


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class      snake_case__	(   unittest.TestCase	):
				# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
				__lowerCAmelCase		=   (ScoreSdeVeScheduler,)
				__lowerCAmelCase		=   ()
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   4
										__magic_name__					:				str				=   3
										__magic_name__					:				Optional[int]				=   8
										__magic_name__					:				str				=   8

										__magic_name__					:				Optional[int]				=   torch.rand((batch_size, num_channels, height, width)   )

										return sample
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   4
										__magic_name__					:				List[Any]				=   3
										__magic_name__					:				Any				=   8
										__magic_name__					:				Dict				=   8

										__magic_name__					:				str				=   batch_size * num_channels * height * width
										__magic_name__					:				Dict				=   torch.arange(_UpperCAmelCase   )
										__magic_name__					:				Union[str, Any]				=   sample.reshape(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   )
										__magic_name__					:				List[Any]				=   sample / num_elems
										__magic_name__					:				List[Any]				=   sample.permute(3  ,   0  ,   1  ,   2   )

										return sample
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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

										return model
				def 			UpperCamelCase_    (	self  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   {
										    'num_train_timesteps': 2000,
										    'snr': 0.15,
										    'sigma_min': 0.01,
										    'sigma_max': 1348,
										    'sampling_eps': 1E-5,
										}

										config.update(**_UpperCAmelCase   )
										return config
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




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

										for scheduler_class in self.scheduler_classes:
																__magic_name__					:				List[str]				=   self.dummy_sample
																__magic_name__					:				Optional[Any]				=   0.1 * sample

																__magic_name__					:				Any				=   self.get_scheduler_config(**_UpperCAmelCase   )
																__magic_name__					:				Dict				=   scheduler_class(**_UpperCAmelCase   )

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

																__magic_name__					:				Optional[int]				=   scheduler.step_pred(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample
																__magic_name__					:				Dict				=   new_scheduler.step_pred(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample

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

																__magic_name__					:				str				=   scheduler.step_correct(_UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample
																__magic_name__					:				int				=   new_scheduler.step_correct(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample

																assert torch.sum(torch.abs(output - new_output   )   ) < 1E-5, "Scheduler correction are not identical"
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				int				=   dict(self.forward_default_kwargs   )
										kwargs.update(_UpperCAmelCase   )

										for scheduler_class in self.scheduler_classes:
																__magic_name__					:				Tuple				=   self.dummy_sample
																__magic_name__					:				List[str]				=   0.1 * sample

																__magic_name__					:				int				=   self.get_scheduler_config()
																__magic_name__					:				str				=   scheduler_class(**_UpperCAmelCase   )

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCAmelCase   )
																						__magic_name__					:				int				=   scheduler_class.from_pretrained(_UpperCAmelCase   )

																__magic_name__					:				List[Any]				=   scheduler.step_pred(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample
																__magic_name__					:				Union[str, Any]				=   new_scheduler.step_pred(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample

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

																__magic_name__					:				int				=   scheduler.step_correct(_UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample
																__magic_name__					:				int				=   new_scheduler.step_correct(
																    _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample

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




										"""simple docstring"""




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




										"""simple docstring"""




										for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]  ,   [1, 100, 1000]   ):
																self.check_over_configs(sigma_min=_UpperCAmelCase  ,   sigma_max=_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   dict(self.forward_default_kwargs   )

										__magic_name__					:				List[str]				=   self.scheduler_classes[0]
										__magic_name__					:				Dict				=   self.get_scheduler_config()
										__magic_name__					:				int				=   scheduler_class(**_UpperCAmelCase   )

										__magic_name__					:				str				=   3

										__magic_name__					:				str				=   self.dummy_model()
										__magic_name__					:				List[str]				=   self.dummy_sample_deter

										scheduler.set_sigmas(_UpperCAmelCase   )
										scheduler.set_timesteps(_UpperCAmelCase   )
										__magic_name__					:				str				=   torch.manual_seed(0   )

										for i, t in enumerate(scheduler.timesteps   ):
																__magic_name__					:				Union[str, Any]				=   scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps   ):
																						with torch.no_grad():
																												__magic_name__					:				int				=   model(_UpperCAmelCase  ,   _UpperCAmelCase   )
																						__magic_name__					:				Optional[Any]				=   scheduler.step_correct(_UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=_UpperCAmelCase  ,   **_UpperCAmelCase   ).prev_sample

																with torch.no_grad():
																						__magic_name__					:				Dict				=   model(_UpperCAmelCase  ,   _UpperCAmelCase   )

																__magic_name__					:				Optional[Any]				=   scheduler.step_pred(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   generator=_UpperCAmelCase  ,   **_UpperCAmelCase   )
																__magic_name__		,			__magic_name__					:				Tuple				=   output.prev_sample, output.prev_sample_mean

										__magic_name__					:				List[Any]				=   torch.sum(torch.abs(_UpperCAmelCase   )   )
										__magic_name__					:				Optional[Any]				=   torch.mean(torch.abs(_UpperCAmelCase   )   )

										assert np.isclose(result_sum.item()  ,   14372758528.0   )
										assert np.isclose(result_mean.item()  ,   18714530.0   )





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   dict(self.forward_default_kwargs   )

										__magic_name__					:				Tuple				=   kwargs.pop('num_inference_steps'  ,   _UpperCAmelCase   )

										for scheduler_class in self.scheduler_classes:
																__magic_name__					:				List[Any]				=   self.get_scheduler_config()
																__magic_name__					:				List[str]				=   scheduler_class(**_UpperCAmelCase   )

																__magic_name__					:				int				=   self.dummy_sample
																__magic_name__					:				Tuple				=   0.1 * sample

																if num_inference_steps is not None and hasattr(_UpperCAmelCase  ,   'set_timesteps'   ):
																						scheduler.set_timesteps(_UpperCAmelCase   )
																elif num_inference_steps is not None and not hasattr(_UpperCAmelCase  ,   'set_timesteps'   ):
																						__magic_name__					:				Any				=   num_inference_steps

																__magic_name__					:				str				=   scheduler.step_pred(_UpperCAmelCase  ,   0  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample
																__magic_name__					:				Dict				=   scheduler.step_pred(_UpperCAmelCase  ,   1  ,   _UpperCAmelCase  ,   generator=torch.manual_seed(0   )  ,   **_UpperCAmelCase   ).prev_sample

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


