

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_     =			0.009

def        lowerCamelCase__       (    _a							,	_a="train"):
	return calculate_hypothesis_value(lowerCAmelCase_							,	lowerCAmelCase_) - output(
	    lowerCAmelCase_							,	lowerCAmelCase_)

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				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__       (    _a							,	_a):
	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None

def        lowerCamelCase__       (    _a							,	_a):
	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__       (    _a							,	_a=m):
	SCREAMING_SNAKE_CASE      :					List[str]    =				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__       (    _a):
	SCREAMING_SNAKE_CASE      :					int    =				summation_of_cost_derivative(lowerCAmelCase_							,	lowerCAmelCase_) / m
	return cost_derivative_value

def        lowerCamelCase__       (    ):
	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	SCREAMING_SNAKE_CASE      :					str    =				0.00_0002
	SCREAMING_SNAKE_CASE      :					List[str]    =				0
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0
	while True:
		j += 1
		SCREAMING_SNAKE_CASE      :					Dict    =				[0, 0, 0, 0]
		for i in range(0							,	len(lowerCAmelCase_)):
			SCREAMING_SNAKE_CASE      :					Tuple    =				get_cost_derivative(i - 1)
			SCREAMING_SNAKE_CASE      :					Any    =				(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    lowerCAmelCase_							,	lowerCAmelCase_							,	atol=lowerCAmelCase_							,	rtol=lowerCAmelCase_							,	):
			break
		SCREAMING_SNAKE_CASE      :					Any    =				temp_parameter_vector
	print(("Number of iterations:", j))

def        lowerCamelCase__       (    ):
	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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase	:     Dict   ={
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							"""ernie_m"""
						SCREAMING_SNAKE_CASE__					:     Dict[str, str]    =							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




						def __init__( self      ,  __lowercase = 2_5_0_0_0_2      ,  __lowercase = 7_6_8      ,  __lowercase = 1_2      ,  __lowercase = 1_2      ,  __lowercase = 3_0_7_2      ,  __lowercase = "gelu"      ,  __lowercase = 0.1      ,  __lowercase = 0.1      ,  __lowercase = 5_1_4      ,  __lowercase = 0.02      ,  __lowercase = 1      ,  __lowercase = 1e-05      ,  __lowercase=None      ,  __lowercase=False      ,  __lowercase=0.0      ,  **__lowercase      ,  )    ->			Tuple:
										super().__init__(pad_token_id=__lowercase      ,  **__lowercase							)
										lowerCAmelCase_       :					Tuple			 = vocab_size
										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Tuple			 = num_hidden_layers
										lowerCAmelCase_       :					int			 = num_attention_heads
										lowerCAmelCase_       :					Dict			 = intermediate_size
										lowerCAmelCase_       :					int			 = hidden_act
										lowerCAmelCase_       :					Union[str, Any]			 = hidden_dropout_prob
										lowerCAmelCase_       :					Any			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					Union[str, Any]			 = max_position_embeddings
										lowerCAmelCase_       :					str			 = initializer_range
										lowerCAmelCase_       :					List[str]			 = layer_norm_eps
										lowerCAmelCase_       :					List[Any]			 = classifier_dropout
										lowerCAmelCase_       :					Any			 = is_decoder
										lowerCAmelCase_       :					List[Any]			 = act_dropout