


'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]     ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=1 / 2_55     ,	SCREAMING_SNAKE_CASE_=True     ,	)							->	int:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         __lowerCamelCase  :      int														=						size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      Union[str, Any]														=						batch_size
         __lowerCamelCase  :      str														=						num_channels
         __lowerCamelCase  :      Union[str, Any]														=						min_resolution
         __lowerCamelCase  :      int														=						max_resolution
         __lowerCamelCase  :      str														=						do_resize
         __lowerCamelCase  :      Tuple														=						size
         __lowerCamelCase  :      str														=						do_normalize
         __lowerCamelCase  :      Optional[int]														=						image_mean
         __lowerCamelCase  :      int														=						image_std
         __lowerCamelCase  :      Union[str, Any]														=						do_rescale
         __lowerCamelCase  :      Optional[Any]														=						rescale_factor
         __lowerCamelCase  :      Optional[int]														=						do_pad







    def  lowercase_							(				self      )							->	str:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False      )							->	Any:
         if not batched:
              __lowerCamelCase  :      str														=						image_inputs[0]
              if isinstance(SCREAMING_SNAKE_CASE_     ,	Image.Image      ):
                   __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						image.size
              else:
                   __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						image.shape[1], image.shape[2]
              if w < h:
                   __lowerCamelCase  :      List[Any]														=						int(self.size['shortest_edge'] * h / w      )
                   __lowerCamelCase  :      str														=						self.size['shortest_edge']
              elif w > h:
                   __lowerCamelCase  :      Optional[int]														=						self.size['shortest_edge']
                   __lowerCamelCase  :      List[str]														=						int(self.size['shortest_edge'] * w / h      )
              else:
                   __lowerCamelCase  :      Any														=						self.size['shortest_edge']
                   __lowerCamelCase  :      List[str]														=						self.size['shortest_edge']

         else:
              __lowerCamelCase  :      Optional[Any]														=						[]
              for image in image_inputs:
                   __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self.get_expected_values([image]      )
                   expected_values.append((expected_height, expected_width)      )
              __lowerCamelCase  :      Any														=						max(SCREAMING_SNAKE_CASE_     ,	key=lambda SCREAMING_SNAKE_CASE_      : item[0]      )[0]
              __lowerCamelCase  :      Any														=						max(SCREAMING_SNAKE_CASE_     ,	key=lambda SCREAMING_SNAKE_CASE_      : item[1]      )[1]

         return expected_height, expected_width







@require_torch
@require_vision
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Union[str, Any]					=     DetaImageProcessor if is_vision_available() else None







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Dict														=						DetaImageProcessingTester(self      )







    @property
    def  lowercase_							(				self      )							->	Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Dict														=						self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_mean'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'image_std'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_normalize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_resize'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_rescale'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'do_pad'      )      )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'size'      )      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Dict														=						self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size     ,	{'shortest_edge': 18, 'longest_edge': 13_33}      )
         self.assertEqual(image_processor.do_pad     ,	SCREAMING_SNAKE_CASE_      )







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







    def  lowercase_							(				self      )							->	Union[str, Any]:
         # Initialize image_processing
         __lowerCamelCase  :      List[Any]														=						self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __lowerCamelCase  :      Dict														=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image      )

         # Test not batched input
         __lowerCamelCase  :      Dict														=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values

         __lowerCamelCase							,			__lowerCamelCase  :      List[str]														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

         # Test batched
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,	batched=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						image_processing(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,	)







    def  lowercase_							(				self      )							->	Tuple:
         # Initialize image_processing
         __lowerCamelCase  :      List[Any]														=						self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __lowerCamelCase  :      Union[str, Any]														=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_     ,	numpify=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	np.ndarray      )

         # Test not batched input
         __lowerCamelCase  :      List[Any]														=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values

         __lowerCamelCase							,			__lowerCamelCase  :      int														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

         # Test batched
         __lowerCamelCase  :      Tuple														=						image_processing(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).pixel_values

         __lowerCamelCase							,			__lowerCamelCase  :      List[str]														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,	batched=SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             encoded_images.shape     ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,	)







    def  lowercase_							(				self      )							->	Optional[Any]:
         # Initialize image_processing
         __lowerCamelCase  :      List[Any]														=						self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __lowerCamelCase  :      Optional[Any]														=						prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_     ,	torchify=SCREAMING_SNAKE_CASE_      )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor      )

         # Test not batched input
         __lowerCamelCase  :      Optional[Any]														=						image_processing(image_inputs[0]     ,	return_tensors='pt'      ).pixel_values

         __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

         # Test batched
         __lowerCamelCase  :      Optional[Any]														=						image_processing(SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      ).pixel_values

