




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[str]									= [2, 4, 1, 5]
lowercase						:      Tuple									= len(train_data)
lowercase						:      Optional[Any]									= 0.0_0_9




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Any="train")      ->					Tuple:






							'''simple docstring'''





							return calculate_hypothesis_value(_lowerCamelCase		,						_lowerCamelCase) - output(
							    _lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    List[Any])      ->					List[str]:






							'''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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple)      ->					List[str]:






							'''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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Optional[Any]=m)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      summation_of_cost_derivative(_lowerCamelCase		,						_lowerCamelCase) / m
							return cost_derivative_value




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[int]:






							'''simple docstring'''





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




def 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''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()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)