

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	(CMStochasticIterativeScheduler,)
 UpperCAmelCase__									=	10
 def   A_			(   self						:   Any    ,  **UpperCAmelCase						:   str			)    ->			Any:
     lowerCamelCase__					:   Dict      				=	{
         'num_train_timesteps': 201,
         'sigma_min': 0.0_0_2,
         'sigma_max': 80.0,
     }

     config.update(**_SCREAMING_SNAKE_CASE			)
     return config
 def   A_			(   self						:   Optional[Any]			)    ->			List[Any]:
     lowerCamelCase__					:   Dict      				=	10

     lowerCamelCase__					:   Any      				=	self.get_scheduler_config()
     lowerCamelCase__					:   str      				=	self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE			)

     scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   Union[str, Any]      				=	scheduler.timesteps[0]
     lowerCamelCase__					:   Union[str, Any]      				=	scheduler.timesteps[1]

     lowerCamelCase__					:   List[str]      				=	self.dummy_sample
     lowerCamelCase__					:   Any      				=	0.1 * sample

     lowerCamelCase__					:   Tuple      				=	scheduler.step(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			).prev_sample
     lowerCamelCase__					:   Optional[Any]      				=	scheduler.step(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			).prev_sample

     self.assertEqual(output_a.shape    ,  sample.shape			)
     self.assertEqual(output_a.shape    ,  output_a.shape			)
 def   A_			(   self						:   str			)    ->			Tuple:






     for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE			)
 def   A_			(   self						:   List[Any]			)    ->			Optional[Any]:






     for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE			)
 def   A_			(   self						:   Optional[int]			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   Dict      				=	self.get_scheduler_config()
     lowerCamelCase__					:   str      				=	scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   Optional[int]      				=	1
     scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
     lowerCamelCase__					:   Any      				=	scheduler.timesteps

     lowerCamelCase__					:   Union[str, Any]      				=	torch.manual_seed(0			)

     lowerCamelCase__					:   Any      				=	self.dummy_model()
     lowerCamelCase__					:   Optional[int]      				=	self.dummy_sample_deter * scheduler.init_noise_sigma

     for i, t in enumerate(_SCREAMING_SNAKE_CASE			):
         # 1. scale model input
         lowerCamelCase__					:   int      				=	scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			)

         # 2. predict noise residual
         lowerCamelCase__					:   str      				=	model(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			)

         # 3. predict previous sample x_t-1
         lowerCamelCase__					:   List[Any]      				=	scheduler.step(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  generator=_SCREAMING_SNAKE_CASE			).prev_sample

         lowerCamelCase__					:   Tuple      				=	pred_prev_sample

     lowerCamelCase__					:   int      				=	torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
     lowerCamelCase__					:   Optional[int]      				=	torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

     assert abs(result_sum.item() - 192.7614			) < 1e-2
     assert abs(result_mean.item() - 0.2_5_1_0			) < 1e-3
 def   A_			(   self						:   Any			)    ->			str:
     lowerCamelCase__					:   Union[str, Any]      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   List[Any]      				=	self.get_scheduler_config()
     lowerCamelCase__					:   List[str]      				=	scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   Optional[Any]      				=	[106, 0]
     scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE			)
     lowerCamelCase__					:   str      				=	scheduler.timesteps

     lowerCamelCase__					:   Union[str, Any]      				=	torch.manual_seed(0			)

     lowerCamelCase__					:   List[str]      				=	self.dummy_model()
     lowerCamelCase__					:   Optional[Any]      				=	self.dummy_sample_deter * scheduler.init_noise_sigma

     for t in timesteps:
         # 1. scale model input
         lowerCamelCase__					:   Tuple      				=	scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			)

         # 2. predict noise residual
         lowerCamelCase__					:   int      				=	model(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE			)

         # 3. predict previous sample x_t-1
         lowerCamelCase__					:   Dict      				=	scheduler.step(_SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  _SCREAMING_SNAKE_CASE    ,  generator=_SCREAMING_SNAKE_CASE			).prev_sample

         lowerCamelCase__					:   Tuple      				=	pred_prev_sample

     lowerCamelCase__					:   List[str]      				=	torch.sum(torch.abs(_SCREAMING_SNAKE_CASE			)			)
     lowerCamelCase__					:   Optional[int]      				=	torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

     assert abs(result_sum.item() - 347.6357			) < 1e-2
     assert abs(result_mean.item() - 0.4_5_2_7			) < 1e-3
 def   A_			(   self						:   str			)    ->			Optional[int]:
     lowerCamelCase__					:   str      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   str      				=	self.get_scheduler_config()
     lowerCamelCase__					:   Union[str, Any]      				=	scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   Dict      				=	[39, 30, 12, 15, 0]

     with self.assertRaises(_SCREAMING_SNAKE_CASE    ,  msg='`timesteps` must be in descending order.'			):
         scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE			)
 def   A_			(   self						:   List[Any]			)    ->			List[Any]:
     lowerCamelCase__					:   int      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   str      				=	self.get_scheduler_config()
     lowerCamelCase__					:   Optional[int]      				=	scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   Tuple      				=	[39, 30, 12, 1, 0]
     lowerCamelCase__					:   Dict      				=	len(_SCREAMING_SNAKE_CASE			)

     with self.assertRaises(_SCREAMING_SNAKE_CASE    ,  msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
         scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE    ,  timesteps=_SCREAMING_SNAKE_CASE			)







 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	self.scheduler_classes[0]
     lowerCamelCase__					:   Any      				=	self.get_scheduler_config()
     lowerCamelCase__					:   str      				=	scheduler_class(**_SCREAMING_SNAKE_CASE			)

     lowerCamelCase__					:   List[str]      				=	[scheduler.config.num_train_timesteps]

     with self.assertRaises(
         _SCREAMING_SNAKE_CASE    ,  msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,  ):
         scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE			)












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

    for i, (row_id, row_dict) in enumerate(generate_fn()				):
        lowerCamelCase__				,							lowerCamelCase__					:   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   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Dict      				=	spark.range(10				).repartition(1				)
    lowerCamelCase__					:   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  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   Dict      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   str      				=	spark.range(30				).repartition(3				)
    # Mock the generator so that shuffle reverses the partition indices.
    with patch('numpy.random.Generator'				) as generator_mock:
        lowerCamelCase__					:   int      				=	lambda _UpperCAmelCase				: x.reverse()
        lowerCamelCase__					:   Any      				=	_get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase		,			[2, 1, 0]				)

        lowerCamelCase__					:   Tuple      				=	SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_UpperCAmelCase				):
            lowerCamelCase__				,							lowerCamelCase__					:   Union[str, 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   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    lowerCamelCase__					:   Tuple      				=	spark.range(20				).repartition(4				)

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





