





'''simple docstring'''






from datetime import datetime as dt
import os

from github import Github


snake_case_     =	[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]
def 							_lowerCamelCase(  )		->  Any:
    A       :       int							=						Github(os.environ['''GITHUB_TOKEN''']		)
    A       :       Optional[int]							=						g.get_repo('''huggingface/transformers'''		)
    A       :       Union[str, Any]							=						repo.get_issues(state='''open'''		)

    for issue in open_issues:
        A       :       Dict							=						sorted([comment for comment in issue.get_comments()]    ,       key=lambda UpperCamelCase__		: i.created_at    ,       reverse=UpperCamelCase__		)
        A       :       Union[str, 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 >= 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 Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

snake_case_     =	logging.get_logger(__name__)
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  List[List[ImageInput]]:
    if isinstance(UpperCamelCase__    ,       (list, tuple)		) and isinstance(videos[0]    ,       (list, tuple)		) and is_valid_image(videos[0][0]		):
        return videos

    elif isinstance(UpperCamelCase__    ,       (list, tuple)		) and is_valid_image(videos[0]		):
        return [videos]

    elif is_valid_image(UpperCamelCase__		):
        return [[videos]]

    raise ValueError(F'''Could not make batched video from {videos}'''		)





class   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""pixel_values"""]


   def __init__( self    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1 / 255    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)
       A       :       int							=						size if size is not None else {'''shortest_edge''': 256}
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       List[Any]							=						crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       A       :       List[Any]							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

       A       :       Dict							=						do_resize
       A       :       Union[str, Any]							=						size
       A       :       int							=						do_center_crop
       A       :       Union[str, Any]							=						crop_size
       A       :       int							=						resample
       A       :       Any							=						do_rescale
       A       :       Optional[Any]							=						rescale_factor
       A       :       Any							=						offset
       A       :       int							=						do_normalize
       A       :       Optional[Any]							=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A       :       str							=						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = PILImageResampling.BILINEAR    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       str							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       if "shortest_edge" in size:
           A       :       Union[str, Any]							=						get_resize_output_image_size(_UpperCAmelCase    ,   size['''shortest_edge''']    ,   default_to_square=_UpperCAmelCase					)
       elif "height" in size and "width" in size:
           A       :       Optional[Any]							=						(size['''height'''], size['''width'''])
       else:
           raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
       return resize(_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						get_size_dict(_UpperCAmelCase					)
       if "height" not in size or "width" not in size:
           raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
       return center_crop(_UpperCAmelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Dict							=						image.astype(np.floataa					)
       if offset:
           A       :       List[str]							=						image - (scale / 2)
       return rescale(_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       return normalize(_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   ):
       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_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.'''					)

       if offset and not do_rescale:
           raise ValueError('''For offset, do_rescale must also be set to True.'''					)

       # All transformations expect numpy arrays.
       A       :       Dict							=						to_numpy_array(_UpperCAmelCase					)

       if do_resize:
           A       :       int							=						self.resize(image=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase					)

       if do_center_crop:
           A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase    ,   size=_UpperCAmelCase					)

       if do_rescale:
           A       :       int							=						self.rescale(image=_UpperCAmelCase    ,   scale=_UpperCAmelCase    ,   offset=_UpperCAmelCase					)

       if do_normalize:
           A       :       List[Any]							=						self.normalize(image=_UpperCAmelCase    ,   mean=_UpperCAmelCase    ,   std=_UpperCAmelCase					)

       A       :       Optional[int]							=						to_channel_dimension_format(_UpperCAmelCase    ,   _UpperCAmelCase					)
       return image


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = ChannelDimension.FIRST    ,   **_UpperCAmelCase    ,   ):
       A       :       Union[str, Any]							=						do_resize if do_resize is not None else self.do_resize
       A       :       Optional[Any]							=						resample if resample is not None else self.resample
       A       :       List[str]							=						do_center_crop if do_center_crop is not None else self.do_center_crop
       A       :       Union[str, Any]							=						do_rescale if do_rescale is not None else self.do_rescale
       A       :       Dict							=						rescale_factor if rescale_factor is not None else self.rescale_factor
       A       :       List[Any]							=						offset if offset is not None else self.offset
       A       :       str							=						do_normalize if do_normalize is not None else self.do_normalize
       A       :       List[str]							=						image_mean if image_mean is not None else self.image_mean
       A       :       Optional[Any]							=						image_std if image_std is not None else self.image_std

       A       :       List[Any]							=						size if size is not None else self.size
       A       :       Any							=						get_size_dict(_UpperCAmelCase    ,   default_to_square=_UpperCAmelCase					)
       A       :       Optional[int]							=						crop_size if crop_size is not None else self.crop_size
       A       :       int							=						get_size_dict(_UpperCAmelCase    ,   param_name='''crop_size'''					)

       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.'''					)

       A       :       List[Any]							=						make_batched(_UpperCAmelCase					)

       A       :       List[Any]							=						[
           [
               self._preprocess_image(
                   image=_UpperCAmelCase    ,   do_resize=_UpperCAmelCase    ,   size=_UpperCAmelCase    ,   resample=_UpperCAmelCase    ,   do_center_crop=_UpperCAmelCase    ,   crop_size=_UpperCAmelCase    ,   do_rescale=_UpperCAmelCase    ,   rescale_factor=_UpperCAmelCase    ,   offset=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   image_mean=_UpperCAmelCase    ,   image_std=_UpperCAmelCase    ,   data_format=_UpperCAmelCase    ,   )
               for img in video
           ]
           for video in videos
       ]

       A       :       Any							=						{'''pixel_values''': videos}
       return BatchFeature(data=_UpperCAmelCase    ,   tensor_type=_UpperCAmelCase					)
