



"""simple docstring"""






import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


__snake_case							:   Dict      =    1e-4

if is_torch_available():
	import torch

	from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
	from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder



@require_torch
class 			A__   :




	'''simple docstring'''





	def __init__(     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Dict=16       ,			_SCREAMING_SNAKE_CASE:		Dict=13       ,			_SCREAMING_SNAKE_CASE:		int=7       ,			_SCREAMING_SNAKE_CASE:		Any=14       ,			_SCREAMING_SNAKE_CASE:		int=10       ,			_SCREAMING_SNAKE_CASE:		Any=19       ,			_SCREAMING_SNAKE_CASE:		int=5       ,			_SCREAMING_SNAKE_CASE:		Any=4       ,			_SCREAMING_SNAKE_CASE:		Tuple=True       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=16       ,			_SCREAMING_SNAKE_CASE:		List[str]=2       ,			_SCREAMING_SNAKE_CASE:		Any=4       ,			_SCREAMING_SNAKE_CASE:		List[Any]=4       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]="gelu"       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=0.1       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.1       ,			_SCREAMING_SNAKE_CASE:		Tuple=[1, 2, 3, 4, 5]       ,			_SCREAMING_SNAKE_CASE:		str=25       ,			_SCREAMING_SNAKE_CASE:		Any=5       ,			)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       d_model
				__lowerCAmelCase      :  Optional[Any]												=       parent
				__lowerCAmelCase      :  Dict												=       batch_size
				__lowerCAmelCase      :  List[Any]												=       prediction_length
				__lowerCAmelCase      :  Optional[Any]												=       context_length
				__lowerCAmelCase      :  Optional[Any]												=       cardinality
				__lowerCAmelCase      :  int												=       num_time_features
				__lowerCAmelCase      :  Optional[int]												=       lags_sequence
				__lowerCAmelCase      :  Optional[Any]												=       embedding_dimension
				__lowerCAmelCase      :  List[Any]												=       is_training
				__lowerCAmelCase      :  Dict												=       hidden_size
				__lowerCAmelCase      :  Optional[Any]												=       num_hidden_layers
				__lowerCAmelCase      :  Union[str, Any]												=       num_attention_heads
				__lowerCAmelCase      :  Union[str, Any]												=       intermediate_size
				__lowerCAmelCase      :  Dict												=       hidden_act
				__lowerCAmelCase      :  int												=       hidden_dropout_prob
				__lowerCAmelCase      :  str												=       attention_probs_dropout_prob

				__lowerCAmelCase      :  Union[str, Any]												=       context_length
				__lowerCAmelCase      :  List[Any]												=       prediction_length + label_length
				__lowerCAmelCase      :  Tuple												=       label_length

				__lowerCAmelCase      :  str												=       moving_average
				__lowerCAmelCase      :  List[str]												=       autocorrelation_factor

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[Any]:

				"""simple docstring"""

				return AutoformerConfig(
				    d_model=self.d_model       ,			encoder_layers=self.num_hidden_layers       ,			decoder_layers=self.num_hidden_layers       ,			encoder_attention_heads=self.num_attention_heads       ,			decoder_attention_heads=self.num_attention_heads       ,			encoder_ffn_dim=self.intermediate_size       ,			decoder_ffn_dim=self.intermediate_size       ,			dropout=self.hidden_dropout_prob       ,			attention_dropout=self.attention_probs_dropout_prob       ,			prediction_length=self.prediction_length       ,			context_length=self.context_length       ,			label_length=self.label_length       ,			lags_sequence=self.lags_sequence       ,			num_time_features=self.num_time_features       ,			num_static_categorical_features=1       ,			cardinality=[self.cardinality]       ,			embedding_dimension=[self.embedding_dimension]       ,			moving_average=self.moving_average       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       config.context_length + max(config.lags_sequence)

				__lowerCAmelCase      :  Dict												=       ids_tensor([self.batch_size, 1]       ,			config.cardinality[0])
				__lowerCAmelCase      :  Optional[Any]												=       floats_tensor([self.batch_size, _past_length, config.num_time_features])
				__lowerCAmelCase      :  Optional[Any]												=       floats_tensor([self.batch_size, _past_length])
				__lowerCAmelCase      :  Tuple												=       floats_tensor([self.batch_size, _past_length]) > 0.5

				# decoder inputs
				__lowerCAmelCase      :  Dict												=       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
				__lowerCAmelCase      :  int												=       floats_tensor([self.batch_size, config.prediction_length])

