





'''simple docstring'''







import numpy

# List of input, output pairs
snake_case_     :       Dict						=  (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
snake_case_     :       Optional[int]						=  (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
snake_case_     :       Union[str, Any]						=  [2, 4, 1, 5]
snake_case_     :       Optional[Any]						=  len(train_data)
snake_case_     :       Tuple						=  0.009





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			str="train"   ) ->	Optional[int]:
						return calculate_hypothesis_value(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ) - output(
						    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple   ) ->	List[Any]:
						UpperCAmelCase_			:      Any     						=  0
						for i in range(len(SCREAMING_SNAKE_CASE__   ) - 1   ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			int   ) ->	Tuple:
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			str   ) ->	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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Dict=m   ) ->	Optional[int]:
						UpperCAmelCase_			:      Dict     						=  0
						for i in range(SCREAMING_SNAKE_CASE__   ):
												if index == -1:
																		summation_value += _error(SCREAMING_SNAKE_CASE__   )
												else:
																		summation_value += _error(SCREAMING_SNAKE_CASE__   ) * train_data[i][0][index]
						return summation_value





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	Any:
						UpperCAmelCase_			:      List[Any]     						=  summation_of_cost_derivative(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ) / m
						return cost_derivative_value





def        lowerCamelCase_      (  ) ->	List[str]:
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						UpperCAmelCase_			:      Optional[Any]     						=  0.00_00_02
						UpperCAmelCase_			:      Optional[int]     						=  0
						UpperCAmelCase_			:      Tuple     						=  0
						while True:
												j += 1
												UpperCAmelCase_			:      List[Any]     						=  [0, 0, 0, 0]
												for i in range(0,			len(SCREAMING_SNAKE_CASE__   )   ):
																		UpperCAmelCase_			:      int     						=  get_cost_derivative(i - 1   )
																		UpperCAmelCase_			:      List[Any]     						=  (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			atol=SCREAMING_SNAKE_CASE__,			rtol=SCREAMING_SNAKE_CASE__,			):
																		break
												UpperCAmelCase_			:      Any     						=  temp_parameter_vector
						print(('''Number of iterations:''', j)   )





def        lowerCamelCase_      (  ) ->	Optional[Any]:
						for i in range(len(SCREAMING_SNAKE_CASE__   )   ):
												print(('''Actual output value:''', output(SCREAMING_SNAKE_CASE__,			'''test'''   ))   )
												print(('''Hypothesis output:''', calculate_hypothesis_value(SCREAMING_SNAKE_CASE__,			'''test'''   ))   )


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












'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint


@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       List[Any]:







													"""simple docstring"""
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													UpperCAmelCase_			:      List[str]     						=  [[1, 2, 4], [1, 2, 3, 4]]
													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )
													self.assertTrue(isinstance(dc.token_ids       ,     __magic_name__   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Dict:







													"""simple docstring"""
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													UpperCAmelCase_			:      Tuple     						=  [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(__magic_name__   )  # fails here

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  [[1, 2, 3], [1, 2, 4]]

													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  dc.update(2   )
													UpperCAmelCase_			:      Optional[Any]     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(3   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is True and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]   )



							def     UpperCAmelCase__	(							self							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													UpperCAmelCase_			:      Tuple     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(4   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2, 4]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

													dc.reset()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 3   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 2   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.remaining() == 0   )
													self.assertTrue(dc.current_seq == [1, 2, 5]   )





