




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a													=			{
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			[
				    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "InstructBlipQFormerModel",
				    "InstructBlipPreTrainedModel",
				    "InstructBlipForConditionalGeneration",
				    "InstructBlipVisionModel",
				]

if TYPE_CHECKING:
				from .configuration_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    InstructBlipConfig,
				    InstructBlipQFormerConfig,
				    InstructBlipVisionConfig,
				)
				from .processing_instructblip import InstructBlipProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_instructblip import (
								    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    InstructBlipForConditionalGeneration,
								    InstructBlipPreTrainedModel,
								    InstructBlipQFormerModel,
								    InstructBlipVisionModel,
								)

else:
				import sys

				a													=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





# 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()