


from datetime import datetime as dt
import os

from github import Github


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




def  a   ():
   __a					=  Github(os.environ["""GITHUB_TOKEN"""]   )
   __a					=  g.get_repo("""huggingface/transformers"""   )
   __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=UpperCamelCase__   )
      __a					=  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()









from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__lowerCamelCase									=       image_std if image_std is not None else IMAGENET_STANDARD_STD


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
							return center_crop(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( 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__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase									=       size if size is not None else self.size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowerCamelCase									=       do_normalize if do_normalize is not None else self.do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else self.image_mean
							__lowerCamelCase									=       image_std if image_std is not None else self.image_std

							__lowerCamelCase									=       make_list_of_images(lowerCamelCase__				)

							if not valid_images(lowerCamelCase__				):
											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.'				)

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

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

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

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

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

							__lowerCamelCase									=       [to_channel_dimension_format(lowerCamelCase__     ,		lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(
															    'Make sure that you pass in as many target sizes as the batch dimension of the logits'				)

											if is_torch_tensor(lowerCamelCase__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

											for idx in range(len(lowerCamelCase__				)				):
															__lowerCamelCase									=       torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0				)     ,		size=target_sizes[idx]     ,		mode='bilinear'     ,		align_corners=lowerCamelCase__				)
															__lowerCamelCase									=       resized_logits[0].argmax(dim=0				)
															semantic_segmentation.append(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       logits.argmax(dim=1				)
											__lowerCamelCase									=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

							return semantic_segmentation


