


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


__lowerCamelCase        =     logging.get_logger(__name__)


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     Union[str, Any]							    =     ["""pixel_values"""]

       def __init__(   self      :     Optional[int]      ,							lowerCamelCase_      :     List[str] = True      ,							lowerCamelCase_      :     Tuple = 3_2      ,							lowerCamelCase_      :     Optional[Any]=PILImageResampling.BILINEAR      ,							lowerCamelCase_      :     Dict = True      ,							**lowerCamelCase_      :     Optional[int]      ,							):
            a_    :     Optional[int]										=    do_resize
            a_    :     int										=    do_rescale
            a_    :     Union[str, Any]										=    size_divisor
            a_    :     int										=    resample
            super().__init__(**__lowerCAmelCase    )

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any] = None      ,							**lowerCamelCase_      :     str    ):
            a_			,			a_    :     Dict										=    get_image_size(__lowerCAmelCase    )
            # Rounds the height and width down to the closest multiple of size_divisor
            a_    :     Optional[int]										=    height // size_divisor * size_divisor
            a_    :     Union[str, Any]										=    width // size_divisor * size_divisor
            a_    :     Union[str, Any]										=    resize(__lowerCAmelCase      ,							(new_h, new_w)      ,							resample=__lowerCAmelCase      ,							data_format=__lowerCAmelCase      ,							**__lowerCAmelCase    )
            return image

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     int = None      ,							**lowerCamelCase_      :     Dict    ):
            return rescale(image=__lowerCAmelCase      ,							scale=__lowerCAmelCase      ,							data_format=__lowerCAmelCase      ,							**__lowerCAmelCase    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     Optional[Any] = None      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     Optional[Any] = None      ,							lowerCamelCase_      :     List[str] = ChannelDimension.FIRST      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     Union[str, Any]										=    do_resize if do_resize is not None else self.do_resize
            a_    :     Dict										=    do_rescale if do_rescale is not None else self.do_rescale
            a_    :     Dict										=    size_divisor if size_divisor is not None else self.size_divisor
            a_    :     Optional[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"""    )

            a_    :     Optional[Any]										=    make_list_of_images(__lowerCAmelCase    )

            if not valid_images(__lowerCAmelCase    ):
                 raise ValueError("""Invalid image(s)"""    )

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

            if do_resize:
                 a_    :     str										=    [self.resize(__lowerCAmelCase      ,							size_divisor=__lowerCAmelCase      ,							resample=__lowerCAmelCase    ) for image in images]

            if do_rescale:
                 a_    :     List[str]										=    [self.rescale(__lowerCAmelCase      ,							scale=1 / 2_5_5    ) for image in images]

            a_    :     str										=    [to_channel_dimension_format(__lowerCAmelCase      ,							__lowerCAmelCase    ) for image in images]

            a_    :     Dict										=    {"""pixel_values""": images}
            return BatchFeature(data=__lowerCAmelCase      ,							tensor_type=__lowerCAmelCase    )







from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 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 >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
               issue.edit(state="""closed""" )
          elif (
              (dt.utcnow() - issue.updated_at).days > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would add stale comment to {issue.number}")
               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/transformers/blob/main/CONTRIBUTING.md) """
                   """are likely to be ignored.""" )


if __name__ == "__main__":
       main()



