




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


a													=			logging.get_logger(__name__)

a													=			{
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class       _A				(		__lowercase       ,     __lowercase     ):
		__a					    =       """nat"""

		__a					    =       {
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=4						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=64						,		_SCREAMING_SNAKE_CASE=[3, 4, 6, 5]						,		_SCREAMING_SNAKE_CASE=[2, 4, 8, 16]						,		_SCREAMING_SNAKE_CASE=7						,		_SCREAMING_SNAKE_CASE=3.0						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-5						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			patch_size
								_UpperCAmelCase      =			num_channels
								_UpperCAmelCase      =			embed_dim
								_UpperCAmelCase      =			depths
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			num_heads
								_UpperCAmelCase      =			kernel_size
								_UpperCAmelCase      =			mlp_ratio
								_UpperCAmelCase      =			qkv_bias
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			drop_path_rate
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								_UpperCAmelCase      =			int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE			) - 1)			)
								_UpperCAmelCase      =			layer_scale_init_value
								_UpperCAmelCase      =			["""stem"""] + [F"stage{idx}" for idx in range(1						,		len(_SCREAMING_SNAKE_CASE			) + 1			)]
								_UpperCAmelCase    ,					_UpperCAmelCase      =			get_aligned_output_features_output_indices(
								    out_features=_SCREAMING_SNAKE_CASE						,		out_indices=_SCREAMING_SNAKE_CASE						,		stage_names=self.stage_names			)





# 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      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_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_0_0 == 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=70000)
				print("theta: ", theta)  # printing the theta i.e our weights vector




				def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										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()