


"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	(HeunDiscreteScheduler,)
		__UpperCamelCase							=	10







		def        a_       (    self					:				str			,      **a__					:				Any						)				->  Dict:





						'''simple docstring'''







						_A         =	{
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**a__						)
						return config







		def        a_       (    self					:				Dict						)				->  Tuple:





						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a__						)







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





						'''simple docstring'''







						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=a__			,      beta_end=a__						)







		def        a_       (    self					:				List[Any]						)				->  Dict:





						'''simple docstring'''







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







		def        a_       (    self					:				Tuple						)				->  int:





						'''simple docstring'''







						for clip_sample_range in [1.0, 2.0, 3.0]:
										self.check_over_configs(clip_sample_range=a__			,      clip_sample=a__						)







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





						'''simple docstring'''







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







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





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3
						else:
										# CUDA
										assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3







		def        a_       (    self					:				str						)				->  int:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config(prediction_type="v_prediction"						)
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
										assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
						else:
										# CUDA
										assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3







		def        a_       (    self					:				Dict						)				->  Optional[int]:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

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

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						if str(a__						).startswith("cpu"						):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3
						elif str(a__						).startswith("mps"						):
										# Larger tolerance on mps
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-2
						else:
										# CUDA
										assert abs(result_sum.item() - 0.1_2_3_3						) < 1E-2
										assert abs(result_mean.item() - 0.0_0_0_2						) < 1E-3







		def        a_       (    self					:				str						)				->  Dict:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__			,      use_karras_sigmas=a__						)

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

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for t in scheduler.timesteps:
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1E-2
						assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7						) < 1E-2
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 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) 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 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) 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 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) 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 					snake_case						()       ->       Dict:

						'''simple docstring'''



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

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@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 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			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 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



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