
'''simple docstring'''





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class  UpperCamelCase__  (    unittest.TestCase):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 UpperCAmelCase__    :    Dict										=					(ScoreSdeVeScheduler,)
 UpperCAmelCase__    :    int										=					()






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







       '''simple docstring'''



       __A       	=			4
       __A       	=			3
       __A       	=			8
       __A       	=			8

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

       return sample






 @property
 def 	lowercase_						(			self  :Optional[Any]			)				->			int:







       '''simple docstring'''



       __A       	=			4
       __A       	=			3
       __A       	=			8
       __A       	=			8

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

       return sample






 def 	lowercase_						(			self  :Dict			)				->			Tuple:







       '''simple docstring'''



       def model(_A  :Optional[int]	,				_A  :str	,				*_A  :Union[str, Any]			):
             return sample * t / (t + 1)

       return model






 def 	lowercase_						(			self  :Optional[int]	,				**_A  :Optional[int]			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			{
           'num_train_timesteps': 2_000,
           'snr': 0.15,
           'sigma_min': 0.01,
           'sigma_max': 1_348,
           'sampling_eps': 1E-5,
       }

       config.update(**_A			)
       return config






 def 	lowercase_						(			self  :Tuple	,				_A  :Dict=0	,				**_A  :Optional[Any]			)				->			List[str]:







       '''simple docstring'''



       __A       	=			dict(self.forward_default_kwargs			)

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

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

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

             __A       	=			scheduler.step_pred(
                 _A	,				_A	,				_A	,				generator=torch.manual_seed(0			)	,				**_A			).prev_sample
             __A       	=			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"

             __A       	=			scheduler.step_correct(_A	,				_A	,				generator=torch.manual_seed(0			)	,				**_A			).prev_sample
             __A       	=			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 	lowercase_						(			self  :int	,				_A  :Optional[Any]=0	,				**_A  :List[Any]			)				->			Optional[Any]:







       '''simple docstring'''



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

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

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

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

             __A       	=			scheduler.step_pred(
                 _A	,				_A	,				_A	,				generator=torch.manual_seed(0			)	,				**_A			).prev_sample
             __A       	=			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"

             __A       	=			scheduler.step_correct(_A	,				_A	,				generator=torch.manual_seed(0			)	,				**_A			).prev_sample
             __A       	=			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 	lowercase_						(			self  :int			)				->			str:







       '''simple docstring'''



       for timesteps in [10, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_A			)






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







       '''simple docstring'''



       for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]	,				[1, 100, 1_000]			):
             self.check_over_configs(sigma_min=_A	,				sigma_max=_A			)






 def 	lowercase_						(			self  :Tuple			)				->			Tuple:







       '''simple docstring'''



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






 def 	lowercase_						(			self  :Optional[int]			)				->			Any:







       '''simple docstring'''



       __A       	=			dict(self.forward_default_kwargs			)

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

       __A       	=			3

       __A       	=			self.dummy_model()
       __A       	=			self.dummy_sample_deter

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

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

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

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

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

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

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






 def 	lowercase_						(			self  :List[Any]			)				->			Dict:







       '''simple docstring'''



       __A       	=			dict(self.forward_default_kwargs			)

       __A       	=			kwargs.pop('num_inference_steps'	,				_A			)

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

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

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








'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






 def __init__(			self  :int	,				_A  :List[str]	,				_A  :Tuple=7	,				_A  :Dict=3	,				_A  :Any=18	,				_A  :List[Any]=30	,				_A  :Dict=400	,				_A  :Dict=True	,				_A  :Any=None	,				_A  :str=True	,				_A  :Union[str, Any]=None	,				_A  :Tuple=True	,				_A  :Any=[0.5, 0.5, 0.5]	,				_A  :List[str]=[0.5, 0.5, 0.5]	,				)				->			List[str]:







       '''simple docstring'''



       __A       	=			size if size is not None else {'height': 20, 'width': 20}
       __A       	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}

       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_center_crop
       __A       	=			crop_size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std






 def 	lowercase_						(			self  :Dict			)				->			List[str]:







       '''simple docstring'''



       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    int										=					DeiTImageProcessor if is_vision_available() else None
 UpperCAmelCase__    :    Dict										=					True






 def 	lowercase_						(			self  :Dict			)				->			str:







       '''simple docstring'''



       __A       	=			DeiTImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :Optional[Any]			)				->			Optional[Any]:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Tuple			)				->			Tuple:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_A	,				'do_resize'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)
       self.assertTrue(hasattr(_A	,				'do_center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'center_crop'			)			)
       self.assertTrue(hasattr(_A	,				'do_normalize'			)			)
       self.assertTrue(hasattr(_A	,				'image_mean'			)			)
       self.assertTrue(hasattr(_A	,				'image_std'			)			)






 def 	lowercase_						(			self  :int			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size	,				{'height': 20, 'width': 20}			)
       self.assertEqual(image_processor.crop_size	,				{'height': 18, 'width': 18}			)

       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84			)
       self.assertEqual(image_processor.size	,				{'height': 42, 'width': 42}			)
       self.assertEqual(image_processor.crop_size	,				{'height': 84, 'width': 84}			)






 def 	lowercase_						(			self  :str			)				->			Any:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       __A       	=			prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_A			)
       for image in image_inputs:
             self.assertIsInstance(_A	,				Image.Image			)

       # Test not batched input
       __A       	=			image_processing(image_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






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







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_A	,				numpify=_A			)
       for image in image_inputs:
             self.assertIsInstance(_A	,				np.ndarray			)

       # Test not batched input
       __A       	=			image_processing(image_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






 def 	lowercase_						(			self  :List[Any]			)				->			Dict:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_A	,				torchify=_A			)
       for image in image_inputs:
             self.assertIsInstance(_A	,				torch.Tensor			)

       # Test not batched input
       __A       	=			image_processing(image_inputs[0]	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)

       # Test batched
       __A       	=			image_processing(_A	,				return_tensors='pt'			).pixel_values
       self.assertEqual(
           encoded_images.shape	,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )	,				)






