



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase										=     logging.get_logger(__name__)






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				'''maskformer-swin'''

  __lowercase							:				Optional[int]           =				{
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }




  def __init__(   self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[3, 6, 12, 24]  ,__UpperCAmelCase=7  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=True  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)   ->		int:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Dict											= image_size
      lowerCAmelCase__			:  Optional[Any]											= patch_size
      lowerCAmelCase__			:  int											= num_channels
      lowerCAmelCase__			:  str											= embed_dim
      lowerCAmelCase__			:  Tuple											= depths
      lowerCAmelCase__			:  Optional[Any]											= len(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= num_heads
      lowerCAmelCase__			:  str											= window_size
      lowerCAmelCase__			:  List[str]											= mlp_ratio
      lowerCAmelCase__			:  List[Any]											= qkv_bias
      lowerCAmelCase__			:  List[Any]											= hidden_dropout_prob
      lowerCAmelCase__			:  Any											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Tuple											= drop_path_rate
      lowerCAmelCase__			:  int											= hidden_act
      lowerCAmelCase__			:  Any											= use_absolute_embeddings
      lowerCAmelCase__			:  str											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      lowerCAmelCase__			:  List[str]											= int(embed_dim * 2 ** (len(__UpperCAmelCase				) - 1)				)
      lowerCAmelCase__			:  Dict											= ["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(__UpperCAmelCase				) + 1				)]
      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names				)







'''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()


