


'''simple docstring'''







from ....configuration_utils import PretrainedConfig
from ....utils import logging


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{
    'Visual-Attention-Network/van-base': (
        'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
    ),
}


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												="""van"""

  def __init__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							int=2_2_4				,							SCREAMING_SNAKE_CASE__     :							List[str]=3				,							SCREAMING_SNAKE_CASE__     :							str=[7, 3, 3, 3]				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=[4, 2, 2, 2]				,							SCREAMING_SNAKE_CASE__     :							Dict=[6_4, 1_2_8, 3_2_0, 5_1_2]				,							SCREAMING_SNAKE_CASE__     :							Dict=[3, 3, 1_2, 3]				,							SCREAMING_SNAKE_CASE__     :							List[str]=[8, 8, 4, 4]				,							SCREAMING_SNAKE_CASE__     :							List[Any]="gelu"				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.0_2				,							SCREAMING_SNAKE_CASE__     :							Any=1E-6				,							SCREAMING_SNAKE_CASE__     :							List[Any]=1E-2				,							SCREAMING_SNAKE_CASE__     :							int=0.0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=0.0				,							**SCREAMING_SNAKE_CASE__     :							Dict				,							):





       '''simple docstring'''

       super().__init__(**_SCREAMING_SNAKE_CASE						)
       __a       =   image_size
       __a       =   num_channels
       __a       =   patch_sizes
       __a       =   strides
       __a       =   hidden_sizes
       __a       =   depths
       __a       =   mlp_ratios
       __a       =   hidden_act
       __a       =   initializer_range
       __a       =   layer_norm_eps
       __a       =   layer_scale_init_value
       __a       =   drop_path_rate
       __a       =   dropout_rate










'''simple docstring'''







import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_      =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_      =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_      =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_      =			len(train_data)
SCREAMING_SNAKE_CASE_      =			0.0_0_9




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="train"		)				->					Optional[int]:
     """simple docstring"""




     return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) - output(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=m		)				->					Optional[Any]:
     """simple docstring"""




     __a       =   0
     for i in range(__SCREAMING_SNAKE_CASE		):
          if index == -1:
               summation_value += _error(__SCREAMING_SNAKE_CASE		)
          else:
               summation_value += _error(__SCREAMING_SNAKE_CASE		) * train_data[i][0][index]
     return summation_value




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   summation_of_cost_derivative(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) / m
     return cost_derivative_value




def        __lowercase						(      )				->					str:
     """simple docstring"""




     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __a       =   0.000_002
     __a       =   0
     __a       =   0
     while True:
          j += 1
          __a       =   [0, 0, 0, 0]
          for i in range(0				,  len(__SCREAMING_SNAKE_CASE		)		):
               __a       =   get_cost_derivative(i - 1		)
               __a       =   (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  atol=__SCREAMING_SNAKE_CASE				,  rtol=__SCREAMING_SNAKE_CASE				,  ):
               break
          __a       =   temp_parameter_vector
     print(("""Number of iterations:""", j)		)




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




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


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






