import numpy

# List of input, output pairs
lowerCamelCase_    :		Dict							=	(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCamelCase_    :		List[str]							=	(((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase_    :		Optional[int]							=	[2, 4, 1, 5]
lowerCamelCase_    :		Dict							=	len(train_data)
lowerCamelCase_    :		Tuple							=	0.0_0_9
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase="train"						):
					return calculate_hypothesis_value(__lowerCamelCase				,    __lowerCamelCase						) - output(
					    __lowerCamelCase				,    __lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       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				,    __lowerCamelCase						):
					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				,    __lowerCamelCase						):
					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				,    __lowerCamelCase=m						):
					__a		 =       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						):
					__a		 =       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
					__a		 =       0.00_00_02
					__a		 =       0
					__a		 =       0
					while True:
										j += 1
										__a		 =       [0, 0, 0, 0]
										for i in range(0				,    len(__lowerCamelCase						)						):
															__a		 =       get_cost_derivative(i - 1						)
															__a		 =       (
															    parameter_vector[i] - LEARNING_RATE * cost_derivative
															)
										if numpy.allclose(
										    __lowerCamelCase				,    __lowerCamelCase				,    atol=__lowerCamelCase				,    rtol=__lowerCamelCase				,    ):
															break
										__a		 =       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 typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




