
"""simple docstring"""


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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__="train"						):
			return calculate_hypothesis_value(lowerCAmelCase__	,lowerCAmelCase__						) - output(
			    lowerCAmelCase__	,lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  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  lowercase						(	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  lowercase						(	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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=m						):
			lowerCamelCase_					=  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  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  summation_of_cost_derivative(lowerCAmelCase__	,lowerCAmelCase__						) / m
			return cost_derivative_value






def  lowercase						(	):
			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(lowerCAmelCase__						)						):
									lowerCamelCase_					=  get_cost_derivative(i - 1						)
									lowerCamelCase_					=  (
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    lowerCAmelCase__	,lowerCAmelCase__	,atol=lowerCAmelCase__	,rtol=lowerCAmelCase__	,):
									break
						lowerCamelCase_					=  temp_parameter_vector
			print(('''Number of iterations:''', j)						)






def  lowercase						(	):
			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()





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