				__lowerCAmelCase      :  Union[str, Any]												=       {
				    "past_values": past_values,
				    "static_categorical_features": static_categorical_features,
				    "past_time_features": past_time_features,
				    "past_observed_mask": past_observed_mask,
				    "future_time_features": future_time_features,
				    "future_values": future_values,
				}
				return inputs_dict

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.get_config()
				__lowerCAmelCase      :  Union[str, Any]												=       self.prepare_autoformer_inputs_dict(snake_case_)
				return config, inputs_dict

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       self.prepare_config_and_inputs()
				return config, inputs_dict







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       AutoformerModel(config=snake_case_).to(snake_case_).eval()
				__lowerCAmelCase      :  Tuple												=       model(**snake_case_)

				__lowerCAmelCase      :  Optional[int]												=       outputs.encoder_last_hidden_state
				__lowerCAmelCase      :  Optional[Any]												=       outputs.last_hidden_state

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase      :  Tuple												=       model.get_encoder()
							encoder.save_pretrained(snake_case_)
							__lowerCAmelCase      :  int												=       AutoformerEncoder.from_pretrained(snake_case_).to(snake_case_)

				__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       model.create_network_inputs(**snake_case_)
				__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])

				__lowerCAmelCase      :  Tuple												=       torch.cat(
				    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])       ,			dim=-1       ,			)
				__lowerCAmelCase      :  Optional[int]												=       encoder(inputs_embeds=snake_case_)[0]
				self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3)

				__lowerCAmelCase      :  str												=       (
				    torch.mean(transformer_inputs[:, : config.context_length, ...]       ,			dim=1)
				    .unsqueeze(1)
				    .repeat(1       ,			config.prediction_length       ,			1)
				)
				__lowerCAmelCase      :  Any												=       torch.zeros(
				    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]       ,			device=enc_input.device       ,			)

				__lowerCAmelCase      :  Optional[int]												=       torch.cat(
				    (
				        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)       ,			dim=1),
				        feature[:, config.context_length - config.label_length :, ...],
				    )       ,			dim=-1       ,			)
				__lowerCAmelCase      :  str												=       torch.cat(
				    (
				        torch.cat((trend_input[:, -config.label_length :, ...], mean)       ,			dim=1),
				        feature[:, config.context_length - config.label_length :, ...],
				    )       ,			dim=-1       ,			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase      :  List[Any]												=       model.get_decoder()
							decoder.save_pretrained(snake_case_)
							__lowerCAmelCase      :  Any												=       AutoformerDecoder.from_pretrained(snake_case_).to(snake_case_)

				__lowerCAmelCase      :  Dict												=       decoder(
				    trend=snake_case_       ,			inputs_embeds=snake_case_       ,			encoder_hidden_states=snake_case_       ,			)[0]

				self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3)






@require_torch
class 			A__   (  lowerCAmelCase_  ,		lowerCAmelCase_  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE          =	(AutoformerForPrediction,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE          =	{"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       AutoformerModelTester(self)
				__lowerCAmelCase      :  Dict												=       ConfigTester(self       ,			config_class=snake_case_       ,			has_text_modality=snake_case_)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       int:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  Union[str, Any]												=       self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  str												=       model_class(snake_case_)

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(snake_case_)
										__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       model_class.from_pretrained(snake_case_       ,			output_loading_info=snake_case_)
							self.assertEqual(info["missing_keys"]       ,			[])

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_encoder_decoder_model_standalone(*snake_case_)

	@unittest.skip(reason="Model has no tokens embeddings")
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       inspect.signature(getattr(snake_case_       ,			"forward"))
				# The main input is the name of the argument after `self`
				__lowerCAmelCase      :  Tuple												=       list(model_signature.parameters.keys())[1]
				self.assertEqual(AutoformerModel.main_input_name       ,			snake_case_)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  Optional[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  Dict												=       model_class(snake_case_)
							__lowerCAmelCase      :  Any												=       inspect.signature(model.forward)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__lowerCAmelCase      :  Any												=       [*signature.parameters.keys()]

							__lowerCAmelCase      :  Optional[Any]												=       [
							    "past_values",
							    "past_time_features",
							    "past_observed_mask",
							    "static_categorical_features",
							    "static_real_features",
							    "future_values",
							    "future_time_features",
							]

							if model.__class__.__name__ in ["AutoformerForPrediction"]:
										expected_arg_names.append("future_observed_mask")

							expected_arg_names.extend(
							    [
							        "decoder_attention_mask",
							        "head_mask",
							        "decoder_head_mask",
							        "cross_attn_head_mask",
							        "encoder_outputs",
							        "past_key_values",
							        "output_hidden_states",
							        "output_attentions",
							        "use_cache",
							        "return_dict",
							    ])

							self.assertListEqual(arg_names[: len(snake_case_)]       ,			snake_case_)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase      :  Dict												=       True

