import numpy as np
import requests
def 					UpperCamelCase__		(							)  ->   Any:
				snake_case__   :      Dict   				= requests.get(
				    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
				    'master/Week1/ADRvsRating.csv'       )
				snake_case__   :      Optional[Any]   				= response.text.splitlines()
				snake_case__   :      Union[str, Any]   				= []
				for item in lines:
								snake_case__   :      Tuple   				= item.split(','       )
								data.append(A__       )
				data.pop(0       )  # This is for removing the labels from the list
				snake_case__   :      Optional[int]   				= np.matrix(A__       )
				return dataset
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__     ,					A__       )  ->   Dict:
				snake_case__   :      int   				= len_data

				snake_case__   :      Optional[int]   				= np.dot(A__     ,					data_x.transpose()       )
				prod -= data_y.transpose()
				snake_case__   :      Any   				= np.dot(A__     ,					A__       )
				snake_case__   :      List[Any]   				= theta - (alpha / n) * sum_grad
				return theta
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__       )  ->   Optional[Any]:
				snake_case__   :      int   				= np.dot(A__     ,					data_x.transpose()       )
				prod -= data_y.transpose()
				snake_case__   :      List[str]   				= np.sum(np.square(A__       )       )
				snake_case__   :      Any   				= sum_elem / (2 * len_data)
				return error
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Dict:
				snake_case__   :      Union[str, Any]   				= 10_0000
				snake_case__   :      Union[str, Any]   				= 0.0_0_0_1_5_5_0

				snake_case__   :      Union[str, Any]   				= data_x.shape[1]
				snake_case__   :      int   				= data_x.shape[0] - 1

				snake_case__   :      int   				= np.zeros((1, no_features)       )

				for i in range(0     ,					A__       ):
								snake_case__   :      Optional[int]   				= run_steep_gradient_descent(A__     ,					A__     ,					A__     ,					A__     ,					A__       )
								snake_case__   :      Union[str, Any]   				= sum_of_square_error(A__     ,					A__     ,					A__     ,					A__       )
								print(F"""At Iteration {i + 1} - Error is {error:.5f}"""       )

				return theta
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Tuple:
				snake_case__   :      str   				= sum(abs(y - predicted_y[i]       ) for i, y in enumerate(A__       )       )
				return total / len(A__       )
def 					UpperCamelCase__		(							)  ->   Dict:
				snake_case__   :      str   				= collect_dataset()

				snake_case__   :      Dict   				= data.shape[0]
				snake_case__   :      Optional[int]   				= np.c_[np.ones(A__       ), data[:, :-1]].astype(A__       )
				snake_case__   :      str   				= data[:, -1].astype(A__       )

				snake_case__   :      Optional[int]   				= run_linear_regression(A__     ,					A__       )
				snake_case__   :      List[str]   				= theta.shape[1]
				print('Resultant Feature vector : '       )
				for i in range(0     ,					A__       ):
								print(F"""{theta[0, i]:.5f}"""       )


if __name__ == "__main__":
				main()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowerCAmelCase__	:  Union[str, Any]						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


