






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

from github import Github


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





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=Github(os.environ['''GITHUB_TOKEN''']							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=g.get_repo('''huggingface/diffusers'''							)
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=repo.get_issues(state='''open'''							)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE__       :			Any													=sorted(issue.get_comments(),		key=lambda UpperCamelCase__							: i.created_at,		reverse=UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			str													=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()							)
            ):
                  # 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 > 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()							)
            ):
                  # 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()







'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       if "height" not in size or "width" not in size:
             raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
       return center_crop(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							str   , )		->  np.ndarray:
       return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=make_list_of_images(__lowercase )

       if not valid_images(__lowercase ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

       if do_resize and size is None:
             raise ValueError('''Size must be specified if do_resize is True.''' )

       if do_center_crop and crop_size is None:
             raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[self.resize(image=__lowercase   , size=__lowercase   , resample=__lowercase ) for image in images]

       if do_center_crop:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.center_crop(image=__lowercase   , size=__lowercase ) for image in images]

       if do_rescale:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

       if do_normalize:
             SCREAMING_SNAKE_CASE__       :			Tuple													=[self.normalize(image=__lowercase   , mean=__lowercase   , std=__lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(__lowercase ) != len(__lowercase ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

             if is_torch_tensor(__lowercase ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

             for idx in range(len(__lowercase ) ):
                   SCREAMING_SNAKE_CASE__       :			Any													=torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0 )   , size=target_sizes[idx]   , mode='''bilinear'''   , align_corners=__lowercase )
                   SCREAMING_SNAKE_CASE__       :			List[str]													=resized_logits[0].argmax(dim=0 )
                   semantic_segmentation.append(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Any													=logits.argmax(dim=1 )
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation