






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 							SCREAMING_SNAKE_CASE__		(       SCREAMING_SNAKE_CASE_		):





					"""simple docstring"""






					a_			=	(UniPCMultistepScheduler,)
					a_			=	(("num_inference_steps", 2_5),)



					def  _lowercase  (      self  :						Tuple							,	**__A  :						Dict ):
						snake_case__				:   Optional[int]     =						{
						    '''num_train_timesteps''': 1_0_0_0,
						    '''beta_start''': 0.0_0_0_1,
						    '''beta_end''': 0.0_2,
						    '''beta_schedule''': '''linear''',
						    '''solver_order''': 2,
						    '''solver_type''': '''bh2''',
						}

						config.update(**UpperCamelCase__ )
						return config



					def  _lowercase  (      self  :						Tuple							,	__A  :						Tuple=0							,	**__A  :						Any ):
						snake_case__				:   List[Any]     =						dict(self.forward_default_kwargs )
						snake_case__				:   List[Any]     =						kwargs.pop("num_inference_steps"							,	UpperCamelCase__ )
						snake_case__				:   Optional[int]     =						self.dummy_sample
						snake_case__				:   Tuple     =						0.1 * sample
						snake_case__				:   Dict     =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__				:   int     =						self.get_scheduler_config(**UpperCamelCase__ )
							snake_case__				:   Optional[int]     =						scheduler_class(**UpperCamelCase__ )
							scheduler.set_timesteps(UpperCamelCase__ )
							# copy over dummy past residuals
							snake_case__				:   Dict     =						dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(UpperCamelCase__ )
								snake_case__				:   Optional[int]     =						scheduler_class.from_pretrained(UpperCamelCase__ )
								new_scheduler.set_timesteps(UpperCamelCase__ )
								# copy over dummy past residuals
								snake_case__				:   Dict     =						dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__				:   Union[str, Any]     =						sample, sample
							for t in range(UpperCamelCase__							,	time_step + scheduler.config.solver_order + 1 ):
								snake_case__				:   int     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample
								snake_case__				:   Dict     =						new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample

								assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"



					def  _lowercase  (      self  :						Dict							,	__A  :						Optional[Any]=0							,	**__A  :						Dict ):
						snake_case__				:   int     =						dict(self.forward_default_kwargs )
						snake_case__				:   List[Any]     =						kwargs.pop("num_inference_steps"							,	UpperCamelCase__ )
						snake_case__				:   int     =						self.dummy_sample
						snake_case__				:   Optional[int]     =						0.1 * sample
						snake_case__				:   str     =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__				:   Optional[int]     =						self.get_scheduler_config()
							snake_case__				:   Union[str, Any]     =						scheduler_class(**UpperCamelCase__ )
							scheduler.set_timesteps(UpperCamelCase__ )

							# copy over dummy past residuals (must be after setting timesteps)
							snake_case__				:   List[str]     =						dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(UpperCamelCase__ )
								snake_case__				:   Dict     =						scheduler_class.from_pretrained(UpperCamelCase__ )
								# copy over dummy past residuals
								new_scheduler.set_timesteps(UpperCamelCase__ )

								# copy over dummy past residual (must be after setting timesteps)
								snake_case__				:   int     =						dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__				:   Optional[Any]     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample
							snake_case__				:   Any     =						new_scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"



					def  _lowercase  (      self  :						int							,	__A  :						List[Any]=None							,	**__A  :						Optional[int] ):
						if scheduler is None:
							snake_case__				:   List[str]     =						self.scheduler_classes[0]
							snake_case__				:   str     =						self.get_scheduler_config(**UpperCamelCase__ )
							snake_case__				:   Dict     =						scheduler_class(**UpperCamelCase__ )

						snake_case__				:   Dict     =						self.scheduler_classes[0]
						snake_case__				:   Tuple     =						self.get_scheduler_config(**UpperCamelCase__ )
						snake_case__				:   List[str]     =						scheduler_class(**UpperCamelCase__ )

						snake_case__				:   Tuple     =						1_0
						snake_case__				:   Tuple     =						self.dummy_model()
						snake_case__				:   int     =						self.dummy_sample_deter
						scheduler.set_timesteps(UpperCamelCase__ )

						for i, t in enumerate(scheduler.timesteps ):
							snake_case__				:   Optional[Any]     =						model(UpperCamelCase__							,	UpperCamelCase__ )
							snake_case__				:   Tuple     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__ ).prev_sample

						return sample



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   int     =						dict(self.forward_default_kwargs )

						snake_case__				:   Dict     =						kwargs.pop("num_inference_steps"							,	UpperCamelCase__ )

						for scheduler_class in self.scheduler_classes:
							snake_case__				:   Dict     =						self.get_scheduler_config()
							snake_case__				:   List[Any]     =						scheduler_class(**UpperCamelCase__ )

							snake_case__				:   List[str]     =						self.dummy_sample
							snake_case__				:   Dict     =						0.1 * sample

							if num_inference_steps is not None and hasattr(UpperCamelCase__							,	"set_timesteps" ):
								scheduler.set_timesteps(UpperCamelCase__ )
							elif num_inference_steps is not None and not hasattr(UpperCamelCase__							,	"set_timesteps" ):
								snake_case__				:   Any     =						num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							snake_case__				:   int     =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							snake_case__				:   Tuple     =						dummy_past_residuals[: scheduler.config.solver_order]

