
'''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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  []
							for part_id in partition_order:
														_snake_case       =  df.where(f"""SPARK_PARTITION_ID() = {part_id}"""				).collect()
														for row_idx, row in enumerate(_SCREAMING_SNAKE_CASE				):
																					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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  pyspark.sql.SparkSession.builder.master("""local[*]"""				).appName("""pyspark"""				).getOrCreate()
							_snake_case       =  spark.range(100				).repartition(1				)
							_snake_case       =  Spark(_SCREAMING_SNAKE_CASE				)
							# 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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  pyspark.sql.SparkSession.builder.master("""local[*]"""				).appName("""pyspark"""				).getOrCreate()
							_snake_case       =  spark.range(10				).repartition(2				)
							_snake_case       =  [1, 0]
							_snake_case       =  _generate_iterable_examples(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)  # Reverse the partitions.
							_snake_case       =  _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							for i, (row_id, row_dict) in enumerate(generate_fn()				):
														_snake_case,							_snake_case       =  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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  pyspark.sql.SparkSession.builder.master("""local[*]"""				).appName("""pyspark"""				).getOrCreate()
							_snake_case       =  spark.range(10				).repartition(1				)
							_snake_case       =  SparkExamplesIterable(_SCREAMING_SNAKE_CASE				)
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE				):
														assert row_id == f"""0_{i}"""
														assert row_dict == {"id": i}






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

														_snake_case       =  SparkExamplesIterable(_SCREAMING_SNAKE_CASE				).shuffle_data_sources(_SCREAMING_SNAKE_CASE				)
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE				):
																					_snake_case,							_snake_case       =  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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  pyspark.sql.SparkSession.builder.master("""local[*]"""				).appName("""pyspark"""				).getOrCreate()
							_snake_case       =  spark.range(20				).repartition(4				)

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

							# Partitions 1 and 3
							_snake_case       =  SparkExamplesIterable(_SCREAMING_SNAKE_CASE				).shard_data_sources(worker_id=1							, num_workers=2				)
							assert shard_it_a.n_shards == 2
							_snake_case       =  _get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE							, [1, 3]				)
							for i, (row_id, row_dict) in enumerate(_SCREAMING_SNAKE_CASE				):
														_snake_case,							_snake_case       =  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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  pyspark.sql.SparkSession.builder.master("""local[*]"""				).appName("""pyspark"""				).getOrCreate()
							_snake_case       =  spark.range(100				).repartition(1				)
							_snake_case       =  Spark(_SCREAMING_SNAKE_CASE				)
							# 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() == 100








'''simple docstring'''
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)