				__lowerCAmelCase      :  int												=       getattr(self.model_tester       ,			"seq_length"       ,			snake_case_)
				__lowerCAmelCase      :  str												=       getattr(self.model_tester       ,			"decoder_seq_length"       ,			snake_case_)
				__lowerCAmelCase      :  List[str]												=       getattr(self.model_tester       ,			"encoder_seq_length"       ,			snake_case_)
				__lowerCAmelCase      :  int												=       getattr(self.model_tester       ,			"d_model"       ,			snake_case_)
				__lowerCAmelCase      :  Optional[Any]												=       getattr(self.model_tester       ,			"num_attention_heads"       ,			snake_case_)
				__lowerCAmelCase      :  Dict												=       d_model // num_attention_heads

				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  Optional[Any]												=       True
							__lowerCAmelCase      :  Any												=       False
							__lowerCAmelCase      :  Tuple												=       True
							__lowerCAmelCase      :  Optional[int]												=       model_class(snake_case_)
							model.to(snake_case_)
							model.eval()
							with torch.no_grad():
										__lowerCAmelCase      :  List[Any]												=       model(**self._prepare_for_class(snake_case_       ,			snake_case_))
							__lowerCAmelCase      :  Dict												=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
							self.assertEqual(len(snake_case_)       ,			self.model_tester.num_hidden_layers)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							__lowerCAmelCase      :  Any												=       True
							__lowerCAmelCase      :  int												=       model_class(snake_case_)
							model.to(snake_case_)
							model.eval()
							with torch.no_grad():
										__lowerCAmelCase      :  str												=       model(**self._prepare_for_class(snake_case_       ,			snake_case_))
							__lowerCAmelCase      :  List[Any]												=       outputs.encoder_attentions
							self.assertEqual(len(snake_case_)       ,			self.model_tester.num_hidden_layers)

							self.assertListEqual(
							    list(attentions[0].shape[-3:])       ,			[self.model_tester.num_attention_heads, encoder_seq_length, dim]       ,			)
							__lowerCAmelCase      :  Union[str, Any]												=       len(snake_case_)

							__lowerCAmelCase      :  Optional[Any]												=       7

							if "last_hidden_state" in outputs:
										correct_outlen += 1

							if "trend" in outputs:
										correct_outlen += 1

							if "past_key_values" in outputs:
										correct_outlen += 1  # past_key_values have been returned

							if "loss" in outputs:
										correct_outlen += 1

							if "params" in outputs:
										correct_outlen += 1

							self.assertEqual(snake_case_       ,			snake_case_)

							# decoder attentions
							__lowerCAmelCase      :  Dict												=       outputs.decoder_attentions
							self.assertIsInstance(snake_case_       ,			(list, tuple))
							self.assertEqual(len(snake_case_)       ,			self.model_tester.num_hidden_layers)
							self.assertListEqual(
							    list(decoder_attentions[0].shape[-3:])       ,			[self.model_tester.num_attention_heads, decoder_seq_length, dim]       ,			)

							# cross attentions
							__lowerCAmelCase      :  List[str]												=       outputs.cross_attentions
							self.assertIsInstance(snake_case_       ,			(list, tuple))
							self.assertEqual(len(snake_case_)       ,			self.model_tester.num_hidden_layers)
							self.assertListEqual(
							    list(cross_attentions[0].shape[-3:])       ,			[self.model_tester.num_attention_heads, decoder_seq_length, dim]       ,			)

				# Check attention is always last and order is fine
				__lowerCAmelCase      :  Optional[int]												=       True
				__lowerCAmelCase      :  str												=       True
				__lowerCAmelCase      :  Union[str, Any]												=       model_class(snake_case_)
				model.to(snake_case_)
				model.eval()
				with torch.no_grad():
							__lowerCAmelCase      :  str												=       model(**self._prepare_for_class(snake_case_       ,			snake_case_))

				self.assertEqual(out_len + 2       ,			len(snake_case_))

				__lowerCAmelCase      :  Union[str, Any]												=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

				self.assertEqual(len(snake_case_)       ,			self.model_tester.num_hidden_layers)
				self.assertListEqual(
				    list(self_attentions[0].shape[-3:])       ,			[self.model_tester.num_attention_heads, encoder_seq_length, dim]       ,			)







