





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 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				snake_case_		:       Dict           =    []
				for part_id in partition_order:
								snake_case_		:       int           =    df.where(f'''SPARK_PARTITION_ID() = {part_id}'''					).collect()
								for row_idx, row in enumerate(__UpperCamelCase					):
												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 	lowerCamelCase_		(							)						->		Optional[int]:






				"""simple docstring"""


				snake_case_		:       Any           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       Dict           =    spark.range(100					).repartition(1					)
				snake_case_		:       Tuple           =    Spark(__UpperCamelCase					)
				# 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 	lowerCamelCase_		(							)						->		Any:






				"""simple docstring"""


				snake_case_		:       Union[str, Any]           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       int           =    spark.range(10					).repartition(2					)
				snake_case_		:       Optional[int]           =    [1, 0]
				snake_case_		:       Tuple           =    _generate_iterable_examples(__UpperCamelCase					,							__UpperCamelCase					)  # Reverse the partitions.
				snake_case_		:       Dict           =    _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase					,							__UpperCamelCase					)

				for i, (row_id, row_dict) in enumerate(generate_fn()					):
								snake_case_		:       Optional[int]           =    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 	lowerCamelCase_		(							)						->		int:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       Tuple           =    spark.range(10					).repartition(1					)
				snake_case_		:       List[str]           =    SparkExamplesIterable(__UpperCamelCase					)
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
								assert row_id == f'''0_{i}'''
								assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def 	lowerCamelCase_		(							)						->		Any:






				"""simple docstring"""


				snake_case_		:       Dict           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       Optional[Any]           =    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_		:       List[str]           =    lambda _UpperCamelCase					: x.reverse()
								snake_case_		:       str           =    _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase					,							[2, 1, 0]					)

								snake_case_		:       Optional[Any]           =    SparkExamplesIterable(__UpperCamelCase					).shuffle_data_sources(__UpperCamelCase					)
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
												snake_case_		:       Any           =    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 	lowerCamelCase_		(							)						->		Dict:






				"""simple docstring"""


				snake_case_		:       Dict           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       List[str]           =    spark.range(20					).repartition(4					)

				# Partitions 0 and 2
				snake_case_		:       List[str]           =    SparkExamplesIterable(__UpperCamelCase					).shard_data_sources(worker_id=0					,							num_workers=2					)
				assert shard_it_a.n_shards == 2
				snake_case_		:       str           =    _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase					,							[0, 2]					)
				for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
								snake_case_		:       List[str]           =    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_		:       Any           =    SparkExamplesIterable(__UpperCamelCase					).shard_data_sources(worker_id=1					,							num_workers=2					)
				assert shard_it_a.n_shards == 2
				snake_case_		:       Optional[int]           =    _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase					,							[1, 3]					)
				for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
								snake_case_		:       List[Any]           =    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 	lowerCamelCase_		(							)						->		Optional[int]:






				"""simple docstring"""


				snake_case_		:       int           =    pyspark.sql.SparkSession.builder.master('''local[*]'''					).appName('''pyspark'''					).getOrCreate()
				snake_case_		:       Any           =    spark.range(100					).repartition(1					)
				snake_case_		:       Any           =    Spark(__UpperCamelCase					)
				# 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





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@property
							def 		_A	(							self				):







											"""simple docstring"""



											torch.manual_seed(0				)
											a__	:							str      						= UNetaDModel(
											    block_out_channels=(32, 64) ,					layers_per_block=2 ,					sample_size=32 ,					in_channels=3 ,					out_channels=3 ,					down_block_types=("DownBlock2D", "AttnDownBlock2D") ,					up_block_types=("AttnUpBlock2D", "UpBlock2D") ,					)
											return model




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.dummy_uncond_unet
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							Union[str, Any]      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)

											a__	:							Tuple      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy"				).images

											a__	:							Optional[int]      						= torch.manual_seed(0				)
											a__	:							Tuple      						= pndm(generator=__UpperCAmelCase ,					num_inference_steps=20 ,					output_type="numpy" ,					return_dict=__UpperCAmelCase				)[0]

											a__	:							str      						= image[0, -3:, -3:, -1]
											a__	:							int      						= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							int      						= np.array([1.0, 1.0, 0.0, 1.0, 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 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= "google/ddpm-cifar10-32"

											a__	:							List[Any]      						= UNetaDModel.from_pretrained(__UpperCAmelCase				)
											a__	:							Optional[Any]      						= PNDMScheduler()

											a__	:							int      						= PNDMPipeline(unet=__UpperCAmelCase ,					scheduler=__UpperCAmelCase				)
											pndm.to(__UpperCAmelCase				)
											pndm.set_progress_bar_config(disable=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= torch.manual_seed(0				)
											a__	:							Union[str, Any]      						= pndm(generator=__UpperCAmelCase ,					output_type="numpy"				).images

											a__	:							List[Any]      						= image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											a__	:							Dict      						= np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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


