

"""simple docstring"""




import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor







class      __lowercase			:

  def __init__(  self		:			str					,A		:			Tuple					,A		:			Optional[int]=100					,A		:			Any=13					,A		:			Optional[Any]=30					,A		:			Dict=2					,A		:			Any=3					,A		:			Any=True					,A		:			Any=True					,A		:			str=32					,A		:			str=4					,A		:			Tuple=4					,A		:			Any=37					,A		:			Optional[int]="gelu"					,A		:			Union[str, Any]=0.1					,A		:			Union[str, Any]=0.1					,A		:			List[str]=10					,A		:			str=0.0_2					,A		:			Any=3					,A		:			Tuple=None					,A		:			List[Any]=[0, 1, 2, 3]					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					parent
   UpperCAmelCase__					:  List[str]					   =					100
   UpperCAmelCase__					:  Any					   =					batch_size
   UpperCAmelCase__					:  Any					   =					image_size
   UpperCAmelCase__					:  Any					   =					patch_size
   UpperCAmelCase__					:  Optional[Any]					   =					num_channels
   UpperCAmelCase__					:  List[str]					   =					is_training
   UpperCAmelCase__					:  Any					   =					use_labels
   UpperCAmelCase__					:  Tuple					   =					hidden_size
   UpperCAmelCase__					:  Tuple					   =					num_hidden_layers
   UpperCAmelCase__					:  List[Any]					   =					num_attention_heads
   UpperCAmelCase__					:  List[str]					   =					intermediate_size
   UpperCAmelCase__					:  Union[str, Any]					   =					hidden_act
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_dropout_prob
   UpperCAmelCase__					:  int					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  Optional[Any]					   =					type_sequence_label_size
   UpperCAmelCase__					:  Optional[Any]					   =					initializer_range
   UpperCAmelCase__					:  int					   =					scope
   UpperCAmelCase__					:  str					   =					out_indices
   UpperCAmelCase__					:  Tuple					   =					num_labels

   # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   UpperCAmelCase__					:  int					   =					(image_size // patch_size) ** 2
   UpperCAmelCase__					:  Any					   =					num_patches + 1

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

   UpperCAmelCase__					:  Optional[int]					   =					None
   UpperCAmelCase__					:  Union[str, Any]					   =					None
   if self.use_labels:
    UpperCAmelCase__					:  Dict					   =					ids_tensor([self.batch_size]					,self.type_sequence_label_size    )
    UpperCAmelCase__					:  Dict					   =					ids_tensor([self.batch_size, self.image_size, self.image_size]					,self.num_labels    )

   UpperCAmelCase__					:  str					   =					self.get_config()

   return config, pixel_values, labels, pixel_labels

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   return BeitConfig(
       vocab_size=self.vocab_size					,image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,hidden_size=self.hidden_size					,num_hidden_layers=self.num_hidden_layers					,num_attention_heads=self.num_attention_heads					,intermediate_size=self.intermediate_size					,hidden_act=self.hidden_act					,hidden_dropout_prob=self.hidden_dropout_prob					,attention_probs_dropout_prob=self.attention_probs_dropout_prob					,is_decoder=A					,initializer_range=self.initializer_range					,out_indices=self.out_indices					,)

  def    __lowercase       (  self		:			Optional[Any]					,A		:			int					,A		:			Union[str, Any]					,A		:			Union[str, Any]					,A		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					BeitModel(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[int]					   =					model(A    )
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )

  def    __lowercase       (  self		:			List[Any]					,A		:			Dict					,A		:			List[Any]					,A		:			str					,A		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					BeitForMaskedImageModeling(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[int]					   =					model(A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length - 1, self.vocab_size)    )

  def    __lowercase       (  self		:			Dict					,A		:			Union[str, Any]					,A		:			Any					,A		:			List[str]					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.type_sequence_label_size
   UpperCAmelCase__					:  Any					   =					BeitForImageClassification(A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[int]					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )

   # test greyscale images
   UpperCAmelCase__					:  List[Any]					   =					1
   UpperCAmelCase__					:  List[str]					   =					BeitForImageClassification(A    )
   model.to(A    )
   model.eval()

   UpperCAmelCase__					:  List[str]					   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  Union[str, Any]					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )

  def    __lowercase       (  self		:			List[Any]					,A		:			str					,A		:			int					,A		:			Optional[int]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.num_labels
   UpperCAmelCase__					:  int					   =					BeitForSemanticSegmentation(A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Dict					   =					model(A    )
   self.parent.assertEqual(
       result.logits.shape					,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
   UpperCAmelCase__					:  int					   =					model(A					,labels=A    )
   self.parent.assertEqual(
       result.logits.shape					,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )




