





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class 					UpperCamelCase_    (       unittest.TestCase      ):
							lowerCamelCase_          =   (ScoreSdeVeScheduler,)
							lowerCamelCase_          =   ()


							@property
							def        _snake_case					(							self      :List[Any]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= 4
									SCREAMING_SNAKE_CASE__								= 3
									SCREAMING_SNAKE_CASE__								= 8
									SCREAMING_SNAKE_CASE__								= 8

									SCREAMING_SNAKE_CASE__								= torch.rand((batch_size, num_channels, height, width)						)

									return sample


							@property
							def        _snake_case					(							self      :List[str]						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= 4
									SCREAMING_SNAKE_CASE__								= 3
									SCREAMING_SNAKE_CASE__								= 8
									SCREAMING_SNAKE_CASE__								= 8

									SCREAMING_SNAKE_CASE__								= batch_size * num_channels * height * width
									SCREAMING_SNAKE_CASE__								= torch.arange(__A						)
									SCREAMING_SNAKE_CASE__								= sample.reshape(__A							,			__A							,			__A							,			__A						)
									SCREAMING_SNAKE_CASE__								= sample / num_elems
									SCREAMING_SNAKE_CASE__								= sample.permute(3							,			0							,			1							,			2						)

									return sample


							def        _snake_case					(							self      :Optional[Any]						)				->				Dict:



									"""simple docstring"""



									def model(__A      :List[Any]							,			__A      :int							,			*__A      :List[Any]						):
											return sample * t / (t + 1)

									return model


							def        _snake_case					(							self      :Optional[int]							,			**__A      :Tuple						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= {
									    "num_train_timesteps": 2000,
									    "snr": 0.1_5,
									    "sigma_min": 0.0_1,
									    "sigma_max": 1348,
									    "sampling_eps": 1E-5,
									}

									config.update(**__A						)
									return config


							def        _snake_case					(							self      :List[Any]							,			__A      :List[str]=0							,			**__A      :str						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= dict(self.forward_default_kwargs						)

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								= self.dummy_sample
											SCREAMING_SNAKE_CASE__								= 0.1 * sample

											SCREAMING_SNAKE_CASE__								= self.get_scheduler_config(**__A						)
											SCREAMING_SNAKE_CASE__								= scheduler_class(**__A						)

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__A						)
													SCREAMING_SNAKE_CASE__								= scheduler_class.from_pretrained(__A						)

											SCREAMING_SNAKE_CASE__								= scheduler.step_pred(
											    __A							,			__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample
											SCREAMING_SNAKE_CASE__								= new_scheduler.step_pred(
											    __A							,			__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample

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

											SCREAMING_SNAKE_CASE__								= scheduler.step_correct(__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample
											SCREAMING_SNAKE_CASE__								= new_scheduler.step_correct(
											    __A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample

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


							def        _snake_case					(							self      :str							,			__A      :List[Any]=0							,			**__A      :List[Any]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= dict(self.forward_default_kwargs						)
									kwargs.update(__A						)

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								= self.dummy_sample
											SCREAMING_SNAKE_CASE__								= 0.1 * sample

											SCREAMING_SNAKE_CASE__								= self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__								= scheduler_class(**__A						)

											with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(__A						)
													SCREAMING_SNAKE_CASE__								= scheduler_class.from_pretrained(__A						)

											SCREAMING_SNAKE_CASE__								= scheduler.step_pred(
											    __A							,			__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample
											SCREAMING_SNAKE_CASE__								= new_scheduler.step_pred(
											    __A							,			__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample

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

											SCREAMING_SNAKE_CASE__								= scheduler.step_correct(__A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample
											SCREAMING_SNAKE_CASE__								= new_scheduler.step_correct(
											    __A							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample

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


							def        _snake_case					(							self      :Any						)				->				Tuple:



									"""simple docstring"""



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


							def        _snake_case					(							self      :List[str]						)				->				Any:



									"""simple docstring"""



									for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]							,			[1, 100, 1000]						):
											self.check_over_configs(sigma_min=__A							,			sigma_max=__A						)


							def        _snake_case					(							self      :Union[str, Any]						)				->				Optional[int]:



									"""simple docstring"""



									for t in [0.1, 0.5, 0.7_5]:
											self.check_over_forward(time_step=__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= dict(self.forward_default_kwargs						)

									SCREAMING_SNAKE_CASE__								= self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								= self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								= scheduler_class(**__A						)

									SCREAMING_SNAKE_CASE__								= 3

									SCREAMING_SNAKE_CASE__								= self.dummy_model()
									SCREAMING_SNAKE_CASE__								= self.dummy_sample_deter

									scheduler.set_sigmas(__A						)
									scheduler.set_timesteps(__A						)
									SCREAMING_SNAKE_CASE__								= torch.manual_seed(0						)

									for i, t in enumerate(scheduler.timesteps						):
											SCREAMING_SNAKE_CASE__								= scheduler.sigmas[i]

											for _ in range(scheduler.config.correct_steps						):
													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								= model(__A							,			__A						)
													SCREAMING_SNAKE_CASE__								= scheduler.step_correct(__A							,			__A							,			generator=__A							,			**__A						).prev_sample

											with torch.no_grad():
													SCREAMING_SNAKE_CASE__								= model(__A							,			__A						)

											SCREAMING_SNAKE_CASE__								= scheduler.step_pred(__A							,			__A							,			__A							,			generator=__A							,			**__A						)
											SCREAMING_SNAKE_CASE__								= output.prev_sample, output.prev_sample_mean

									SCREAMING_SNAKE_CASE__								= torch.sum(torch.abs(__A						)						)
									SCREAMING_SNAKE_CASE__								= torch.mean(torch.abs(__A						)						)

									assert np.isclose(result_sum.item()							,			143_7275_8528.0						)
									assert np.isclose(result_mean.item()							,			1871_4530.0						)


							def        _snake_case					(							self      :Optional[Any]						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= dict(self.forward_default_kwargs						)

									SCREAMING_SNAKE_CASE__								= kwargs.pop("""num_inference_steps"""							,			__A						)

									for scheduler_class in self.scheduler_classes:
											SCREAMING_SNAKE_CASE__								= self.get_scheduler_config()
											SCREAMING_SNAKE_CASE__								= scheduler_class(**__A						)

											SCREAMING_SNAKE_CASE__								= self.dummy_sample
											SCREAMING_SNAKE_CASE__								= 0.1 * sample

											if num_inference_steps is not None and hasattr(__A							,			"""set_timesteps"""						):
													scheduler.set_timesteps(__A						)
											elif num_inference_steps is not None and not hasattr(__A							,			"""set_timesteps"""						):
													SCREAMING_SNAKE_CASE__								= num_inference_steps

											SCREAMING_SNAKE_CASE__								= scheduler.step_pred(__A							,			0							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample
											SCREAMING_SNAKE_CASE__								= scheduler.step_pred(__A							,			1							,			__A							,			generator=torch.manual_seed(0						)							,			**__A						).prev_sample

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






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

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






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= {"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE__								= (select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""      )
				SCREAMING_SNAKE_CASE__								= datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
				SCREAMING_SNAKE_CASE__								= generate_example_dataset(
				    os.path.join(UpperCamelCase__   ,				"""dataset.arrow"""      )   ,				UpperCamelCase__   ,				num_examples=UpperCamelCase__      )
				print("""Functions"""      )
				for func in functions:
						print(func.__name__      )
						SCREAMING_SNAKE_CASE__								= func(UpperCamelCase__      )

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


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