
import numpy

# List of input, output pairs
UpperCAmelCase	      = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	      = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	      = [2, 4, 1, 5]
UpperCAmelCase	      = len(train_data)
UpperCAmelCase	      = 0.009






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE="train"							):
	return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) - output(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=m							):
	lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    summation_of_cost_derivative(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) / m
	return cost_derivative_value






def 							UpperCAmelCase_		(							):
	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	lowercase       =    0.00_00_02
	lowercase       =    0
	lowercase       =    0
	while True:
		j += 1
		lowercase       =    [0, 0, 0, 0]
		for i in range(0 ,							len(__SCREAMING_SNAKE_CASE							)							):
			lowercase       =    get_cost_derivative(i - 1							)
			lowercase       =    (
			    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
		lowercase       =    temp_parameter_vector
	print(('Number of iterations:', j)							)






def 							UpperCAmelCase_		(							):
	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()



import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase	      = [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]






def 							UpperCAmelCase_		(							):
	lowercase       =    Github(os.environ['GITHUB_TOKEN']							)
	lowercase       =    g.get_repo('huggingface/diffusers'							)
	lowercase       =    repo.get_issues(state='open'							)

	for issue in open_issues:
		lowercase       =    sorted(issue.get_comments() ,							key=lambda __SCREAMING_SNAKE_CASE							: i.created_at ,							reverse=__SCREAMING_SNAKE_CASE							)
		lowercase       =    comments[0] if len(__SCREAMING_SNAKE_CASE							) > 0 else None
		if (
		    last_comment is not None
		    and last_comment.user.login == "github-actions[bot]"
		    and (dt.utcnow() - issue.updated_at).days > 7
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Closes the issue after 7 days of inactivity since the Stalebot notification.
			issue.edit(state='closed'							)
		elif (
		    "stale" in issue.get_labels()
		    and last_comment is not None
		    and last_comment.user.login != "github-actions[bot]"
		):
			# Opens the issue if someone other than Stalebot commented.
			issue.edit(state='open'							)
			issue.remove_from_labels('stale'							)
		elif (
		    (dt.utcnow() - issue.updated_at).days > 23
		    and (dt.utcnow() - issue.created_at).days >= 30
		    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
		):
			# Post a Stalebot notification after 23 days of inactivity.
			issue.create_comment(
			    'This issue has been automatically marked as stale because it has not had '
			    'recent activity. If you think this still needs to be addressed '
			    'please comment on this thread.\n\nPlease note that issues that do not follow the '
			    '[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '
			    'are likely to be ignored.'							)
			issue.add_to_labels('stale'							)


if __name__ == "__main__":
	main()

