



import numpy

# List of input, output pairs
a_			=					(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
a_			=					(((515, 22, 13), 555), ((61, 35, 49), 150))
a_			=					[2, 4, 1, 5]
a_			=					len(train_data)
a_			=					0.0_09





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Optional[int]="train"							):
   return calculate_hypothesis_value(a__ ,a__							) - output(
       a__ ,a__							)





def 		a__			(		_UpperCamelCase			: Any							):
   __lowerCamelCase													=    0
   for i in range(len(a__							) - 1							):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val





def 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: int							):






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





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: List[str]							):






   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 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Optional[Any]=m							):
   __lowerCamelCase													=    0
   for i in range(a__							):
      if index == -1:
         summation_value += _error(a__							)
      else:
         summation_value += _error(a__							) * train_data[i][0][index]
   return summation_value





def 		a__			(		_UpperCamelCase			: Tuple							):
   __lowerCamelCase													=    summation_of_cost_derivative(a__ ,a__							) / m
   return cost_derivative_value





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





def 		a__			(		):






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


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




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


a_			=					logging.get_logger(__name__)

a_			=					{
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """lxmert"""
 lowerCAmelCase__       					=  {}




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=12	,				__UpperCAmelCase=9500	,				__UpperCAmelCase=1600	,				__UpperCAmelCase=400	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=9	,				__UpperCAmelCase=5	,				__UpperCAmelCase=5	,				__UpperCAmelCase=2048	,				__UpperCAmelCase=4	,				__UpperCAmelCase=6.67	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    num_qa_labels
    __lowerCamelCase													=    num_object_labels
    __lowerCamelCase													=    num_attr_labels
    __lowerCamelCase													=    l_layers
    __lowerCamelCase													=    x_layers
    __lowerCamelCase													=    r_layers
    __lowerCamelCase													=    visual_feat_dim
    __lowerCamelCase													=    visual_pos_dim
    __lowerCamelCase													=    visual_loss_normalizer
    __lowerCamelCase													=    task_matched
    __lowerCamelCase													=    task_mask_lm
    __lowerCamelCase													=    task_obj_predict
    __lowerCamelCase													=    task_qa
    __lowerCamelCase													=    visual_obj_loss
    __lowerCamelCase													=    visual_attr_loss
    __lowerCamelCase													=    visual_feat_loss
    __lowerCamelCase													=    {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
    super().__init__(**__UpperCAmelCase				)






