



'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	lowerCAmelCase_(    lowerCamelCase_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				["""image_processor""", """tokenizer"""]
  __lowercase							:				Optional[int]           =				"""CLIPImageProcessor"""
  __lowercase							:				List[str]           =				("""CLIPTokenizer""", """CLIPTokenizerFast""")




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  int											= None
      if "feature_extractor" in kwargs:
          warnings.warn(
              """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
              """ instead."""  ,_UpperCAmelCase  ,)
          lowerCAmelCase__			:  List[Any]											= kwargs.pop("""feature_extractor"""				)

      lowerCAmelCase__			:  Union[str, Any]											= image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
          raise ValueError("""You need to specify an `image_processor`."""				)
      if tokenizer is None:
          raise ValueError("""You need to specify a `tokenizer`."""				)

      super().__init__(_UpperCAmelCase  ,_UpperCAmelCase				)




  def __call__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		int:






      if text is None and images is None:
          raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

      if text is not None:
          lowerCAmelCase__			:  int											= self.tokenizer(_UpperCAmelCase  ,return_tensors=_UpperCAmelCase  ,**_UpperCAmelCase				)

      if images is not None:
          lowerCAmelCase__			:  List[Any]											= self.image_processor(_UpperCAmelCase  ,return_tensors=_UpperCAmelCase  ,**_UpperCAmelCase				)

      if text is not None and images is not None:
          lowerCAmelCase__			:  int											= image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(data=dict(**_UpperCAmelCase				)  ,tensor_type=_UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[int]:
      return self.tokenizer.batch_decode(*_UpperCAmelCase  ,**_UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      return self.tokenizer.decode(*_UpperCAmelCase  ,**_UpperCAmelCase				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  str											= self.tokenizer.model_input_names
      lowerCAmelCase__			:  int											= self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,_UpperCAmelCase  ,)
      return self.image_processor_class






  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,_UpperCAmelCase  ,)
      return self.image_processor











'''simple docstring'''





# 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      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= cost_function(UpperCamelCase							,   UpperCamelCase      )
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""      )  # printing the loss after every 100 iterations
    return theta


# In[68]:

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

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     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      ):



     """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))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