  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					self.prepare_config_and_inputs()
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					config_and_inputs
   UpperCAmelCase__					:  List[Any]					   =					{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (
      (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  snake_case_				       =       (
      {
          """feature-extraction""": BeitModel,
          """image-classification""": BeitForImageClassification,
          """image-segmentation""": BeitForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					BeitModelTester(self    )
   UpperCAmelCase__					:  Optional[Any]					   =					ConfigTester(self					,config_class=A					,has_text_modality=A					,hidden_size=37    )

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  @unittest.skip(reason="""BEiT does not use inputs_embeds"""    )
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   pass

  @require_torch_multi_gpu
  @unittest.skip(reason="""BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""    )
  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  List[Any]					   =					model_class(A    )
    self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)    )
    UpperCAmelCase__					:  Optional[Any]					   =					model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(A					,nn.Linear    )    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  Optional[int]					   =					model_class(A    )
    UpperCAmelCase__					:  Tuple					   =					inspect.signature(model.forward    )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase__					:  List[str]					   =					[*signature.parameters.keys()]

    UpperCAmelCase__					:  Union[str, Any]					   =					["""pixel_values"""]
    self.assertListEqual(arg_names[:1]					,A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   if not self.model_tester.is_training:
    return

   UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__					:  Optional[Any]					   =					True

   for model_class in self.all_model_classes:
    # we don't test BeitForMaskedImageModeling
    if model_class in [*get_values(A    ), BeitForMaskedImageModeling]:
     continue

    UpperCAmelCase__					:  str					   =					model_class(A    )
    model.to(A    )
    model.train()
    UpperCAmelCase__					:  Optional[int]					   =					self._prepare_for_class(A					,A					,return_labels=A    )
    UpperCAmelCase__					:  int					   =					model(**A    ).loss
    loss.backward()

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
    return

   UpperCAmelCase__					:  Any					   =					False
   UpperCAmelCase__					:  List[str]					   =					True

   for model_class in self.all_model_classes:
    # we don't test BeitForMaskedImageModeling
    if (
        model_class in [*get_values(A    ), BeitForMaskedImageModeling]
        or not model_class.supports_gradient_checkpointing
    ):
     continue

    UpperCAmelCase__					:  Optional[int]					   =					model_class(A    )
    model.gradient_checkpointing_enable()
    model.to(A    )
    model.train()
    UpperCAmelCase__					:  int					   =					self._prepare_for_class(A					,A					,return_labels=A    )
    UpperCAmelCase__					:  Union[str, Any]					   =					model(**A    ).loss
    loss.backward()

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__					:  Union[str, Any]					   =					_config_zero_init(A    )
   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  List[str]					   =					model_class(config=A    )
    for name, param in model.named_parameters():
     # we skip lambda parameters as these require special initial values
     # determined by config.layer_scale_init_value
     if "lambda" in name:
      continue
     if param.requires_grad:
      self.assertIn(
          ((param.data.mean() * 1e9).round() / 1e9).item()					,[0.0, 1.0]					,msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,)




  @slow
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__					:  List[Any]					   =					BeitModel.from_pretrained(A    )
    self.assertIsNotNone(A    )







def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  List[str]					   =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
 return image







@require_torch
@require_vision
class      __lowercase			(    unittest.TestCase					):

  @cached_property
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""    ) if is_vision_available() else None

  @slow
  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""    ).to(A    )

   UpperCAmelCase__					:  Optional[Any]					   =					self.default_image_processor
   UpperCAmelCase__					:  List[str]					   =					prepare_img()
   UpperCAmelCase__					:  List[str]					   =					image_processor(images=A					,return_tensors="""pt"""    ).pixel_values.to(A    )

   # prepare bool_masked_pos
   UpperCAmelCase__					:  Dict					   =					torch.ones((1, 196)					,dtype=torch.bool    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  Any					   =					model(pixel_values=A					,bool_masked_pos=A    )
   UpperCAmelCase__					:  Optional[int]					   =					outputs.logits

   # verify the logits
   UpperCAmelCase__					:  List[Any]					   =					torch.Size((1, 196, 8_192)    )
   self.assertEqual(logits.shape					,A    )