	@is_flaky()
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Union[str, Any]:

				"""simple docstring"""

				super().test_retain_grad_hidden_states_attentions()






def 			_lowercase				(  __snake_case="train-batch.pt"	)						->   List[str]:
			__lowerCAmelCase      :  Any												=       hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"					,filename=__lowercase					,repo_type="dataset"	)
			__lowerCAmelCase      :  Dict												=       torch.load(__lowercase					,map_location=__lowercase	)
			return batch





@require_torch
@slow
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly").to(snake_case_)
				__lowerCAmelCase      :  Union[str, Any]												=       prepare_batch()

				with torch.no_grad():
							__lowerCAmelCase      :  Any												=       model(
							    past_values=batch["past_values"]       ,			past_time_features=batch["past_time_features"]       ,			past_observed_mask=batch["past_observed_mask"]       ,			static_categorical_features=batch["static_categorical_features"]       ,			future_values=batch["future_values"]       ,			future_time_features=batch["future_time_features"]       ,			)[0]

				__lowerCAmelCase      :  Tuple												=       torch.Size(
				    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size))
				self.assertEqual(output.shape       ,			snake_case_)

				__lowerCAmelCase      :  List[Any]												=       torch.tensor(
				    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]       ,			device=snake_case_)
				self.assertTrue(torch.allclose(output[0, :3, :3]       ,			snake_case_       ,			atol=snake_case_))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(snake_case_)
				__lowerCAmelCase      :  List[str]												=       prepare_batch("val-batch.pt")
				with torch.no_grad():
							__lowerCAmelCase      :  int												=       model(
							    past_values=batch["past_values"]       ,			past_time_features=batch["past_time_features"]       ,			past_observed_mask=batch["past_observed_mask"]       ,			static_categorical_features=batch["static_categorical_features"]       ,			).encoder_last_hidden_state
				__lowerCAmelCase      :  int												=       torch.Size((64, model.config.context_length, model.config.d_model))
				self.assertEqual(output.shape       ,			snake_case_)

				__lowerCAmelCase      :  Tuple												=       torch.tensor(
				    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]       ,			device=snake_case_)
				self.assertTrue(torch.allclose(output[0, :3, :3]       ,			snake_case_       ,			atol=snake_case_))







	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(snake_case_)
				__lowerCAmelCase      :  Tuple												=       prepare_batch("val-batch.pt")
				with torch.no_grad():
							__lowerCAmelCase      :  Any												=       model.generate(
							    static_categorical_features=batch["static_categorical_features"]       ,			past_time_features=batch["past_time_features"]       ,			past_values=batch["past_values"]       ,			future_time_features=batch["future_time_features"]       ,			past_observed_mask=batch["past_observed_mask"]       ,			)
				__lowerCAmelCase      :  List[Any]												=       torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
				self.assertEqual(outputs.sequences.shape       ,			snake_case_)

				__lowerCAmelCase      :  Dict												=       torch.tensor([3130.6763, 4056.5293, 7053.0786]       ,			device=snake_case_)
				__lowerCAmelCase      :  Union[str, Any]												=       outputs.sequences.mean(dim=1)
				self.assertTrue(torch.allclose(mean_prediction[0, -3:]       ,			snake_case_       ,			rtol=1e-1))






'''simple docstring'''




import os
from datetime import datetime as dt

from github import Github


__SCREAMING_SNAKE_CASE     :str       =				[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Github(os.environ["GITHUB_TOKEN"]       )
     _UpperCAmelCase     							=   g.get_repo("huggingface/accelerate"       )
     _UpperCAmelCase     							=   repo.get_issues(state="open"       )

     for issue in open_issues:
          _UpperCAmelCase     							=   sorted([comment for comment in issue.get_comments()]			,     key=lambda __lowercase       : i.created_at			,     reverse=__lowercase       )
          _UpperCAmelCase     							=   comments[0] if len(__lowercase       ) > 0 else None
          _UpperCAmelCase     							=   dt.utcnow()
          _UpperCAmelCase     							=   (current_time - issue.updated_at).days
          _UpperCAmelCase     							=   (current_time - issue.created_at).days
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and days_since_updated > 7
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Close issue since it has been 7 days of inactivity since bot mention.
               issue.edit(state="closed"       )
          elif (
              days_since_updated > 23
              and days_since_creation >= 30
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
          ):
               # Add stale comment
               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/accelerate/blob/main/CONTRIBUTING.md) "
                   "are likely to be ignored."       )


if __name__ == "__main__":
  main()




