
'''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


lowercase    :							int      =					logging.get_logger(__name__)


if is_vision_available():
					import PIL


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       ["""pixel_values"""]


							def __init__(       self    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = PILImageResampling.BICUBIC    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = 1 / 2_55    ,   lowerCAmelCase_ = True    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = True    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								super().__init__(**_UpperCamelCase  )
								_snake_case   						=		size if size is not None else {'shortest_edge': 2_24}
								_snake_case   						=		get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase  )
								_snake_case   						=		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								_snake_case   						=		get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase    ,   param_name='crop_size'  )

								_snake_case   						=		do_resize
								_snake_case   						=		size
								_snake_case   						=		resample
								_snake_case   						=		do_center_crop
								_snake_case   						=		crop_size
								_snake_case   						=		do_rescale
								_snake_case   						=		rescale_factor
								_snake_case   						=		do_normalize
								_snake_case   						=		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_snake_case   						=		image_std if image_std is not None else OPENAI_CLIP_STD
								_snake_case   						=		do_convert_rgb


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = PILImageResampling.BICUBIC    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase  )
								if "shortest_edge" not in size:
									raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'  )
								_snake_case   						=		get_resize_output_image_size(_UpperCamelCase    ,   size=size['shortest_edge']    ,   default_to_square=_UpperCamelCase  )
								return resize(_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		get_size_dict(_UpperCamelCase  )
								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(_UpperCamelCase    ,   size=(size['height'], size['width'])    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






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




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = None    ,   lowerCAmelCase_ = ChannelDimension.FIRST    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		do_resize if do_resize is not None else self.do_resize
								_snake_case   						=		size if size is not None else self.size
								_snake_case   						=		get_size_dict(_UpperCamelCase    ,   param_name='size'    ,   default_to_square=_UpperCamelCase  )
								_snake_case   						=		resample if resample is not None else self.resample
								_snake_case   						=		do_center_crop if do_center_crop is not None else self.do_center_crop
								_snake_case   						=		crop_size if crop_size is not None else self.crop_size
								_snake_case   						=		get_size_dict(_UpperCamelCase    ,   param_name='crop_size'    ,   default_to_square=_UpperCamelCase  )
								_snake_case   						=		do_rescale if do_rescale is not None else self.do_rescale
								_snake_case   						=		rescale_factor if rescale_factor is not None else self.rescale_factor
								_snake_case   						=		do_normalize if do_normalize is not None else self.do_normalize
								_snake_case   						=		image_mean if image_mean is not None else self.image_mean
								_snake_case   						=		image_std if image_std is not None else self.image_std
								_snake_case   						=		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_snake_case   						=		make_list_of_images(_UpperCamelCase  )

								if not valid_images(_UpperCamelCase  ):
									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:
									_snake_case   						=		[convert_to_rgb(_UpperCamelCase  ) for image in images]

								# All transformations expect numpy arrays.
								_snake_case   						=		[to_numpy_array(_UpperCamelCase  ) for image in images]

								if do_resize:
									_snake_case   						=		[self.resize(image=_UpperCamelCase    ,   size=_UpperCamelCase    ,   resample=_UpperCamelCase  ) for image in images]

								if do_center_crop:
									_snake_case   						=		[self.center_crop(image=_UpperCamelCase    ,   size=_UpperCamelCase  ) for image in images]

								if do_rescale:
									_snake_case   						=		[self.rescale(image=_UpperCamelCase    ,   scale=_UpperCamelCase  ) for image in images]

								if do_normalize:
									_snake_case   						=		[self.normalize(image=_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase  ) for image in images]

								_snake_case   						=		[to_channel_dimension_format(_UpperCamelCase    ,   _UpperCamelCase  ) for image in images]

								_snake_case   						=		{'pixel_values': images}
								return BatchFeature(data=_UpperCamelCase    ,   tensor_type=_UpperCamelCase  )








import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state='''closed''' )
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Add stale comment
                  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/accelerate/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )


if __name__ == "__main__":
       main()





