
"""simple docstring"""




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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BridgeTowerImageProcessor


class       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_ = True			,					A_ = None			,					A_ = 32			,					A_ = True			,					A_ = 1 / 255			,					A_ = True			,					A_ = True			,					A_ = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,					A_ = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,					A_ = True			,					A_=7			,					A_=30			,					A_=400			,					A_=3			,					)  ->						int:





         """simple docstring"""




         _lowercase:      Any   			=  parent
         _lowercase:      List[str]   			=  do_resize
         _lowercase:      str   			=  size if size is not None else {'''shortest_edge''': 288}
         _lowercase:      Union[str, Any]   			=  size_divisor
         _lowercase:      Optional[Any]   			=  do_rescale
         _lowercase:      Tuple   			=  rescale_factor
         _lowercase:      List[str]   			=  do_normalize
         _lowercase:      Tuple   			=  do_center_crop
         _lowercase:      Optional[int]   			=  image_mean
         _lowercase:      Optional[int]   			=  image_std
         _lowercase:      List[Any]   			=  do_pad
         _lowercase:      Optional[int]   			=  batch_size
         _lowercase:      List[Any]   			=  num_channels
         _lowercase:      List[str]   			=  min_resolution
         _lowercase:      int   			=  max_resolution
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



       def 				lowercase_				(  self			,					A_			,					A_=False							)  ->						List[Any]:





         """simple docstring"""




         if not batched:
           _lowercase:      List[str]   			=  self.size['''shortest_edge''']
           _lowercase:      Optional[int]   			=  image_inputs[0]
           if isinstance(A_			,					Image.Image							):
             _lowercase			,					_lowercase:      Any   			=  image.size
           else:
             _lowercase			,					_lowercase:      List[str]   			=  image.shape[1], image.shape[2]
           _lowercase:      int   			=  size / min(A_			,					A_							)
           if h < w:
             _lowercase			,					_lowercase:      Dict   			=  size, scale * w
           else:
             _lowercase			,					_lowercase:      Any   			=  scale * h, size

           _lowercase:      Optional[Any]   			=  int((1333 / 800) * size							)
           if max(A_			,					A_							) > max_size:
             _lowercase:      Optional[Any]   			=  max_size / max(A_			,					A_							)
             _lowercase:      Optional[int]   			=  newh * scale
             _lowercase:      List[Any]   			=  neww * scale

           _lowercase			,					_lowercase:      int   			=  int(newh + 0.5							), int(neww + 0.5							)
           _lowercase			,					_lowercase:      Dict   			=  (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

         else:
           _lowercase:      Tuple   			=  []
           for image in image_inputs:
             _lowercase			,					_lowercase:      str   			=  self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
           _lowercase:      Any   			=  max(A_			,					key=lambda A_							: item[0]							)[0]
           _lowercase:      Tuple   			=  max(A_			,					key=lambda A_							: item[1]							)[1]

         return expected_height, expected_width


@require_torch
@require_vision
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		BridgeTowerImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      List[Any]   			=  BridgeTowerImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      int   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
         self.assertTrue(hasattr(A_			,					'''size_divisor'''							)							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      List[str]   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					Image.Image							)

         # Test not batched input
         _lowercase:      str   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      Optional[Any]   			=  self.image_processor_tester.get_expected_values(A_							)
         self.assertEqual(
             encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

         # Test batched
         _lowercase:      str   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      int   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      Dict   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					numpify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					np.ndarray							)

         # Test not batched input
         _lowercase:      str   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      Dict   			=  self.image_processor_tester.get_expected_values(A_							)
         self.assertEqual(
             encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

         # Test batched
         _lowercase:      Union[str, Any]   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      List[str]   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      int   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      Any   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_			,					torchify=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					torch.Tensor							)

         # Test not batched input
         _lowercase:      str   			=  image_processing(image_inputs[0]			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      Optional[Any]   			=  self.image_processor_tester.get_expected_values(A_							)
         self.assertEqual(
             encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

         # Test batched
         _lowercase:      Optional[Any]   			=  image_processing(A_			,					return_tensors='''pt'''							).pixel_values

         _lowercase			,					_lowercase:      Union[str, Any]   			=  self.image_processor_tester.get_expected_values(A_			,					batched=A_							)
         self.assertEqual(
             encoded_images.shape			,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,					)


"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


A__ :       Tuple				= [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]




def 			_lowerCAmelCase		(		):






  """simple docstring"""



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

  for issue in open_issues:
    _lowercase:      List[Any]   			=  sorted(issue.get_comments()			,					key=lambda _UpperCamelCase    : i.created_at			,					reverse=_UpperCamelCase    )
    _lowercase:      Optional[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()    )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''    )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''    )
      issue.remove_from_labels('''stale'''    )
    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()    )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      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/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''    )
      issue.add_to_labels('''stale'''    )


if __name__ == "__main__":
  main()
