
"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      List[str]						  =				(HeunDiscreteScheduler,)
				UpperCAmelCase_				:      int						  =				10


				def 							A					(				self		,	**lowercase__			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**lowercase__			)
									return config


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase__			)


				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]			):
														self.check_over_configs(beta_start=lowercase__		,	beta_end=lowercase__			)


				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__			)

									scheduler.set_timesteps(self.num_inference_steps			)

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

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

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config(prediction_type='v_prediction'			)
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__			)

									scheduler.set_timesteps(self.num_inference_steps			)

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

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

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6_934E-07			) < 1E-2
														assert abs(result_mean.item() - 6.1_112E-10			) < 1E-3
									else:
														# CUDA
														assert abs(result_sum.item() - 4.693_428_650_170_972E-07			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3


				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__			)

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

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma

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

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									if str(lowercase__			).startswith('cpu'			):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3
									elif str(lowercase__			).startswith('mps'			):
														# Larger tolerance on mps
														assert abs(result_mean.item() - 0.0_002			) < 1E-2
									else:
														# CUDA
														assert abs(result_sum.item() - 0.1_233			) < 1E-2
														assert abs(result_mean.item() - 0.0_002			) < 1E-3




				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE				=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				=	scheduler_class(**lowercase__		,	use_karras_sigmas=lowercase__			)

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

									SCREAMING_SNAKE_CASE				=	self.dummy_model()
									SCREAMING_SNAKE_CASE				=	self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE				=	sample.to(lowercase__			)

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

														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	lowercase__			)

														SCREAMING_SNAKE_CASE				=	scheduler.step(lowercase__		,	lowercase__		,	lowercase__			)
														SCREAMING_SNAKE_CASE				=	output.prev_sample

									SCREAMING_SNAKE_CASE				=	torch.sum(torch.abs(lowercase__			)			)
									SCREAMING_SNAKE_CASE				=	torch.mean(torch.abs(lowercase__			)			)

									assert abs(result_sum.item() - 0.00_015			) < 1E-2
									assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07			) < 1E-2


"""simple docstring"""




import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


snake_case    =		1_0_0_0_0_0_0_0_0_0_0_0
snake_case    =		1_0_0_0_0

snake_case   ,			snake_case    =		os.path.split(__file__)
snake_case    =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	pa.Table.from_pydict({'col': [0] * chunk_size}			)
					SCREAMING_SNAKE_CASE				=	pa.concat_tables([table] * (num_examples // chunk_size)			)
					return datasets.Dataset(SCREAMING_SNAKE_CASE_,     fingerprint='table_100B'			)




@dataclass
class      UpperCamelCase	:

				"""simple docstring"""



				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	np.random.default_rng(self.seed			)
									SCREAMING_SNAKE_CASE				=	rng.integers(low=self.low		,	high=self.high		,	size=self.size			).tolist()


				def __iter__(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									return iter(self._sampled_values			)




				def __len__(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									return self.size






@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[0]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[-1]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[range(len(SCREAMING_SNAKE_CASE_			) // 2,     len(SCREAMING_SNAKE_CASE_			) // 2 + 1_0_2_4			)]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[RandIter(0,     len(SCREAMING_SNAKE_CASE_			),     1_0_2_4,     seed=4_2			)]

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	{'num examples': SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('generating dataset'			)
					SCREAMING_SNAKE_CASE				=	generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE_,     chunk_size=SCREAMING_SNAKE_CASE_			)
					print('Functions'			)
					for func in functions:
										print(func.__name__			)
										SCREAMING_SNAKE_CASE				=	func(SCREAMING_SNAKE_CASE_			)

					with open(SCREAMING_SNAKE_CASE_,     'wb'			) as f:
										f.write(json.dumps(SCREAMING_SNAKE_CASE_			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_table_100B()
