


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


A   :					Tuple	=      logging.get_logger(__name__)


if is_vision_available():
    import PIL







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["pixel_values"]





       def __init__(     self							:    str      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Union[int, float] = 1 / 255      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    bool = True      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             lowerCamelCase__	:       int					=						size if size is not None else {"shortest_edge": 224}
             lowerCamelCase__	:       Optional[Any]					=						get_size_dict(__lowerCamelCase      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						crop_size if crop_size is not None else {"height": 224, "width": 224}
             lowerCamelCase__	:       Union[str, Any]					=						get_size_dict(__lowerCamelCase      ,   default_to_square=__lowerCamelCase      ,   param_name="crop_size" )

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





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    str      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						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__	:       Any					=						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							:    List[Any]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						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							:    List[str]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[int, float]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    List[str]      ,   ):


             '''simple docstring'''



             return rescale(__lowerCamelCase      ,   scale=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



             return normalize(__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    ImageInput      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    int = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    float = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase							:    Optional[ChannelDimension] = ChannelDimension.FIRST      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						do_resize if do_resize is not None else self.do_resize
             lowerCamelCase__	:       Dict					=						size if size is not None else self.size
             lowerCamelCase__	:       str					=						get_size_dict(__lowerCamelCase      ,   param_name="size"      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						resample if resample is not None else self.resample
             lowerCamelCase__	:       List[str]					=						do_center_crop if do_center_crop is not None else self.do_center_crop
             lowerCamelCase__	:       int					=						crop_size if crop_size is not None else self.crop_size
             lowerCamelCase__	:       List[str]					=						get_size_dict(__lowerCamelCase      ,   param_name="crop_size"      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						do_rescale if do_rescale is not None else self.do_rescale
             lowerCamelCase__	:       Dict					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             lowerCamelCase__	:       List[str]					=						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__	:       Optional[int]					=						image_std if image_std is not None else self.image_std
             lowerCamelCase__	:       Optional[Any]					=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowerCamelCase__	:       Optional[int]					=						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__	:       Tuple					=						[to_numpy_array(__lowerCamelCase ) for image in images]

             if do_resize:
                   lowerCamelCase__	:       int					=						[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[str]					=						[self.rescale(image=__lowerCamelCase      ,   scale=__lowerCamelCase ) for image in images]

             if do_normalize:
                   lowerCamelCase__	:       Any					=						[self.normalize(image=__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Optional[Any]					=						[to_channel_dimension_format(__lowerCamelCase      ,   __lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Dict					=						{"pixel_values": images}
             return BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )






import os
from datetime import datetime as dt

from github import Github


A   :					Union[str, Any]	=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						Github(os.environ["GITHUB_TOKEN"]		)
      lowerCamelCase__	:       str					=						g.get_repo("huggingface/diffusers"		)
      lowerCamelCase__	:       Optional[int]					=						repo.get_issues(state="open"		)

      for issue in open_issues:
            lowerCamelCase__	:       str					=						sorted(issue.get_comments()					,      key=lambda _A		: i.created_at					,      reverse=_A		)
            lowerCamelCase__	:       str					=						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()


