



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
class     UpperCAmelCase_       (			_SCREAMING_SNAKE_CASE):
							lowerCamelCase__       =						42
							lowerCamelCase__       =						42
							lowerCamelCase__       =						None



class     UpperCAmelCase_       (			_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE):
							lowerCamelCase__       =						2







							@register_to_config
							def __init__(  self,     __a = 0.02,     __a = 100,     __a = 1.007,     __a = 80,     __a = 0.05,     __a = 50,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[int]					  =       sigma_max

														# setable values
														_lowerCAmelCase	:				List[str]					  =       None
														_lowerCAmelCase	:				str					  =       None
														_lowerCAmelCase	:				Tuple					  =       None  # sigma(t_i)







							def   snake_case__ (  self,     __a,     __a = None):

														'''simple docstring'''

														return sample







							def   snake_case__ (  self,     __a,     __a = None):

														'''simple docstring'''

														_lowerCAmelCase	:				List[str]					  =       num_inference_steps
														_lowerCAmelCase	:				int					  =       np.arange(0,     self.num_inference_steps)[::-1].copy()
														_lowerCAmelCase	:				Optional[Any]					  =       torch.from_numpy(_lowercase).to(_lowercase)
														_lowerCAmelCase	:				List[Any]					  =       [
														    (
														        self.config.sigma_max**2
														        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
														    )
														    for i in self.timesteps
														]
														_lowerCAmelCase	:				List[str]					  =       torch.tensor(_lowercase,     dtype=torch.floataa,     device=_lowercase)







							def   snake_case__ (  self,     __a,     __a,     __a = None):

														'''simple docstring'''

														if self.config.s_min <= sigma <= self.config.s_max:
																					_lowerCAmelCase	:				Union[str, Any]					  =       min(self.config.s_churn / self.num_inference_steps,     2**0.5 - 1)
														else:
																					_lowerCAmelCase	:				Tuple					  =       0

														# sample eps ~ N(0, S_noise^2 * I)
														_lowerCAmelCase	:				str					  =       self.config.s_noise * randn_tensor(sample.shape,     generator=_lowercase).to(sample.device)
														_lowerCAmelCase	:				int					  =       sigma + gamma * sigma
														_lowerCAmelCase	:				Optional[Any]					  =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

														return sample_hat, sigma_hat







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a = True,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				Dict					  =       sample_hat + sigma_hat * model_output
														_lowerCAmelCase	:				int					  =       (sample_hat - pred_original_sample) / sigma_hat
														_lowerCAmelCase	:				str					  =       sample_hat + (sigma_prev - sigma_hat) * derivative

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=_lowercase,     derivative=_lowercase,     pred_original_sample=_lowercase)







							def   snake_case__ (  self,     __a,     __a,     __a,     __a,     __a,     __a,     __a = True,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				Optional[int]					  =       sample_prev + sigma_prev * model_output
														_lowerCAmelCase	:				int					  =       (sample_prev - pred_original_sample) / sigma_prev
														_lowerCAmelCase	:				Tuple					  =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

														if not return_dict:
																					return (sample_prev, derivative)

														return KarrasVeOutput(
														    prev_sample=_lowercase,     derivative=_lowercase,     pred_original_sample=_lowercase)







							def   snake_case__ (  self,     __a,     __a,     __a):

														'''simple docstring'''

														raise NotImplementedError()


'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


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


def A ():
						_lowerCAmelCase    		=					Github(os.environ["""GITHUB_TOKEN"""]						)
						_lowerCAmelCase    		=					g.get_repo("""huggingface/diffusers"""						)
						_lowerCAmelCase    		=					repo.get_issues(state="""open"""						)

						for issue in open_issues:
												_lowerCAmelCase    		=					sorted(issue.get_comments()					,      key=lambda __lowerCamelCase						: i.created_at					,      reverse=__lowerCamelCase						)
												_lowerCAmelCase    		=					comments[0] if len(__lowerCamelCase						) > 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()
