


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


A   :					List[Any]	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					np.ndarray					,      _A    :					Union[int, Iterable[int]]					,      _A    :					bool					,      _A    :					int		):

      """simple docstring"""




      def constraint_to_multiple_of(_A    :					Optional[Any]					,      _A    :					Optional[int]					,      _A    :					Any=0					,      _A    :					int=None		):
            lowerCamelCase__	:       Tuple					=						round(val / multiple		) * multiple

            if max_val is not None and x > max_val:
                  lowerCamelCase__	:       str					=						math.floor(val / multiple		) * multiple

            if x < min_val:
                  lowerCamelCase__	:       Any					=						math.ceil(val / multiple		) * multiple

            return x

      lowerCamelCase__	:       int					=						(output_size, output_size) if isinstance(_A					,      _A		) else output_size

      lowerCamelCase__	:       int					=						get_image_size(_A		)
      lowerCamelCase__	:       List[str]					=						output_size

      # determine new height and width
      lowerCamelCase__	:       int					=						output_height / input_height
      lowerCamelCase__	:       int					=						output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width		) < abs(1 - scale_height		):
                  # fit width
                  lowerCamelCase__	:       Tuple					=						scale_width
            else:
                  # fit height
                  lowerCamelCase__	:       Optional[int]					=						scale_height

      lowerCamelCase__	:       Any					=						constraint_to_multiple_of(scale_height * input_height					,      multiple=_A		)
      lowerCamelCase__	:       Tuple					=						constraint_to_multiple_of(scale_width * input_width					,      multiple=_A		)

      return (new_height, new_width)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["pixel_values"]





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BILINEAR      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    int = 1      ,   __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							:    List[str]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						size if size is not None else {"height": 384, "width": 384}
             lowerCamelCase__	:       Any					=						get_size_dict(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						do_resize
             lowerCamelCase__	:       int					=						size
             lowerCamelCase__	:       str					=						keep_aspect_ratio
             lowerCamelCase__	:       Tuple					=						ensure_multiple_of
             lowerCamelCase__	:       Union[str, Any]					=						resample
             lowerCamelCase__	:       int					=						do_rescale
             lowerCamelCase__	:       int					=						rescale_factor
             lowerCamelCase__	:       List[Any]					=						do_normalize
             lowerCamelCase__	:       str					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowerCamelCase__	:       int					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						get_size_dict(__lowerCamelCase )
             if "height" not in size or "width" not in size:
                   raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
             lowerCamelCase__	:       List[Any]					=						get_resize_output_image_size(
                 __lowerCamelCase      ,   output_size=(size["height"], size["width"])      ,   keep_aspect_ratio=__lowerCamelCase      ,   multiple=__lowerCamelCase      ,   )
             return resize(__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





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


             '''simple docstring'''



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





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


             '''simple docstring'''



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





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


             '''simple docstring'''



             lowerCamelCase__	:       str					=						do_resize if do_resize is not None else self.do_resize
             lowerCamelCase__	:       Tuple					=						size if size is not None else self.size
             lowerCamelCase__	:       Dict					=						get_size_dict(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
             lowerCamelCase__	:       Optional[Any]					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
             lowerCamelCase__	:       int					=						resample if resample is not None else self.resample
             lowerCamelCase__	:       Union[str, Any]					=						do_rescale if do_rescale is not None else self.do_rescale
             lowerCamelCase__	:       int					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             lowerCamelCase__	:       Dict					=						do_normalize if do_normalize is not None else self.do_normalize
             lowerCamelCase__	:       Optional[Any]					=						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__	:       List[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 or resample is None:
                   raise ValueError("Size and resample must be specified if do_resize 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.
             lowerCamelCase__	:       Any					=						[to_numpy_array(__lowerCamelCase ) for image in images]

             if do_resize:
                   lowerCamelCase__	:       Tuple					=						[self.resize(image=__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase ) for image in images]

             if do_rescale:
                   lowerCamelCase__	:       Dict					=						[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__	:       str					=						[to_channel_dimension_format(__lowerCamelCase      ,   __lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Optional[Any]					=						{"pixel_values": images}
             return BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Tuple] = None ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						outputs.logits

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

                   if is_torch_tensor(__lowerCamelCase ):
                         lowerCamelCase__	:       Any					=						target_sizes.numpy()

                   lowerCamelCase__	:       Optional[Any]					=						[]

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

             return semantic_segmentation






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()


