


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A							:					Tuple     						=     50000
A							:					Optional[int]     						=     5000

A						,  A							:					Dict     						=     os.path.split(__file__)
A							:					Tuple     						=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))





@get_duration
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						for i in range(_lowerCAmelCase ):
												__snake_case				=		dataset[i]





@get_duration
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						for i in range(0      ,	len(_lowerCAmelCase )      ,	_lowerCAmelCase ):
												__snake_case				=		dataset[i : i + batch_size]





@get_duration
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						with dataset.formatted_as(type=_lowerCAmelCase ):
												for i in range(_lowerCAmelCase ):
																		__snake_case				=		dataset[i]





@get_duration
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''simple docstring'''






						with dataset.formatted_as(type=_lowerCAmelCase ):
												for i in range(0      ,	_lowerCAmelCase      ,	_lowerCAmelCase ):
																		__snake_case				=		dataset[i : i + batch_size]





def 	_lowerCAmelCase       (    )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		{"num examples": SPEED_TEST_N_EXAMPLES}
						__snake_case				=		[
						    (read, {"length": SMALL_TEST}),
						    (read, {"length": SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
						    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
						    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
						    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
						    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
						    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
						    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
						]

						__snake_case				=		[
						    (read, {"length": SMALL_TEST}),
						    (read, {"length": SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
						    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
						    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
						    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
						    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
						]
						with tempfile.TemporaryDirectory() as tmp_dir:
												print("generating dataset" )
												__snake_case				=		datasets.Features(
												    {"list": datasets.Sequence(datasets.Value("float32" ) ), "numbers": datasets.Value("float32" )} )
												__snake_case				=		generate_example_dataset(
												    os.path.join(_lowerCAmelCase      ,	"dataset.arrow" )      ,	_lowerCAmelCase      ,	num_examples=_lowerCAmelCase      ,	seq_shapes={"list": (100,)}      ,	)
												print("first set of iterations" )
												for func, kwargs in functions:
																		print(func.__name__      ,	str(_lowerCAmelCase ) )
																		__snake_case				=		func(_lowerCAmelCase      ,	**_lowerCAmelCase )

												print("shuffling dataset" )
												__snake_case				=		dataset.shuffle()
												print("Second set of iterations (after shuffling" )
												for func, kwargs in functions_shuffled:
																		print("shuffled "      ,	func.__name__      ,	str(_lowerCAmelCase ) )
																		__snake_case				=		func(
																		    _lowerCAmelCase      ,	**_lowerCAmelCase )

						with open(_lowerCAmelCase      ,	"wb" ) as f:
												f.write(json.dumps(_lowerCAmelCase ).encode("utf-8" ) )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_iterating()









import os
from datetime import datetime as dt

from github import Github


A							:					Union[str, Any]     						=     [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]





def 	_lowerCAmelCase       (    )						->	str:


						'''simple docstring'''






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

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





