
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  assert isinstance(lowercase_			,			lowercase_ )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""			,			[False, True] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: int     		=   tmp_path / """cache"""
  lowercase__	: Dict     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase__	: int     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_			,			keep_in_memory=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )

@pytest.mark.parametrize(
    """features"""			,			[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,			)
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: int     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: int     		=   features.copy() if features else default_expected_features
  lowercase__	: str     		=   (
      Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
  )
  lowercase__	: str     		=   ParquetDatasetReader(lowercase_			,			features=lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )

@pytest.mark.parametrize("""split"""			,			[None, NamedSplit("""train""" ), """train""", """test"""] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   tmp_path / """cache"""
  lowercase__	: Any     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[Any]     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_			,			split=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""			,			[str, list] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  if issubclass(lowercase_			,			lowercase_ ):
    lowercase__	: Any     		=   parquet_path
  elif issubclass(lowercase_			,			lowercase_ ):
    lowercase__	: str     		=   [parquet_path]
  lowercase__	: Any     		=   tmp_path / """cache"""
  lowercase__	: List[Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[str]     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=("train",) )		->						int:


  '''simple docstring'''






  assert isinstance(lowercase_			,			lowercase_ )
  for split in splits:
    lowercase__	: List[Any]     		=   dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""			,			[False, True] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: Any     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase__	: Tuple     		=   ParquetDatasetReader(
        {"""train""": parquet_path}			,			cache_dir=lowercase_			,			keep_in_memory=lowercase_ ).read()
  _check_parquet_datasetdict(lowercase_			,			lowercase_ )

@pytest.mark.parametrize(
    """features"""			,			[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,			)
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: Union[str, Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[str]     		=   features.copy() if features else default_expected_features
  lowercase__	: Tuple     		=   (
      Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
  )
  lowercase__	: Union[str, Any]     		=   ParquetDatasetReader({"""train""": parquet_path}			,			features=lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_datasetdict(lowercase_			,			lowercase_ )

@pytest.mark.parametrize("""split"""			,			[None, NamedSplit("""train""" ), """train""", """test"""] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  if split:
    lowercase__	: Union[str, Any]     		=   {split: parquet_path}
  else:
    lowercase__	: Optional[int]     		=   """train"""
    lowercase__	: List[Any]     		=   {"""train""": parquet_path, """test""": parquet_path}
  lowercase__	: Dict     		=   tmp_path / """cache"""
  lowercase__	: Union[str, Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: int     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_datasetdict(lowercase_			,			lowercase_			,			splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Dict     		=   ParquetDatasetWriter(lowercase_			,			tmp_path / """foo.parquet""" )
  assert writer.write() > 0
  lowercase__	: Any     		=   pq.ParquetFile(tmp_path / """foo.parquet""" )
  lowercase__	: Optional[Any]     		=   pf.read()
  assert dataset.data.table == output_table

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  lowercase__	: List[str]     		=   str(shared_datadir / """test_image_rgb.jpg""" )
  lowercase__	: Any     		=   {"""image""": [image_path]}
  lowercase__	: str     		=   Features({"""image""": Image()} )
  lowercase__	: Dict     		=   Dataset.from_dict(lowercase_			,			features=lowercase_ )
  lowercase__	: Any     		=   ParquetDatasetWriter(lowercase_			,			tmp_path / """foo.parquet""" )
  assert writer.write() > 0

  lowercase__	: Tuple     		=   Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
  assert dataset.features == reloaded_dataset.features

  lowercase__	: Tuple     		=   ParquetDatasetReader(str(tmp_path / """foo.parquet""" )			,			streaming=lowercase_ ).read()
  assert dataset.features == reloaded_iterable_dataset.features



@pytest.mark.parametrize(
    """feature, expected"""			,			[
        (Features({"""foo""": Value("""int32""" )} ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]			,			)
def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  assert get_writer_batch_size(lowercase_ ) == expected












from datetime import datetime as dt
import os

from github import Github


__A								=       [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def      lowerCamelCase_						(			)	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       Github(os.environ['GITHUB_TOKEN'] )
				__lowerCamelCase									=       g.get_repo('huggingface/transformers' )
				__lowerCamelCase									=       repo.get_issues(state='open' )

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


