





'''simple docstring'''







from datetime import datetime as dt
import os

from github import Github


snake_case_     :       Optional[int]						=  [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]





def        lowerCamelCase_      (  ) ->	Tuple:
						UpperCAmelCase_			:      str     						=  Github(os.environ['''GITHUB_TOKEN''']   )
						UpperCAmelCase_			:      Optional[Any]     						=  g.get_repo('''huggingface/transformers'''   )
						UpperCAmelCase_			:      Optional[int]     						=  repo.get_issues(state='''open'''   )

						for issue in open_issues:
												UpperCAmelCase_			:      Tuple     						=  sorted([comment for comment in issue.get_comments()],			key=lambda SCREAMING_SNAKE_CASE__   : i.created_at,			reverse=lowerCAmelCase__   )
												UpperCAmelCase_			:      Tuple     						=  comments[0] if len(lowerCAmelCase__   ) > 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()   )
												):
																		# 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 > 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()   )
												):
																		# 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()












'''simple docstring'''







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


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  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_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

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

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

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

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





