



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 							a_  (		unittest.TestCase				):
						# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
						UpperCamelCase__      :						Tuple				       =(ScoreSdeVeScheduler,)
						UpperCamelCase__      :						Optional[int]				       =()

						@property
						def 	__a       (    self	:List[str])		->					Dict:
												UpperCAmelCase_     							=     4
												UpperCAmelCase_     							=     3
												UpperCAmelCase_     							=     8
												UpperCAmelCase_     							=     8

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

												return sample

						@property
						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     4
												UpperCAmelCase_     							=     3
												UpperCAmelCase_     							=     8
												UpperCAmelCase_     							=     8

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

												return sample

						def 	__a       (    self	:List[str])		->					Dict:
												def model(_lowercase	:Union[str, Any]   ,						_lowercase	:str   ,						*_lowercase	:Optional[Any]):
																		return sample * t / (t + 1)

												return model

						def 	__a       (    self	:List[str]   ,						**_lowercase	:Optional[Any])		->					Optional[int]:
												UpperCAmelCase_     							=     {
												    '''num_train_timesteps''': 2000,
												    '''snr''': 0.15,
												    '''sigma_min''': 0.01,
												    '''sigma_max''': 1348,
												    '''sampling_eps''': 1E-5,
												}

												config.update(**_lowercase)
												return config

						def 	__a       (    self	:Optional[int]   ,						_lowercase	:List[str]=0   ,						**_lowercase	:List[Any])		->					Optional[int]:
												UpperCAmelCase_     							=     dict(self.forward_default_kwargs)

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

																		UpperCAmelCase_     							=     self.get_scheduler_config(**_lowercase)
																		UpperCAmelCase_     							=     scheduler_class(**_lowercase)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(_lowercase)
																								UpperCAmelCase_     							=     scheduler_class.from_pretrained(_lowercase)

																		UpperCAmelCase_     							=     scheduler.step_pred(
																		    _lowercase   ,						_lowercase   ,						_lowercase   ,						generator=torch.manual_seed(0)   ,						**_lowercase).prev_sample
																		UpperCAmelCase_     							=     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"

																		UpperCAmelCase_     							=     scheduler.step_correct(_lowercase   ,						_lowercase   ,						generator=torch.manual_seed(0)   ,						**_lowercase).prev_sample
																		UpperCAmelCase_     							=     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 	__a       (    self	:Optional[int]   ,						_lowercase	:List[str]=0   ,						**_lowercase	:Any)		->					int:
												UpperCAmelCase_     							=     dict(self.forward_default_kwargs)
												kwargs.update(_lowercase)

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

																		UpperCAmelCase_     							=     self.get_scheduler_config()
																		UpperCAmelCase_     							=     scheduler_class(**_lowercase)

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(_lowercase)
																								UpperCAmelCase_     							=     scheduler_class.from_pretrained(_lowercase)

																		UpperCAmelCase_     							=     scheduler.step_pred(
																		    _lowercase   ,						_lowercase   ,						_lowercase   ,						generator=torch.manual_seed(0)   ,						**_lowercase).prev_sample
																		UpperCAmelCase_     							=     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"

																		UpperCAmelCase_     							=     scheduler.step_correct(_lowercase   ,						_lowercase   ,						generator=torch.manual_seed(0)   ,						**_lowercase).prev_sample
																		UpperCAmelCase_     							=     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 	__a       (    self	:List[Any])		->					str:
												for timesteps in [10, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=_lowercase)

						def 	__a       (    self	:Optional[Any])		->					str:
												for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]   ,						[1, 100, 1000]):
																		self.check_over_configs(sigma_min=_lowercase   ,						sigma_max=_lowercase)

						def 	__a       (    self	:Union[str, Any])		->					List[Any]:
												for t in [0.1, 0.5, 0.75]:
																		self.check_over_forward(time_step=_lowercase)

						def 	__a       (    self	:int)		->					str:
												UpperCAmelCase_     							=     dict(self.forward_default_kwargs)

												UpperCAmelCase_     							=     self.scheduler_classes[0]
												UpperCAmelCase_     							=     self.get_scheduler_config()
												UpperCAmelCase_     							=     scheduler_class(**_lowercase)

												UpperCAmelCase_     							=     3

												UpperCAmelCase_     							=     self.dummy_model()
												UpperCAmelCase_     							=     self.dummy_sample_deter

												scheduler.set_sigmas(_lowercase)
												scheduler.set_timesteps(_lowercase)
												UpperCAmelCase_     							=     torch.manual_seed(0)

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

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

																		with torch.no_grad():
																								UpperCAmelCase_     							=     model(_lowercase   ,						_lowercase)

																		UpperCAmelCase_     							=     scheduler.step_pred(_lowercase   ,						_lowercase   ,						_lowercase   ,						generator=_lowercase   ,						**_lowercase)
																		UpperCAmelCase_							,      UpperCAmelCase_     							=     output.prev_sample, output.prev_sample_mean

												UpperCAmelCase_     							=     torch.sum(torch.abs(_lowercase))
												UpperCAmelCase_     							=     torch.mean(torch.abs(_lowercase))

												assert np.isclose(result_sum.item()   ,						14_372_758_528.0)
												assert np.isclose(result_mean.item()   ,						18_714_530.0)




						def 	__a       (    self	:Union[str, Any])		->					Any:
												UpperCAmelCase_     							=     dict(self.forward_default_kwargs)

												UpperCAmelCase_     							=     kwargs.pop('''num_inference_steps'''   ,						_lowercase)

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase_     							=     self.get_scheduler_config()
																		UpperCAmelCase_     							=     scheduler_class(**_lowercase)

																		UpperCAmelCase_     							=     self.dummy_sample
																		UpperCAmelCase_     							=     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'''):
																								UpperCAmelCase_     							=     num_inference_steps

																		UpperCAmelCase_     							=     scheduler.step_pred(_lowercase   ,						0   ,						_lowercase   ,						generator=torch.manual_seed(0)   ,						**_lowercase).prev_sample
																		UpperCAmelCase_     							=     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)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_	       =   {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTHybridForImageClassification",
							    "ViTHybridModel",
							    "ViTHybridPreTrainedModel",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





