




"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'AutoImageProcessor'
						_a        =  'AutoTokenizer'


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Union[str, Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""  ,  lowerCAmelCase  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("""You have to specify an image_processor."""   )
										if tokenizer is None:
														raise ValueError("""You have to specify a tokenizer."""   )

										super().__init__(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Optional[int]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     str=None  ,  **lowerCAmelCase			:     Tuple   ):

										if text is None and images is None:
														raise ValueError("""You have to specify either text or images. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if images is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and images is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[Any]  ,  *lowerCAmelCase			:     Optional[Any]  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     int  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     Optional[int]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.tokenizer.model_input_names
										lowerCAmelCase		=			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


						@property
						def 					__lowercase	(							self			:     Optional[Any]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor_class




						@property
						def 					__lowercase	(							self			:     Optional[int]   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor











"""simple docstring"""







import numpy as np
import requests





def 							lowercase   ()       ->					Optional[int]:


				'''simple docstring'''






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





def 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	int ,    snake_case__				:	List[str] ,    snake_case__				:	Tuple ,    snake_case__				:	Union[str, Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			len_data

				lowerCAmelCase		=			np.dot(snake_case__ ,    data_x.transpose()	)
				prod -= data_y.transpose()
				lowerCAmelCase		=			np.dot(snake_case__ ,    snake_case__	)
				lowerCAmelCase		=			theta - (alpha / n) * sum_grad
				return theta





def 							lowercase   (snake_case__				:	Any ,    snake_case__				:	int ,    snake_case__				:	Any ,    snake_case__				:	Dict	)       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			np.dot(snake_case__ ,    data_x.transpose()	)
				prod -= data_y.transpose()
				lowerCAmelCase		=			np.sum(np.square(snake_case__	)	)
				lowerCAmelCase		=			sum_elem / (2 * len_data)
				return error





def 							lowercase   (snake_case__				:	Optional[Any] ,    snake_case__				:	List[Any]	)       ->					str:


				'''simple docstring'''






				lowerCAmelCase		=			100_000
				lowerCAmelCase		=			0.0_001_550

				lowerCAmelCase		=			data_x.shape[1]
				lowerCAmelCase		=			data_x.shape[0] - 1

				lowerCAmelCase		=			np.zeros((1, no_features)	)

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

				return theta





def 							lowercase   (snake_case__				:	List[str] ,    snake_case__				:	Optional[int]	)       ->					List[str]:


				'''simple docstring'''






				lowerCAmelCase		=			sum(abs(y - predicted_y[i]	) for i, y in enumerate(snake_case__	)	)
				return total / len(snake_case__	)





def 							lowercase   ()       ->					Optional[int]:


				'''simple docstring'''






				lowerCAmelCase		=			collect_dataset()

				lowerCAmelCase		=			data.shape[0]
				lowerCAmelCase		=			np.c_[np.ones(snake_case__	), data[:, :-1]].astype(snake_case__	)
				lowerCAmelCase		=			data[:, -1].astype(snake_case__	)

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


if __name__ == "__main__":
		main()





