

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase				:   Dict					   =							logging.get_logger(__name__)

_lowerCamelCase				:   Dict					   =							{
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}





class 					__UpperCAmelCase     (       lowerCamelCase__						):
   UpperCamelCase        =     """swin2sr"""

   UpperCamelCase        =     {
       """hidden_size""": """embed_dim""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }



   def __init__(							self							:     Tuple,		__A							:     List[Any]=6_4,		__A							:     List[Any]=1,		__A							:     List[Any]=3,		__A							:     Optional[int]=1_8_0,		__A							:     Optional[int]=[6, 6, 6, 6, 6, 6],		__A							:     List[Any]=[6, 6, 6, 6, 6, 6],		__A							:     Any=8,		__A							:     List[str]=2.0,		__A							:     int=True,		__A							:     Optional[int]=0.0,		__A							:     Optional[int]=0.0,		__A							:     List[Any]=0.1,		__A							:     Optional[Any]="gelu",		__A							:     Optional[int]=False,		__A							:     str=0.0_2,		__A							:     Optional[Any]=1E-5,		__A							:     Optional[int]=2,		__A							:     Any=1.0,		__A							:     str="1conv",		__A							:     str="pixelshuffle",		**__A							:     Dict,		):
     super().__init__(**__a						)

     UpperCAmelCase       :							Tuple  = image_size
     UpperCAmelCase       :							List[Any]  = patch_size
     UpperCAmelCase       :							Dict  = num_channels
     UpperCAmelCase       :							List[Any]  = embed_dim
     UpperCAmelCase       :							Dict  = depths
     UpperCAmelCase       :							Tuple  = len(__a						)
     UpperCAmelCase       :							str  = num_heads
     UpperCAmelCase       :							int  = window_size
     UpperCAmelCase       :							Union[str, Any]  = mlp_ratio
     UpperCAmelCase       :							List[Any]  = qkv_bias
     UpperCAmelCase       :							Dict  = hidden_dropout_prob
     UpperCAmelCase       :							List[Any]  = attention_probs_dropout_prob
     UpperCAmelCase       :							Any  = drop_path_rate
     UpperCAmelCase       :							Dict  = hidden_act
     UpperCAmelCase       :							List[str]  = use_absolute_embeddings
     UpperCAmelCase       :							Any  = layer_norm_eps
     UpperCAmelCase       :							Optional[Any]  = initializer_range
     UpperCAmelCase       :							List[Any]  = upscale
     UpperCAmelCase       :							Any  = img_range
     UpperCAmelCase       :							Optional[int]  = resi_connection
     UpperCAmelCase       :							List[str]  = upsampler







"""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       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





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

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

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

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





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

				plt.legend()
				plt.show()






