
from datetime import datetime as dt
import os

from github import Github


lowercase__		:str			  =   [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						Github(os.environ['''GITHUB_TOKEN''']		)
					lowercase						     =						g.get_repo('''huggingface/transformers'''		)
					lowercase						     =						repo.get_issues(state='''open'''		)

					for issue in open_issues:
										lowercase						     =						sorted([comment for comment in issue.get_comments()]			,      key=lambda lowerCAmelCase__		: i.created_at			,      reverse=lowerCAmelCase__		)
										lowercase						     =						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()		)
										):
															# 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()






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						np.inf

					def set_batch_size(lowerCAmelCase__		) -> None:
										nonlocal batch_size
										if isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		):
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
										elif isinstance(lowerCAmelCase__			,      lowerCAmelCase__		) and feature.dtype == "binary":
															lowercase						     =						min(lowerCAmelCase__			,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

					_visit(lowerCAmelCase__			,      lowerCAmelCase__		)

					return None if batch_size is np.inf else batch_size







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = None       ,**A__       ,):
												super().__init__(
												    A__       ,split=A__       ,features=A__       ,cache_dir=A__       ,keep_in_memory=A__       ,streaming=A__       ,num_proc=A__       ,**A__       ,)
												lowercase						     =						path_or_paths if isinstance(A__       ,A__) else {self.split: path_or_paths}
												lowercase						     =						_PACKAGED_DATASETS_MODULES['''parquet'''][1]
												lowercase						     =						Parquet(
												    cache_dir=A__       ,data_files=A__       ,features=A__       ,hash=A__       ,**A__       ,)





							def 			A__				(    self):
												# Build iterable dataset
												if self.streaming:
																	lowercase						     =						self.builder.as_streaming_dataset(split=self.split)
												# Build regular (map-style) dataset
												else:
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None
																	lowercase						     =						None

																	self.builder.download_and_prepare(
																	    download_config=A__       ,download_mode=A__       ,verification_mode=A__       ,base_path=A__       ,num_proc=self.num_proc       ,)
																	lowercase						     =						self.builder.as_dataset(
																	    split=self.split       ,verification_mode=A__       ,in_memory=self.keep_in_memory)
												return dataset







class     lowercase		:




							def __init__(    self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
												lowercase						     =						dataset
												lowercase						     =						path_or_buf
												lowercase						     =						batch_size or get_writer_batch_size(dataset.features)
												lowercase						     =						parquet_writer_kwargs




							def 			A__				(    self):
												lowercase						     =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

												if isinstance(self.path_or_buf       ,(str, bytes, os.PathLike)):
																	with open(self.path_or_buf       ,'''wb+''') as buffer:
																						lowercase						     =						self._write(file_obj=A__       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												else:
																	lowercase						     =						self._write(file_obj=self.path_or_buf       ,batch_size=A__       ,**self.parquet_writer_kwargs)
												return written





							def 			A__				(    self       ,A__       ,A__       ,**A__):
												lowercase						     =						0
												lowercase						     =						parquet_writer_kwargs.pop('''path_or_buf'''       ,A__)
												lowercase						     =						self.dataset.features.arrow_schema

												lowercase						     =						pq.ParquetWriter(A__       ,schema=A__       ,**A__)

												for offset in logging.tqdm(
												    range(0       ,len(self.dataset)       ,A__)       ,unit='''ba'''       ,disable=not logging.is_progress_bar_enabled()       ,desc='''Creating parquet from Arrow format'''       ,):
																	lowercase						     =						query_table(
																	    table=self.dataset._data       ,key=slice(A__       ,offset + batch_size)       ,indices=self.dataset._indices if self.dataset._indices is not None else None       ,)
																	writer.write_table(A__)
																	written += batch.nbytes
												writer.close()
												return written