							snake_case__				:   Union[str, Any]     =						scheduler.timesteps[5]
							snake_case__				:   Optional[int]     =						scheduler.timesteps[6]

							snake_case__				:   Dict     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample
							snake_case__				:   Tuple     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__ ).prev_sample

							self.assertEqual(output_a.shape							,	sample.shape )
							self.assertEqual(output_a.shape							,	output_a.shape )



					def  _lowercase  (      self  :						Tuple ):
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						snake_case__				:   Optional[Any]     =						UniPCMultistepScheduler(**self.get_scheduler_config() )
						snake_case__				:   int     =						self.full_loop(scheduler=UpperCamelCase__ )
						snake_case__				:   List[Any]     =						torch.mean(torch.abs(UpperCamelCase__ ) )

						assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

						snake_case__				:   Union[str, Any]     =						DPMSolverSinglestepScheduler.from_config(scheduler.config )
						snake_case__				:   Any     =						DEISMultistepScheduler.from_config(scheduler.config )
						snake_case__				:   Union[str, Any]     =						DPMSolverMultistepScheduler.from_config(scheduler.config )
						snake_case__				:   str     =						UniPCMultistepScheduler.from_config(scheduler.config )

						snake_case__				:   Union[str, Any]     =						self.full_loop(scheduler=UpperCamelCase__ )
						snake_case__				:   Union[str, Any]     =						torch.mean(torch.abs(UpperCamelCase__ ) )

						assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3



					def  _lowercase  (      self  :						Tuple ):
						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase__ )



					def  _lowercase  (      self  :						int ):
						self.check_over_configs(thresholding=UpperCamelCase__ )
						for order in [1, 2, 3]:
							for solver_type in ["bh1", "bh2"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	sample_max_value=UpperCamelCase__							,	solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	)



					def  _lowercase  (      self  :						Optional[int] ):
						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=UpperCamelCase__ )



					def  _lowercase  (      self  :						Any ):
						for solver_type in ["bh1", "bh2"]:
							for order in [1, 2, 3]:
								for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(
									    solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
									snake_case__				:   List[str]     =						self.full_loop(
									    solver_order=UpperCamelCase__							,	solver_type=UpperCamelCase__							,	prediction_type=UpperCamelCase__							,	)
									assert not torch.isnan(UpperCamelCase__ ).any(), "Samples have nan numbers"



					def  _lowercase  (      self  :						int ):
						self.check_over_configs(lower_order_final=UpperCamelCase__ )
						self.check_over_configs(lower_order_final=UpperCamelCase__ )



					def  _lowercase  (      self  :						Union[str, Any] ):
						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=UpperCamelCase__							,	time_step=0 )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   Optional[int]     =						self.full_loop()
						snake_case__				:   Optional[int]     =						torch.mean(torch.abs(UpperCamelCase__ ) )

						assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3



					def  _lowercase  (      self  :						List[Any] ):
						snake_case__				:   str     =						self.full_loop(prediction_type="v_prediction" )
						snake_case__				:   str     =						torch.mean(torch.abs(UpperCamelCase__ ) )

						assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3



					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   Tuple     =						self.scheduler_classes[0]
						snake_case__				:   Any     =						self.get_scheduler_config(thresholding=UpperCamelCase__							,	dynamic_thresholding_ratio=0 )
						snake_case__				:   Optional[Any]     =						scheduler_class(**UpperCamelCase__ )

						snake_case__				:   int     =						1_0
						snake_case__				:   str     =						self.dummy_model()
						snake_case__				:   str     =						self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCamelCase__ )

						for i, t in enumerate(scheduler.timesteps ):
							snake_case__				:   List[Any]     =						model(UpperCamelCase__							,	UpperCamelCase__ )
							snake_case__				:   int     =						scheduler.step(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__ ).prev_sample

						assert sample.dtype == torch.floataa






					def  _lowercase  (      self  :						Optional[Any]							,	**__A  :						Dict ):
						for scheduler_class in self.scheduler_classes:
							snake_case__				:   List[Any]     =						self.get_scheduler_config(**UpperCamelCase__ )
							snake_case__				:   Optional[Any]     =						scheduler_class(**UpperCamelCase__ )

							scheduler.set_timesteps(scheduler.config.num_train_timesteps )
							assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps











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       (				snake_case_      :							List[Any]  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   Optional[int]     =						[]
	for part_id in partition_order:
		snake_case__				:   List[Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Tuple     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Union[str, Any]     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Any     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[Any]     =						spark.range(10     ).repartition(2     )
	snake_case__				:   Optional[Any]     =						[1, 0]
	snake_case__				:   Dict     =						_generate_iterable_examples(snake_case_  ,						snake_case_     )  # Reverse the partitions.
	snake_case__				:   Tuple     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						snake_case_     )

	for i, (row_id, row_dict) in enumerate(generate_fn()     ):
		snake_case__,		snake_case__				:   Tuple     =						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__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[int]     =						spark.range(10     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   str     =						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__				:   Union[str, Any]     =						lambda snake_case_     : x.reverse()
		snake_case__				:   Optional[int]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[2, 1, 0]     )

		snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shuffle_data_sources(snake_case_     )
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(snake_case_     ):
			snake_case__,		snake_case__				:   Optional[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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Any     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(20     ).repartition(4     )

	# Partitions 0 and 2
	snake_case__				:   List[Any]     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=0  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[str]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[0, 2]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[int]     =						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(snake_case_     ).shard_data_sources(worker_id=1  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[Any]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[1, 3]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						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() == 100



