
"""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      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Union[str, Any]:
			__lowercase       =       []
			for part_id in partition_order:
						__lowercase       =       df.where(F'''SPARK_PARTITION_ID() = {part_id}'''      ).collect()
						for row_idx, row in enumerate(_lowercase      ):
									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[Any]:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(1_0_0      ).repartition(1      )
			__lowercase       =       Spark(_lowercase      )
			# 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=1_6      )
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0



@require_not_windows
@require_dill_gt_0_3_2
def      lowerCAmelCase__			(		)						->Dict:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(1_0      ).repartition(2      )
			__lowercase       =       [1, 0]
			__lowercase       =       _generate_iterable_examples(_lowercase			,   _lowercase      )  # Reverse the partitions.
			__lowercase       =       _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,   _lowercase      )

			for i, (row_id, row_dict) in enumerate(generate_fn()      ):
						__lowercase       =       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:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(1_0      ).repartition(1      )
			__lowercase       =       SparkExamplesIterable(_lowercase      )
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(_lowercase      ):
						assert row_id == F'''0_{i}'''
						assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def      lowerCAmelCase__			(		)						->Optional[Any]:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(3_0      ).repartition(3      )
			# Mock the generator so that shuffle reverses the partition indices.
			with patch("numpy.random.Generator"      ) as generator_mock:
						__lowercase       =       lambda __magic_name__      : x.reverse()
						__lowercase       =       _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,   [2, 1, 0]      )

						__lowercase       =       SparkExamplesIterable(_lowercase      ).shuffle_data_sources(_lowercase      )
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(_lowercase      ):
									__lowercase       =       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__			(		)						->Union[str, Any]:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(2_0      ).repartition(4      )

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

			# Partitions 1 and 3
			__lowercase       =       SparkExamplesIterable(_lowercase      ).shard_data_sources(worker_id=1			,   num_workers=2      )
			assert shard_it_a.n_shards == 2
			__lowercase       =       _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,   [1, 3]      )
			for i, (row_id, row_dict) in enumerate(_lowercase      ):
						__lowercase       =       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]:
			__lowercase       =       pyspark.sql.SparkSession.builder.master("local[*]"      ).appName("pyspark"      ).getOrCreate()
			__lowercase       =       spark.range(1_0_0      ).repartition(1      )
			__lowercase       =       Spark(_lowercase      )
			# 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_0_0












import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],					[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
					self.check_over_configs(beta_start=lowerCamelCase_,					beta_end=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Optional[int]      =		torch.manual_seed(0			)

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      Dict      =		torch.manual_seed(0			)

			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Any      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[Any]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
			assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1E-3


