



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


a_								=       logging.get_logger(__name__)

a_								=       {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""dpr"""




 def __init__(    self						,					__UpperCAmelCase=30522						,					__UpperCAmelCase=768						,					__UpperCAmelCase=12						,					__UpperCAmelCase=12						,					__UpperCAmelCase=3072						,					__UpperCAmelCase="gelu"						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=0.1						,					__UpperCAmelCase=512						,					__UpperCAmelCase=2						,					__UpperCAmelCase=0.02						,					__UpperCAmelCase=1E-1_2						,					__UpperCAmelCase=0						,					__UpperCAmelCase="absolute"						,					__UpperCAmelCase = 0						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase						,					**__UpperCAmelCase					)

    __lowerCamelCase    				=     vocab_size
    __lowerCamelCase    				=     hidden_size
    __lowerCamelCase    				=     num_hidden_layers
    __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    				=     projection_dim
    __lowerCamelCase    				=     position_embedding_type











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      :			str			,_UpperCamelCase      :			List[Any]="train"				):
   return calculate_hypothesis_value(_UpperCamelCase			,_UpperCamelCase				) - output(
       _UpperCamelCase			,_UpperCamelCase				)





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





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





def 		a__      (		_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     summation_of_cost_derivative(_UpperCamelCase			,_UpperCamelCase				) / 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(_UpperCamelCase				)				):
         __lowerCamelCase    				=     get_cost_derivative(i - 1				)
         __lowerCamelCase    				=     (
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          _UpperCamelCase			,_UpperCamelCase			,atol=_UpperCamelCase			,rtol=_UpperCamelCase			,):
         break
      __lowerCamelCase    				=     temp_parameter_vector
   print(('''Number of iterations:''', j)				)





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


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






