





import numpy as np
import requests




def _a					(  )       ->				int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"   )
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						response.text.splitlines()
							SCREAMING_SNAKE_CASE__   :			str      =						[]
							for item in lines:
														SCREAMING_SNAKE_CASE__   :			str      =						item.split(","   )
														data.append(SCREAMING_SNAKE_CASE__   )
							data.pop(0   )  # This is for removing the labels from the list
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						np.matrix(SCREAMING_SNAKE_CASE__   )
							return dataset




def _a					(  SCREAMING_SNAKE_CASE__			:  List[str] ,      SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      SCREAMING_SNAKE_CASE__			:  List[str]   )       ->				List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						len_data

							SCREAMING_SNAKE_CASE__   :			Dict      =						np.dot(SCREAMING_SNAKE_CASE__ ,      data_x.transpose()   )
							prod -= data_y.transpose()
							SCREAMING_SNAKE_CASE__   :			Dict      =						np.dot(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						theta - (alpha / n) * sum_grad
							return theta




def _a					(  SCREAMING_SNAKE_CASE__			:  Any ,      SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  List[Any]   )       ->				Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[Any]      =						np.dot(SCREAMING_SNAKE_CASE__ ,      data_x.transpose()   )
							prod -= data_y.transpose()
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						np.sum(np.square(SCREAMING_SNAKE_CASE__   )   )
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						sum_elem / (2 * len_data)
							return error




def _a					(  SCREAMING_SNAKE_CASE__			:  int ,      SCREAMING_SNAKE_CASE__			:  Tuple   )       ->				Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[Any]      =						10_00_00
							SCREAMING_SNAKE_CASE__   :			Any      =						0.0_0_0_1_5_5_0

							SCREAMING_SNAKE_CASE__   :			Any      =						data_x.shape[1]
							SCREAMING_SNAKE_CASE__   :			Tuple      =						data_x.shape[0] - 1

							SCREAMING_SNAKE_CASE__   :			Tuple      =						np.zeros((1, no_features)   )

							for i in range(0 ,      SCREAMING_SNAKE_CASE__   ):
														SCREAMING_SNAKE_CASE__   :			str      =						run_steep_gradient_descent(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
														SCREAMING_SNAKE_CASE__   :			int      =						sum_of_square_error(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
														print(f'''At Iteration {i + 1} - Error is {error:.5f}'''   )

							return theta




def _a					(  SCREAMING_SNAKE_CASE__			:  List[str] ,      SCREAMING_SNAKE_CASE__			:  str   )       ->				int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Any      =						sum(abs(y - predicted_y[i]   ) for i, y in enumerate(SCREAMING_SNAKE_CASE__   )   )
							return total / len(SCREAMING_SNAKE_CASE__   )




def _a					(  )       ->				str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Tuple      =						collect_dataset()

							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						data.shape[0]
							SCREAMING_SNAKE_CASE__   :			Any      =						np.c_[np.ones(SCREAMING_SNAKE_CASE__   ), data[:, :-1]].astype(SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						data[:, -1].astype(SCREAMING_SNAKE_CASE__   )

							SCREAMING_SNAKE_CASE__   :			Tuple      =						run_linear_regression(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						theta.shape[1]
							print("Resultant Feature vector : "   )
							for i in range(0 ,      SCREAMING_SNAKE_CASE__   ):
														print(f'''{theta[0, i]:.5f}'''   )


if __name__ == "__main__":
		main()












from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
