

"""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 	A       (     snake_case__		,				snake_case__    )     ->   Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			for part_id in partition_order:
						SCREAMING_SNAKE_CASE__								=      df.where(f"""SPARK_PARTITION_ID() = {part_id}"""    ).collect()
						for row_idx, row in enumerate(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 	A       (     )     ->   Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      pyspark.sql.SparkSession.builder.master("""local[*]"""    ).appName("""pyspark"""    ).getOrCreate()
			SCREAMING_SNAKE_CASE__								=      spark.range(1_00    ).repartition(1    )
			SCREAMING_SNAKE_CASE__								=      Spark(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 	A       (     )     ->   Union[str, Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      pyspark.sql.SparkSession.builder.master("""local[*]"""    ).appName("""pyspark"""    ).getOrCreate()
			SCREAMING_SNAKE_CASE__								=      spark.range(10    ).repartition(2    )
			SCREAMING_SNAKE_CASE__								=      [1, 0]
			SCREAMING_SNAKE_CASE__								=      _generate_iterable_examples(snake_case__		,				snake_case__    )  # Reverse the partitions.
			SCREAMING_SNAKE_CASE__								=      _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__		,				snake_case__    )

			for i, (row_id, row_dict) in enumerate(generate_fn()    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_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 	A       (     )     ->   List[Any]:




			'''simple docstring'''


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

@require_not_windows
@require_dill_gt_0_3_2
def 	A       (     )     ->   Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      pyspark.sql.SparkSession.builder.master("""local[*]"""    ).appName("""pyspark"""    ).getOrCreate()
			SCREAMING_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:
						SCREAMING_SNAKE_CASE__								=      lambda snake_case__    : x.reverse()
						SCREAMING_SNAKE_CASE__								=      _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__		,				[2, 1, 0]    )

						SCREAMING_SNAKE_CASE__								=      SparkExamplesIterable(snake_case__    ).shuffle_data_sources(snake_case__    )
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(snake_case__    ):
									SCREAMING_SNAKE_CASE__							,		SCREAMING_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 	A       (     )     ->   str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      pyspark.sql.SparkSession.builder.master("""local[*]"""    ).appName("""pyspark"""    ).getOrCreate()
			SCREAMING_SNAKE_CASE__								=      spark.range(20    ).repartition(4    )

			# Partitions 0 and 2
			SCREAMING_SNAKE_CASE__								=      SparkExamplesIterable(snake_case__    ).shard_data_sources(worker_id=0		,				num_workers=2    )
			assert shard_it_a.n_shards == 2
			SCREAMING_SNAKE_CASE__								=      _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__		,				[0, 2]    )
			for i, (row_id, row_dict) in enumerate(snake_case__    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_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
			SCREAMING_SNAKE_CASE__								=      SparkExamplesIterable(snake_case__    ).shard_data_sources(worker_id=1		,				num_workers=2    )
			assert shard_it_a.n_shards == 2
			SCREAMING_SNAKE_CASE__								=      _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__		,				[1, 3]    )
			for i, (row_id, row_dict) in enumerate(snake_case__    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_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 	A       (     )     ->   Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      pyspark.sql.SparkSession.builder.master("""local[*]"""    ).appName("""pyspark"""    ).getOrCreate()
			SCREAMING_SNAKE_CASE__								=      spark.range(1_00    ).repartition(1    )
			SCREAMING_SNAKE_CASE__								=      Spark(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() == 1_00





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2





@slow
@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


