


"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Dict				  =  (KDPMaAncestralDiscreteScheduler,)
							__magic_name__					:     Union[str, Any]				  =  10




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Dict		=      {
								    "num_train_timesteps": 1_100,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**lowerCamelCase__)
								return config




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								for timesteps in [10, 50, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]	,					[0.00_02, 0.0_02, 0.02]):
									self.check_over_configs(beta_start=lowerCamelCase__	,					beta_end=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						Tuple		=      self.scheduler_classes[0]
								snake_case__  :						List[Any]		=      self.get_scheduler_config()
								snake_case__  :						Tuple		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps)

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

								snake_case__  :						Any		=      self.dummy_model()
								snake_case__  :						Tuple		=      self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case__  :						int		=      sample.to(lowerCamelCase__)

								for i, t in enumerate(scheduler.timesteps):
									snake_case__  :						List[str]		=      scheduler.scale_model_input(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						Any		=      model(lowerCamelCase__	,					lowerCamelCase__)

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

								snake_case__  :						List[Any]		=      torch.sum(torch.abs(lowerCamelCase__))
								snake_case__  :						Tuple		=      torch.mean(torch.abs(lowerCamelCase__))

								assert abs(result_sum.item() - 1_38_49.38_77) < 1E-2
								assert abs(result_mean.item() - 18.03_31) < 5E-3




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						int		=      self.scheduler_classes[0]
								snake_case__  :						Optional[int]		=      self.get_scheduler_config(prediction_type="v_prediction")
								snake_case__  :						int		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps)

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

								snake_case__  :						Tuple		=      torch.manual_seed(0)

								for i, t in enumerate(scheduler.timesteps):
									snake_case__  :						Tuple		=      scheduler.scale_model_input(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						int		=      model(lowerCamelCase__	,					lowerCamelCase__)

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

								snake_case__  :						Optional[Any]		=      torch.sum(torch.abs(lowerCamelCase__))
								snake_case__  :						List[Any]		=      torch.mean(torch.abs(lowerCamelCase__))

								assert abs(result_sum.item() - 3_28.99_70) < 1E-2
								assert abs(result_mean.item() - 0.42_84) < 1E-3







							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						str		=      self.scheduler_classes[0]
								snake_case__  :						Dict		=      self.get_scheduler_config()
								snake_case__  :						Dict		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps	,					device=lowerCamelCase__)
								snake_case__  :						Tuple		=      torch.manual_seed(0)

								snake_case__  :						Tuple		=      self.dummy_model()
								snake_case__  :						int		=      self.dummy_sample_deter.to(lowerCamelCase__) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
									snake_case__  :						Dict		=      scheduler.scale_model_input(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						int		=      model(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						str		=      scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					generator=lowerCamelCase__)
									snake_case__  :						Dict		=      output.prev_sample

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

								assert abs(result_sum.item() - 1_38_49.38_18) < 1E-1
								assert abs(result_mean.item() - 18.03_31) < 1E-3







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    Dataset.from_dict({'i': range(17							)}							)
			UpperCAmelCase__		:Optional[Any] 		=    len(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    3
			UpperCAmelCase__		:List[str] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(SCREAMING_SNAKE_CASE							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Union[str, Any] 		=    3
			UpperCAmelCase__		:Union[str, Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Union[str, Any] 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			UpperCAmelCase__		:Dict 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('shards_per_node'     ,						[1, 2, 3]							)
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			def gen(SCREAMING_SNAKE_CASE							):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17							))

			UpperCAmelCase__		:int 		=    3
			UpperCAmelCase__		:Any 		=    shards_per_node * world_size
			UpperCAmelCase__		:int 		=    {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(SCREAMING_SNAKE_CASE							)]}
			UpperCAmelCase__		:Optional[Any] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE     ,						gen_kwargs=SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Tuple 		=    len(list(SCREAMING_SNAKE_CASE							)							)
			assert full_ds.n_shards == world_size * shards_per_node
			UpperCAmelCase__		:Optional[Any] 		=    [
			    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=SCREAMING_SNAKE_CASE     ,						world_size=SCREAMING_SNAKE_CASE							) for rank in range(SCREAMING_SNAKE_CASE							)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(SCREAMING_SNAKE_CASE							)							) for ds in datasets_per_rank							) == full_size
			assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 		A						(  ):




			"""simple docstring"""


			def gen():
						return ({"i": i} for i in range(17							))

			UpperCAmelCase__		:Tuple 		=    2
			UpperCAmelCase__		:List[str] 		=    IterableDataset.from_generator(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:str 		=    len(list(SCREAMING_SNAKE_CASE							)							)

			UpperCAmelCase__		:List[str] 		=    split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle(seed=42							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(SCREAMING_SNAKE_CASE     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							).shuffle()

			UpperCAmelCase__		:Dict 		=    split_dataset_by_node(full_ds.shuffle(seed=42							)     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)
			assert len(list(SCREAMING_SNAKE_CASE							)							) == 1 + full_size // world_size
			with pytest.raises(SCREAMING_SNAKE_CASE							):
						split_dataset_by_node(full_ds.shuffle()     ,						rank=0     ,						world_size=SCREAMING_SNAKE_CASE							)




@pytest.mark.parametrize('streaming'     ,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    2
			UpperCAmelCase__		:str 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Optional[int] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Any 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Optional[int] 		=    f"""
        --streaming={streaming}
    """.split()
			UpperCAmelCase__		:Optional[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)




@pytest.mark.parametrize(
    'nproc_per_node, num_workers'     ,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]     ,						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'     ,						reason='execute_subprocess_async doesn\'t support windows'							)
@pytest.mark.integration
def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:List[str] 		=    True
			UpperCAmelCase__		:Optional[Any] 		=    get_torch_dist_unique_port()
			UpperCAmelCase__		:Union[str, Any] 		=    Path(SCREAMING_SNAKE_CASE							).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			UpperCAmelCase__		:Union[str, Any] 		=    f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			UpperCAmelCase__		:List[Any] 		=    [sys.executable] + distributed_args + args
			execute_subprocess_async(SCREAMING_SNAKE_CASE     ,						env=os.environ.copy()							)





