



import numpy

# List of input, output pairs
__lowercase       :Tuple       =  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__lowercase       :Tuple       =  (((515, 22, 13), 555), ((61, 35, 49), 150))
__lowercase       :List[Any]       =  [2, 4, 1, 5]
__lowercase       :str       =  len(train_data)
__lowercase       :Optional[Any]       =  0.0_0_9







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  Tuple="train"     ):





						'''simple docstring'''


						return calculate_hypothesis_value(lowerCamelCase_			,   lowerCamelCase_     ) - output(
						    lowerCamelCase_			,   lowerCamelCase_     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					0
						for i in range(len(lowerCamelCase_     ) - 1     ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Tuple     ):





						'''simple docstring'''


						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]			,   _lowerCamelCase      :  Union[str, Any]     ):





						'''simple docstring'''


						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]     )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]     )
						return None







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]			,   _lowerCamelCase      :  Union[str, Any]=m     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						for i in range(lowerCamelCase_     ):
												if index == -1:
																		summation_value += _error(lowerCamelCase_     )
												else:
																		summation_value += _error(lowerCamelCase_     ) * train_data[i][0][index]
						return summation_value







def       UpperCAmelCase							(     _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					summation_of_cost_derivative(lowerCamelCase_			,   lowerCamelCase_     ) / m
						return cost_derivative_value







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0.0_0_0_0_0_2
						SCREAMING_SNAKE_CASE__     :				Dict   =					0
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						while True:
												j += 1
												SCREAMING_SNAKE_CASE__     :				Any   =					[0, 0, 0, 0]
												for i in range(0			,   len(lowerCamelCase_     )     ):
																		SCREAMING_SNAKE_CASE__     :				Any   =					get_cost_derivative(i - 1     )
																		SCREAMING_SNAKE_CASE__     :				Any   =					(
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    lowerCamelCase_			,   lowerCamelCase_			,   atol=lowerCamelCase_			,   rtol=lowerCamelCase_			,   ):
																		break
												SCREAMING_SNAKE_CASE__     :				int   =					temp_parameter_vector
						print(("Number of iterations:", j)     )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						for i in range(len(lowerCamelCase_     )     ):
												print(("Actual output value:", output(lowerCamelCase_			,   "test"     ))     )
												print(("Hypothesis output:", calculate_hypothesis_value(lowerCamelCase_			,   "test"     ))     )


if __name__ == "__main__":
				run_gradient_descent()
				print("\nTesting gradient descent for a linear hypothesis function.\n")
				test_gradient_descent()






import warnings

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



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "tokenizer"]
							snake_case_							 =						"CLIPImageProcessor"
							snake_case_							 =						("CLIPTokenizer", "CLIPTokenizerFast")

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

													SCREAMING_SNAKE_CASE__     :				int   =					image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."    )
													if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."    )

													super().__init__(a							,	a    )

							def __call__(      self				:     Tuple							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	a				:     List[str]=None							,	**a				:     Optional[Any]    )	->Optional[Any]:

													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:
																			SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(a							,	return_tensors=a							,	**a    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.image_processor(a							,	return_tensors=a							,	**a    )

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

							def      A_  (      self				:     Optional[int]							,	*a				:     Any							,	**a				:     List[str]    )	->Any:
													return self.tokenizer.batch_decode(*a							,	**a    )

							def      A_  (      self				:     Any							,	*a				:     Optional[int]							,	**a				:     Dict    )	->Any:
													return self.tokenizer.decode(*a							,	**a    )

							@property
							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,	a							,	)
													return self.image_processor_class






							@property
							def      A_  (      self				:     Dict    )	->str:
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,	a							,	)
													return self.image_processor