import os
from datetime import datetime as dt

from github import Github


__lowercase							 =					[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]



def lowerCamelCase  (   ):
    '''simple docstring'''







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

    for issue in open_issues:
        __UpperCamelCase     :Any			  =		sorted([comment for comment in issue.get_comments()]  ,			key=lambda SCREAMING_SNAKE_CASE	: i.created_at  ,			reverse=SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Optional[Any]			  =		comments[0] if len(SCREAMING_SNAKE_CASE	) > 0 else None
        __UpperCamelCase     :Union[str, Any]			  =		dt.utcnow()
        __UpperCamelCase     :Tuple			  =		(current_time - issue.updated_at).days
        __UpperCamelCase     :str			  =		(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()






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

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

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





