
from datetime import datetime as dt
import os

from github import Github


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

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


  '''simple docstring'''






  lowercase__	: int     		=   Github(os.environ["""GITHUB_TOKEN"""] )
  lowercase__	: Optional[Any]     		=   g.get_repo("""huggingface/transformers""" )
  lowercase__	: Any     		=   repo.get_issues(state="""open""" )

  for issue in open_issues:
    lowercase__	: Optional[Any]     		=   sorted([comment for comment in issue.get_comments()]			,			key=lambda lowercase_ : i.created_at			,			reverse=lowercase_ )
    lowercase__	: str     		=   comments[0] if len(lowercase_ ) > 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()








'''simple docstring'''
from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						[]
	for part_id in partition_order:
		a_      				=						df.where(F'''SPARK_PARTITION_ID() = {part_id}'''						).collect()
		for row_idx, row in enumerate(A__						):
			expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict())						)
	return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						Spark(A__						)
	# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
	# that each partition can hold 2 rows.
	spark_builder._repartition_df_if_needed(max_shard_size=16						)
	# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
	assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(2						)
	a_      				=						[1, 0]
	a_      				=						_generate_iterable_examples(A__  ,      A__						)  # Reverse the partitions.
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      A__						)

	for i, (row_id, row_dict) in enumerate(generate_fn()						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(1						)
	a_      				=						SparkExamplesIterable(A__						)
	assert it.n_shards == 1
	for i, (row_id, row_dict) in enumerate(A__						):
		assert row_id == F'''0_{i}'''
		assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(30						).repartition(3						)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("""numpy.random.Generator"""						) as generator_mock:
		a_      				=						lambda A__						: x.reverse()
		a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [2, 1, 0]						)

		a_      				=						SparkExamplesIterable(A__						).shuffle_data_sources(A__						)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(A__						):
			a_				,  a_      				=						expected_row_ids_and_row_dicts[i]
			assert row_id == expected_row_id
			assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(20						).repartition(4						)

	# Partitions 0 and 2
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=0  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [0, 2]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict

	# Partitions 1 and 3
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=1  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [1, 3]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						expected_row_ids_and_row_dicts_a[i]
		assert row_id == expected_row_id
		assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						Spark(A__						)
	# Choose a small max_shard_size for maximum partitioning.
	spark_builder._repartition_df_if_needed(max_shard_size=1						)
	# The new number of partitions should not be greater than the number of rows.
	assert spark_builder.df.rdd.getNumPartitions() == 1_00
