


'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):
			# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
			__a					:      int					=  (ScoreSdeVeScheduler,)
			__a					:      Tuple					=  ()




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = 4
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 3
								__SCREAMING_SNAKE_CASE					:							Any			   = 8
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = 8

								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = torch.rand((batch_size, num_channels, height, width)       )

								return sample




			@property
			def 			_snake_case   (      self       )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[str]			   = 4
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 3
								__SCREAMING_SNAKE_CASE					:							str			   = 8
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = 8

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = batch_size * num_channels * height * width
								__SCREAMING_SNAKE_CASE					:							Any			   = torch.arange(lowercase       )
								__SCREAMING_SNAKE_CASE					:							Dict			   = sample.reshape(lowercase					,      lowercase					,      lowercase					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = sample / num_elems
								__SCREAMING_SNAKE_CASE					:							Tuple			   = sample.permute(3					,      0					,      1					,      2       )

								return sample




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								def model(lowercase					,      lowercase					,      *lowercase       ):
													return sample * t / (t + 1)

								return model




			def 			_snake_case   (      self					,      **lowercase       )			->						Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Dict			   = {
								    '''num_train_timesteps''': 2_0_0_0,
								    '''snr''': 0.1_5,
								    '''sigma_min''': 0.0_1,
								    '''sigma_max''': 1_3_4_8,
								    '''sampling_eps''': 1e-5,
								}

								config.update(**lowercase       )
								return config




			def 			_snake_case   (      self					,      lowercase=0					,      **lowercase       )			->						int:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = dict(self.forward_default_kwargs       )

								for scheduler_class in self.scheduler_classes:
													__SCREAMING_SNAKE_CASE					:							str			   = self.dummy_sample
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = 0.1 * sample

													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.get_scheduler_config(**lowercase       )
													__SCREAMING_SNAKE_CASE					:							List[str]			   = scheduler_class(**lowercase       )

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase       )
																		__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = scheduler_class.from_pretrained(lowercase       )

													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = scheduler.step_pred(
													    lowercase					,      lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample
													__SCREAMING_SNAKE_CASE					:							Any			   = new_scheduler.step_pred(
													    lowercase					,      lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample

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

													__SCREAMING_SNAKE_CASE					:							Dict			   = scheduler.step_correct(lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample
													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = new_scheduler.step_correct(
													    lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample

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




			def 			_snake_case   (      self					,      lowercase=0					,      **lowercase       )			->						str:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = dict(self.forward_default_kwargs       )
								kwargs.update(lowercase       )

								for scheduler_class in self.scheduler_classes:
													__SCREAMING_SNAKE_CASE					:							List[Any]			   = self.dummy_sample
													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = 0.1 * sample

													__SCREAMING_SNAKE_CASE					:							Tuple			   = self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE					:							List[str]			   = scheduler_class(**lowercase       )

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase       )
																		__SCREAMING_SNAKE_CASE					:							List[str]			   = scheduler_class.from_pretrained(lowercase       )

													__SCREAMING_SNAKE_CASE					:							List[str]			   = scheduler.step_pred(
													    lowercase					,      lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample
													__SCREAMING_SNAKE_CASE					:							Dict			   = new_scheduler.step_pred(
													    lowercase					,      lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample

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

													__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = scheduler.step_correct(lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample
													__SCREAMING_SNAKE_CASE					:							str			   = new_scheduler.step_correct(
													    lowercase					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample

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




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


								'''simple docstring'''






								for timesteps in [1_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowercase       )




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


								'''simple docstring'''






								for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]					,      [1, 1_0_0, 1_0_0_0]       ):
													self.check_over_configs(sigma_min=lowercase					,      sigma_max=lowercase       )




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


								'''simple docstring'''






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




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							int			   = dict(self.forward_default_kwargs       )

								__SCREAMING_SNAKE_CASE					:							str			   = self.scheduler_classes[0]
								__SCREAMING_SNAKE_CASE					:							Dict			   = self.get_scheduler_config()
								__SCREAMING_SNAKE_CASE					:							Tuple			   = scheduler_class(**lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 3

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.dummy_model()
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.dummy_sample_deter

								scheduler.set_sigmas(lowercase       )
								scheduler.set_timesteps(lowercase       )
								__SCREAMING_SNAKE_CASE					:							int			   = torch.manual_seed(0       )

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

													for _ in range(scheduler.config.correct_steps       ):
																		with torch.no_grad():
																							__SCREAMING_SNAKE_CASE					:							List[str]			   = model(lowercase					,      lowercase       )
																		__SCREAMING_SNAKE_CASE					:							List[Any]			   = scheduler.step_correct(lowercase					,      lowercase					,      generator=lowercase					,      **lowercase       ).prev_sample

													with torch.no_grad():
																		__SCREAMING_SNAKE_CASE					:							Dict			   = model(lowercase					,      lowercase       )

													__SCREAMING_SNAKE_CASE					:							List[Any]			   = scheduler.step_pred(lowercase					,      lowercase					,      lowercase					,      generator=lowercase					,      **lowercase       )
													__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							List[str]			   = output.prev_sample, output.prev_sample_mean

								__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.sum(torch.abs(lowercase       )       )
								__SCREAMING_SNAKE_CASE					:							str			   = torch.mean(torch.abs(lowercase       )       )

								assert np.isclose(result_sum.item()					,      1_4_3_7_2_7_5_8_5_2_8.0       )
								assert np.isclose(result_mean.item()					,      1_8_7_1_4_5_3_0.0       )




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = dict(self.forward_default_kwargs       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = kwargs.pop('''num_inference_steps'''					,      lowercase       )

								for scheduler_class in self.scheduler_classes:
													__SCREAMING_SNAKE_CASE					:							List[str]			   = self.get_scheduler_config()
													__SCREAMING_SNAKE_CASE					:							Tuple			   = scheduler_class(**lowercase       )

													__SCREAMING_SNAKE_CASE					:							Any			   = self.dummy_sample
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase					,      '''set_timesteps'''       ):
																		scheduler.set_timesteps(lowercase       )
													elif num_inference_steps is not None and not hasattr(lowercase					,      '''set_timesteps'''       ):
																		__SCREAMING_SNAKE_CASE					:							List[str]			   = num_inference_steps

													__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = scheduler.step_pred(lowercase					,      0					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample
													__SCREAMING_SNAKE_CASE					:							Tuple			   = scheduler.step_pred(lowercase					,      1					,      lowercase					,      generator=torch.manual_seed(0       )					,      **lowercase       ).prev_sample

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






'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_A      =     TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=snake_case		)
class      SCREAMING_SNAKE_CASE_	( abc.ABC		):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			__a					:      str
			__a					:      ClassVar[Features]
			__a					:      ClassVar[Features]




			def 			_snake_case   (      self					,      lowercase       )			->						T:


								'''simple docstring'''






								return copy.deepcopy(self       )




			@property
			def 			_snake_case   (      self       )			->						Features:


								'''simple docstring'''






								return Features(**self.input_schema					,      **self.label_schema       )




			@property
			@abc.abstractmethod
			def 			_snake_case   (      self       )			->						Dict[str, str]:


								'''simple docstring'''






								raise NotImplementedError




			@classmethod
			def 			_snake_case   (      cls					,      lowercase       )			->						T:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = {f.name for f in dataclasses.fields(cls       )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )


