






'''simple docstring'''
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        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
	snake_case_												=							42
	snake_case_												=							42
	snake_case_												=							None






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase			):
	snake_case_												=							2




	@register_to_config
	def __init__( self		:							List[str]   , __lowercase		:							float = 0.02   , __lowercase		:							float = 1_00   , __lowercase		:							float = 1.007   , __lowercase		:							float = 80   , __lowercase		:							float = 0.05   , __lowercase		:							float = 50   , )		->  Optional[int]:
							# standard deviation of the initial noise distribution
							SCREAMING_SNAKE_CASE__       :			List[Any]													=sigma_max

							# setable values
							SCREAMING_SNAKE_CASE__       :			Dict													=None
							SCREAMING_SNAKE_CASE__       :			Dict													=None
							SCREAMING_SNAKE_CASE__       :			int													=None  # sigma(t_i)




	def        __magic_name__		( self		:							Any   , __lowercase		:							torch.FloatTensor   , __lowercase		:							Optional[int] = None )		->  torch.FloatTensor:
							return sample




	def        __magic_name__		( self		:							Union[str, Any]   , __lowercase		:							int   , __lowercase		:							Union[str, torch.device] = None )		->  List[Any]:
							SCREAMING_SNAKE_CASE__       :			Optional[int]													=num_inference_steps
							SCREAMING_SNAKE_CASE__       :			int													=np.arange(0   , self.num_inference_steps )[::-1].copy()
							SCREAMING_SNAKE_CASE__       :			int													=torch.from_numpy(__A ).to(__A )
							SCREAMING_SNAKE_CASE__       :			Tuple													=[
							    (
							        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
							]
							SCREAMING_SNAKE_CASE__       :			Any													=torch.tensor(__A   , dtype=torch.floataa   , device=__A )




	def        __magic_name__		( self		:							List[Any]   , __lowercase		:							torch.FloatTensor   , __lowercase		:							float   , __lowercase		:							Optional[torch.Generator] = None )		->  Tuple[torch.FloatTensor, float]:
							if self.config.s_min <= sigma <= self.config.s_max:
													SCREAMING_SNAKE_CASE__       :			List[Any]													=min(self.config.s_churn / self.num_inference_steps   , 2**0.5 - 1 )
							else:
													SCREAMING_SNAKE_CASE__       :			Any													=0

							# sample eps ~ N(0, S_noise^2 * I)
							SCREAMING_SNAKE_CASE__       :			Any													=self.config.s_noise * randn_tensor(sample.shape   , generator=__A ).to(sample.device )
							SCREAMING_SNAKE_CASE__       :			Tuple													=sigma + gamma * sigma
							SCREAMING_SNAKE_CASE__       :			int													=sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat




	def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							torch.FloatTensor   , __lowercase		:							float   , __lowercase		:							float   , __lowercase		:							torch.FloatTensor   , __lowercase		:							bool = True   , )		->  Union[KarrasVeOutput, Tuple]:
							SCREAMING_SNAKE_CASE__       :			Optional[Any]													=sample_hat + sigma_hat * model_output
							SCREAMING_SNAKE_CASE__       :			Dict													=(sample_hat - pred_original_sample) / sigma_hat
							SCREAMING_SNAKE_CASE__       :			Any													=sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

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




	def        __magic_name__		( self		:							Dict   , __lowercase		:							torch.FloatTensor   , __lowercase		:							float   , __lowercase		:							float   , __lowercase		:							torch.FloatTensor   , __lowercase		:							torch.FloatTensor   , __lowercase		:							torch.FloatTensor   , __lowercase		:							bool = True   , )		->  Union[KarrasVeOutput, Tuple]:
							SCREAMING_SNAKE_CASE__       :			List[Any]													=sample_prev + sigma_prev * model_output
							SCREAMING_SNAKE_CASE__       :			List[str]													=(sample_prev - pred_original_sample) / sigma_prev
							SCREAMING_SNAKE_CASE__       :			Any													=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=__A   , derivative=__A   , pred_original_sample=__A )




	def        __magic_name__		( self		:							Any   , __lowercase		:							List[Any]   , __lowercase		:							int   , __lowercase		:							int )		->  int:
							raise NotImplementedError()







'''simple docstring'''
from datetime import datetime as dt
import os

from github import Github


a_					=	[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=Github(os.environ['''GITHUB_TOKEN''']							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=g.get_repo('''huggingface/transformers'''							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=repo.get_issues(state='''open'''							)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=sorted([comment for comment in issue.get_comments()],		key=lambda UpperCamelCase__							: i.created_at,		reverse=UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Dict													=comments[0] if len(UpperCamelCase__							) > 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 >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='''closed'''							)
            elif (
                (dt.utcnow() - issue.updated_at).days > 2_3
                and (dt.utcnow() - issue.created_at).days >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.'''							)


if __name__ == "__main__":
  main()