



import numpy

# List of input, output pairs
__snake_case       :	Tuple            =(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
__snake_case       :	Tuple            =(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
__snake_case       :	List[Any]            =[2, 4, 1, 5]
__snake_case       :	str            =len(train_data)
__snake_case       :	Optional[Any]            =0.009






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	Tuple="train"):





				'''simple docstring'''

				return calculate_hypothesis_value(lowerCamelCase_ ,lowerCamelCase_) - output(
				    lowerCamelCase_ ,lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  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_	:	Union[str, Any] ,lowerCamelCase_	:	Tuple):





				'''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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Union[str, Any]):





				'''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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Union[str, Any]=m):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  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_	:	List[str]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  summation_of_cost_derivative(lowerCamelCase_ ,lowerCamelCase_) / m
				return cost_derivative_value






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				global parameter_vector
				# Tune these values to set a tolerance value for predicted output
				lowerCAmelCase__  :       List[Any]				=  0.000002
				lowerCAmelCase__  :       Dict				=  0
				lowerCAmelCase__  :       Optional[Any]				=  0
				while True:
								j += 1
								lowerCAmelCase__  :       Any				=  [0, 0, 0, 0]
								for i in range(0 ,len(lowerCamelCase_)):
												lowerCAmelCase__  :       Any				=  get_cost_derivative(i - 1)
												lowerCAmelCase__  :       Any				=  (
												    parameter_vector[i] - LEARNING_RATE * cost_derivative
												)
								if numpy.allclose(
								    lowerCamelCase_ ,lowerCamelCase_ ,atol=lowerCamelCase_ ,rtol=lowerCamelCase_ ,):
												break
								lowerCAmelCase__  :       int				=  temp_parameter_vector
				print(('''Number of iterations:''', j))






def 			lowerCAmelCase__	(					):





				'''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()






import warnings

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


__snake_case       :	List[Any]            =logging.get_logger(__name__)

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

