercase ,			range(len(_lowercase   )   )   )   )
     SCREAMING_SNAKE_CASE__    							=   ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

     SCREAMING_SNAKE_CASE__    							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["""vocab_file"""]   )
     SCREAMING_SNAKE_CASE__    							=   os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["""merges_file"""]   )
     with open(self.vocab_file ,			"""w"""   ) as fp:
       fp.write(json.dumps(_lowercase   )   )
     with open(self.merges_file ,			"""w"""   ) as fp:
       fp.write("""\n""".join(_lowercase   )   )




   def 			__a		(					self    :       Any ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return "lower newer", "lower newer"




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   OpenAIGPTTokenizer(self.vocab_file ,			self.merges_file   )

     SCREAMING_SNAKE_CASE__    							=   """lower"""
     SCREAMING_SNAKE_CASE__    							=   ["""low""", """er</w>"""]
     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize(_lowercase   )
     self.assertListEqual(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokens + ["""<unk>"""]
     SCREAMING_SNAKE_CASE__    							=   [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase   ) ,			_lowercase   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       List[Any]=15   ):



     """simple docstring"""






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         SCREAMING_SNAKE_CASE__    							=   self.rust_tokenizer_class.from_pretrained(_lowercase ,			**_lowercase   )

         # Simple input
         SCREAMING_SNAKE_CASE__    							=   """This is a simple input"""
         SCREAMING_SNAKE_CASE__    							=   ["""This is a simple input 1""", """This is a simple input 2"""]
         SCREAMING_SNAKE_CASE__    							=   ("""This is a simple input""", """This is a pair""")
         SCREAMING_SNAKE_CASE__    							=   [
             ("""This is a simple input 1""", """This is a simple input 2"""),
             ("""This is a simple pair 1""", """This is a simple pair 2"""),
         ]

         # Simple input tests
         self.assertRaises(_lowercase ,			tokenizer_r.encode ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length"""   )

         # Simple input
         self.assertRaises(_lowercase ,			tokenizer_r.encode_plus ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length"""   )

         # Simple input
         self.assertRaises(
             _lowercase ,			tokenizer_r.batch_encode_plus ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length""" ,			)

         # Pair input
         self.assertRaises(_lowercase ,			tokenizer_r.encode ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length"""   )

         # Pair input
         self.assertRaises(_lowercase ,			tokenizer_r.encode_plus ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length"""   )

         # Pair input
         self.assertRaises(
             _lowercase ,			tokenizer_r.batch_encode_plus ,			_lowercase ,			max_length=_lowercase ,			padding="""max_length""" ,			)




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     pass







@require_ftfy
@require_spacy
@require_tokenizers
class __snake_case       (		lowerCamelCase_	):
   pass

# 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()
