




import numpy

# List of input, output pairs
UpperCAmelCase_		:					Any          =  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase_		:					Tuple          =  (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase_		:					Dict          =  [2, 4, 1, 5]
UpperCAmelCase_		:					Optional[Any]          =  len(train_data)
UpperCAmelCase_		:					Optional[Any]          =  0.009




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase="train" ):
				return calculate_hypothesis_value(lowerCamelCase						,  lowerCamelCase ) - output(
				    lowerCamelCase						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Union[str, Any]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):


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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):


				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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=m ):
				__magic_name__						:						Tuple										=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 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Dict										=summation_of_cost_derivative(lowerCamelCase						,  lowerCamelCase ) / m
				return cost_derivative_value




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




def 		lowerCAmelCase_ (						):


				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()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)