





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase									=   logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		lowerCamelCase	(   _A							):
	snake_case_   = ['''pixel_values''']



	def __init__( self		,   a_ = True		,   a_ = None		,   a_ = PILImageResampling.BICUBIC		,   a_ = True		,   a_ = None		,   a_ = True		,   a_ = 1 / 255		,   a_ = True		,   a_ = None		,   a_ = None		,   a_ = True		,   **a_		,   ):
			super().__init__(**lowerCamelCase_						)
			lowerCAmelCase  :     str       						= size if size is not None else {'''shortest_edge''': 224}
			lowerCAmelCase  :     Dict       						= get_size_dict(lowerCamelCase_		,   default_to_square=lowerCamelCase_						)
			lowerCAmelCase  :     Dict       						= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase  :     Optional[int]       						= get_size_dict(lowerCamelCase_		,   default_to_square=lowerCamelCase_		,   param_name="crop_size"						)

			lowerCAmelCase  :     Optional[Any]       						= do_resize
			lowerCAmelCase  :     Optional[int]       						= size
			lowerCAmelCase  :     List[str]       						= resample
			lowerCAmelCase  :     Optional[Any]       						= do_center_crop
			lowerCAmelCase  :     List[str]       						= crop_size
			lowerCAmelCase  :     List[Any]       						= do_rescale
			lowerCAmelCase  :     List[str]       						= rescale_factor
			lowerCAmelCase  :     Union[str, Any]       						= do_normalize
			lowerCAmelCase  :     str       						= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
			lowerCAmelCase  :     Optional[int]       						= image_std if image_std is not None else OPENAI_CLIP_STD
			lowerCAmelCase  :     Optional[Any]       						= do_convert_rgb



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = PILImageResampling.BICUBIC		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     Tuple       						= get_size_dict(lowerCamelCase_		,   default_to_square=lowerCamelCase_						)
			if "shortest_edge" not in size:
					raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
			lowerCAmelCase  :     Tuple       						= get_resize_output_image_size(lowerCamelCase_		,   size=size["shortest_edge"]		,   default_to_square=lowerCamelCase_						)
			return resize(lowerCamelCase_		,   size=lowerCamelCase_		,   resample=lowerCamelCase_		,   data_format=lowerCamelCase_		,   **lowerCamelCase_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     Optional[int]       						= get_size_dict(lowerCamelCase_						)
			if "height" not in size or "width" not in size:
					raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
			return center_crop(lowerCamelCase_		,   size=(size["height"], size["width"])		,   data_format=lowerCamelCase_		,   **lowerCamelCase_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return rescale(lowerCamelCase_		,   scale=lowerCamelCase_		,   data_format=lowerCamelCase_		,   **lowerCamelCase_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return normalize(lowerCamelCase_		,   mean=lowerCamelCase_		,   std=lowerCamelCase_		,   data_format=lowerCamelCase_		,   **lowerCamelCase_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = ChannelDimension.FIRST		,   **a_		,   ):
			lowerCAmelCase  :     Any       						= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase  :     Any       						= size if size is not None else self.size
			lowerCAmelCase  :     int       						= get_size_dict(lowerCamelCase_		,   param_name="size"		,   default_to_square=lowerCamelCase_						)
			lowerCAmelCase  :     Tuple       						= resample if resample is not None else self.resample
			lowerCAmelCase  :     str       						= do_center_crop if do_center_crop is not None else self.do_center_crop
			lowerCAmelCase  :     Any       						= crop_size if crop_size is not None else self.crop_size
			lowerCAmelCase  :     Optional[int]       						= get_size_dict(lowerCamelCase_		,   param_name="crop_size"		,   default_to_square=lowerCamelCase_						)
			lowerCAmelCase  :     List[str]       						= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase  :     str       						= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase  :     List[Any]       						= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase  :     Tuple       						= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase  :     Dict       						= image_std if image_std is not None else self.image_std
			lowerCAmelCase  :     int       						= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

			lowerCAmelCase  :     Optional[Any]       						= make_list_of_images(lowerCamelCase_						)

			if not valid_images(lowerCamelCase_						):
					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."						)

			# PIL RGBA images are converted to RGB
			if do_convert_rgb:
					lowerCAmelCase  :     Union[str, Any]       						= [convert_to_rgb(lowerCamelCase_						) for image in images]

			# All transformations expect numpy arrays.
			lowerCAmelCase  :     Optional[int]       						= [to_numpy_array(lowerCamelCase_						) for image in images]

			if do_resize:
					lowerCAmelCase  :     List[Any]       						= [self.resize(image=lowerCamelCase_		,   size=lowerCamelCase_		,   resample=lowerCamelCase_						) for image in images]

			if do_center_crop:
					lowerCAmelCase  :     Any       						= [self.center_crop(image=lowerCamelCase_		,   size=lowerCamelCase_						) for image in images]

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

			if do_normalize:
					lowerCAmelCase  :     int       						= [self.normalize(image=lowerCamelCase_		,   mean=lowerCamelCase_		,   std=lowerCamelCase_						) for image in images]

			lowerCAmelCase  :     Optional[int]       						= [to_channel_dimension_format(lowerCamelCase_		,   lowerCamelCase_						) for image in images]

			lowerCAmelCase  :     Any       						= {'''pixel_values''': images}
			return BatchFeature(data=lowerCamelCase_		,   tensor_type=lowerCamelCase_						)




'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


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







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

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