   UpperCAmelCase__					:  int					   =					torch.tensor(
       [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]    ).to(A    )

   self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]					,A					,atol=1e-2    )    )

  @slow
  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""    ).to(A    )

   UpperCAmelCase__					:  Any					   =					self.default_image_processor
   UpperCAmelCase__					:  List[str]					   =					prepare_img()
   UpperCAmelCase__					:  Optional[Any]					   =					image_processor(images=A					,return_tensors="""pt"""    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  int					   =					model(**A    )
   UpperCAmelCase__					:  Tuple					   =					outputs.logits

   # verify the logits
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.Size((1, 1_000)    )
   self.assertEqual(logits.shape					,A    )

   UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]    ).to(A    )

   self.assertTrue(torch.allclose(logits[0, :3]					,A					,atol=1e-4    )    )

   UpperCAmelCase__					:  Optional[Any]					   =					281
   self.assertEqual(logits.argmax(-1    ).item()					,A    )

  @slow
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""    ).to(
       A    )

   UpperCAmelCase__					:  List[str]					   =					self.default_image_processor
   UpperCAmelCase__					:  List[str]					   =					prepare_img()
   UpperCAmelCase__					:  Optional[int]					   =					image_processor(images=A					,return_tensors="""pt"""    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  List[str]					   =					model(**A    )
   UpperCAmelCase__					:  List[Any]					   =					outputs.logits

   # verify the logits
   UpperCAmelCase__					:  List[str]					   =					torch.Size((1, 21_841)    )
   self.assertEqual(logits.shape					,A    )

   UpperCAmelCase__					:  Optional[Any]					   =					torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]    ).to(A    )

   self.assertTrue(torch.allclose(logits[0, :3]					,A					,atol=1e-4    )    )

   UpperCAmelCase__					:  Tuple					   =					2_396
   self.assertEqual(logits.argmax(-1    ).item()					,A    )

  @slow
  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					model.to(A    )

   UpperCAmelCase__					:  Optional[Any]					   =					BeitImageProcessor(do_resize=A					,size=640					,do_center_crop=A    )

   UpperCAmelCase__					:  List[Any]					   =					load_dataset("""hf-internal-testing/fixtures_ade20k"""					,split="""test"""    )
   UpperCAmelCase__					:  str					   =					Image.open(ds[0]["""file"""]    )
   UpperCAmelCase__					:  Optional[Any]					   =					image_processor(images=A					,return_tensors="""pt"""    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  List[str]					   =					model(**A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					outputs.logits

   # verify the logits
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.Size((1, 150, 160, 160)    )
   self.assertEqual(logits.shape					,A    )

   UpperCAmelCase__					:  str					   =					version.parse(PIL.__version__    ) < version.parse("""9.0.0"""    )

   if is_pillow_less_than_a:
    UpperCAmelCase__					:  Dict					   =					torch.tensor(
        [
            [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
            [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
            [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
        ]					,device=A					,)
   else:
    UpperCAmelCase__					:  List[str]					   =					torch.tensor(
        [
            [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
            [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
            [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
        ]					,device=A					,)

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3]					,A					,atol=1e-4    )    )




  @slow
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""    )
   UpperCAmelCase__					:  str					   =					model.to(A    )

   UpperCAmelCase__					:  int					   =					BeitImageProcessor(do_resize=A					,size=640					,do_center_crop=A    )

   UpperCAmelCase__					:  Union[str, Any]					   =					load_dataset("""hf-internal-testing/fixtures_ade20k"""					,split="""test"""    )
   UpperCAmelCase__					:  Dict					   =					Image.open(ds[0]["""file"""]    )
   UpperCAmelCase__					:  Any					   =					image_processor(images=A					,return_tensors="""pt"""    ).to(A    )

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__					:  Dict					   =					model(**A    )

   UpperCAmelCase__					:  int					   =					outputs.logits.detach().cpu()

   UpperCAmelCase__					:  List[Any]					   =					image_processor.post_process_semantic_segmentation(outputs=A					,target_sizes=[(500, 300)]    )
   UpperCAmelCase__					:  Dict					   =					torch.Size((500, 300)    )
   self.assertEqual(segmentation[0].shape					,A    )

   UpperCAmelCase__					:  List[str]					   =					image_processor.post_process_semantic_segmentation(outputs=A    )
   UpperCAmelCase__					:  List[str]					   =					torch.Size((160, 160)    )
   self.assertEqual(segmentation[0].shape					,A    )








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  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()





