




'''simple docstring'''


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
		from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class    __lowercase						(  unittest.TestCase ):







			def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Tuple:
					for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['bs']      ,							model_result['ss']      ):
									__a			=					model_result['result'][batch_size][sequence_length]
									self.assertIsNotNone(UpperCamelCase      )







			def 		UpperCamelCase__			(	self      )				->    int:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    List[str]:
					__a			=					'sgugger/tiny-distilbert-classification'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							only_pretrain_model=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    Any:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							torchscript=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			@unittest.skipIf(torch_device == 'cpu'      ,							'Cant do half precision'      )
			def 		UpperCamelCase__			(	self      )				->    int:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							fpaa=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    Dict:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					AutoConfig.from_pretrained(UpperCamelCase      )
					# set architectures equal to `None`
					__a			=					None
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      ,							configs=[config]      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    List[Any]:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result      )
					self.check_results_dict_not_empty(results.memory_train_result      )







			@unittest.skipIf(torch_device == 'cpu'      ,							'Can\'t do half precision'      )
			def 		UpperCamelCase__			(	self      )				->    str:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							fpaa=UpperCamelCase      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result      )
					self.check_results_dict_not_empty(results.memory_train_result      )







			def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					AutoConfig.from_pretrained(UpperCamelCase      )
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      ,							configs=[config]      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    Tuple:
					__a			=					'sshleifer/tinier_bart'
					__a			=					AutoConfig.from_pretrained(UpperCamelCase      )
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      ,							configs=[config]      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_inference_result      )
					self.check_results_dict_not_empty(results.memory_inference_result      )







			def 		UpperCamelCase__			(	self      )				->    Dict:
					__a			=					'sshleifer/tiny-gpt2'
					__a			=					AutoConfig.from_pretrained(UpperCamelCase      )
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      ,							configs=[config]      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result      )
					self.check_results_dict_not_empty(results.memory_train_result      )







			def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
					__a			=					'sshleifer/tinier_bart'
					__a			=					AutoConfig.from_pretrained(UpperCamelCase      )
					__a			=					PyTorchBenchmarkArguments(
					    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							multi_process=UpperCamelCase      ,							)
					__a			=					PyTorchBenchmark(UpperCamelCase      ,							configs=[config]      )
					__a			=					benchmark.run()
					self.check_results_dict_not_empty(results.time_train_result      )
					self.check_results_dict_not_empty(results.memory_train_result      )







			def 		UpperCamelCase__			(	self      )				->    List[Any]:
					__a			=					'sshleifer/tiny-gpt2'
					with tempfile.TemporaryDirectory() as tmp_dir:
							__a			=					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							save_to_csv=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							inference_time_csv_file=os.path.join(UpperCamelCase      ,							'inf_time.csv'      )      ,							train_memory_csv_file=os.path.join(UpperCamelCase      ,							'train_mem.csv'      )      ,							inference_memory_csv_file=os.path.join(UpperCamelCase      ,							'inf_mem.csv'      )      ,							train_time_csv_file=os.path.join(UpperCamelCase      ,							'train_time.csv'      )      ,							env_info_csv_file=os.path.join(UpperCamelCase      ,							'env.csv'      )      ,							multi_process=UpperCamelCase      ,							)
							__a			=					PyTorchBenchmark(UpperCamelCase      )
							benchmark.run()
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'inf_time.csv'      )      ).exists()      )
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'train_time.csv'      )      ).exists()      )
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'inf_mem.csv'      )      ).exists()      )
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'train_mem.csv'      )      ).exists()      )
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'env.csv'      )      ).exists()      )







			def 		UpperCamelCase__			(	self      )				->    Optional[int]:
					__a			=					'sshleifer/tiny-gpt2'

					def _check_summary_is_not_empty(UpperCamelCase      ):
							self.assertTrue(hasattr(UpperCamelCase      ,							'sequential'      )      )
							self.assertTrue(hasattr(UpperCamelCase      ,							'cumulative'      )      )
							self.assertTrue(hasattr(UpperCamelCase      ,							'current'      )      )
							self.assertTrue(hasattr(UpperCamelCase      ,							'total'      )      )

					with tempfile.TemporaryDirectory() as tmp_dir:
							__a			=					PyTorchBenchmarkArguments(
							    models=[MODEL_ID]      ,							training=UpperCamelCase      ,							inference=UpperCamelCase      ,							sequence_lengths=[8]      ,							batch_sizes=[1]      ,							log_filename=os.path.join(UpperCamelCase      ,							'log.txt'      )      ,							log_print=UpperCamelCase      ,							trace_memory_line_by_line=UpperCamelCase      ,							multi_process=UpperCamelCase      ,							)
							__a			=					PyTorchBenchmark(UpperCamelCase      )
							__a			=					benchmark.run()
							_check_summary_is_not_empty(result.inference_summary      )
							_check_summary_is_not_empty(result.train_summary      )
							self.assertTrue(Path(os.path.join(UpperCamelCase      ,							'log.txt'      )      ).exists()      )









'''simple docstring'''


import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase_ 		=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					Github(os.environ['GITHUB_TOKEN']			)
  __a			=					g.get_repo('huggingface/diffusers'			)
  __a			=					repo.get_issues(state='open'			)

  for issue in open_issues:
    __a			=					sorted(issue.get_comments()				,					key=lambda a_			: i.created_at				,					reverse=a_			)
    __a			=					comments[0] if len(a_			) > 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()