         __lowerCamelCase							,			__lowerCamelCase  :      str														=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_     ,	batched=SCREAMING_SNAKE_CASE_      )

         self.assertEqual(
             encoded_images.shape     ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,	)







    @slow
    def  lowercase_							(				self      )							->	List[Any]:
         # prepare image and target
         __lowerCamelCase  :      Dict														=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,	'r'      ) as f:
              __lowerCamelCase  :      List[Any]														=						json.loads(f.read()      )

         __lowerCamelCase  :      List[str]														=						{'image_id': 3_97_69, 'annotations': target}

         # encode them
         __lowerCamelCase  :      Union[str, Any]														=						DetaImageProcessor()
         __lowerCamelCase  :      int														=						image_processing(images=SCREAMING_SNAKE_CASE_     ,	annotations=SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      )

         # verify pixel values
         __lowerCamelCase  :      Optional[int]														=						torch.Size([1, 3, 8_00, 10_66]      )
         self.assertEqual(encoding['pixel_values'].shape     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )

         # verify area
         __lowerCamelCase  :      Optional[Any]														=						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify boxes
         __lowerCamelCase  :      Optional[Any]														=						torch.Size([6, 4]      )
         self.assertEqual(encoding['labels'][0]['boxes'].shape     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-3      )      )
         # verify image_id
         __lowerCamelCase  :      Union[str, Any]														=						torch.tensor([3_97_69]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify is_crowd
         __lowerCamelCase  :      List[Any]														=						torch.tensor([0, 0, 0, 0, 0, 0]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify class_labels
         __lowerCamelCase  :      Optional[int]														=						torch.tensor([75, 75, 63, 65, 17, 17]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify orig_size
         __lowerCamelCase  :      List[Any]														=						torch.tensor([4_80, 6_40]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify size
         __lowerCamelCase  :      Optional[Any]														=						torch.tensor([8_00, 10_66]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,	SCREAMING_SNAKE_CASE_      )      )







    @slow
    def  lowercase_							(				self      )							->	Optional[Any]:
         # prepare image, target and masks_path
         __lowerCamelCase  :      List[str]														=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,	'r'      ) as f:
              __lowerCamelCase  :      Any														=						json.loads(f.read()      )

         __lowerCamelCase  :      int														=						{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

         __lowerCamelCase  :      List[Any]														=						pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

         # encode them
         __lowerCamelCase  :      Union[str, Any]														=						DetaImageProcessor(format='coco_panoptic'      )
         __lowerCamelCase  :      Optional[Any]														=						image_processing(images=SCREAMING_SNAKE_CASE_     ,	annotations=SCREAMING_SNAKE_CASE_     ,	masks_path=SCREAMING_SNAKE_CASE_     ,	return_tensors='pt'      )

         # verify pixel values
         __lowerCamelCase  :      List[str]														=						torch.Size([1, 3, 8_00, 10_66]      )
         self.assertEqual(encoding['pixel_values'].shape     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )

         # verify area
         __lowerCamelCase  :      Optional[Any]														=						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify boxes
         __lowerCamelCase  :      int														=						torch.Size([6, 4]      )
         self.assertEqual(encoding['labels'][0]['boxes'].shape     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[Any]														=						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-3      )      )
         # verify image_id
         __lowerCamelCase  :      Optional[int]														=						torch.tensor([3_97_69]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify is_crowd
         __lowerCamelCase  :      Union[str, Any]														=						torch.tensor([0, 0, 0, 0, 0, 0]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify class_labels
         __lowerCamelCase  :      Optional[Any]														=						torch.tensor([17, 17, 63, 75, 75, 93]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify masks
         __lowerCamelCase  :      Any														=						82_28_73
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,	SCREAMING_SNAKE_CASE_      )
         # verify orig_size
         __lowerCamelCase  :      Tuple														=						torch.tensor([4_80, 6_40]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,	SCREAMING_SNAKE_CASE_      )      )
         # verify size
         __lowerCamelCase  :      Tuple														=						torch.tensor([8_00, 10_66]      )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,	SCREAMING_SNAKE_CASE_      )      )













'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       (DPMSolverSinglestepScheduler,)
 __lowerCamelCase    :				int								=       (("num_inference_steps", 25),)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
         "prediction_type": "epsilon",
         "thresholding": False,
         "sample_max_value": 1.0,
         "algorithm_type": "dpmsolver++",
         "solver_type": "midpoint",
         "lambda_min_clipped": -float("inf"		),
         "variance_type": None,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)
         # copy over dummy past residuals
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   scheduler_class.from_pretrained(_lowerCAmelCase		)
             new_scheduler.set_timesteps(_lowerCAmelCase		)
             # copy over dummy past residuals
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase      ,      _lowerCAmelCase						      =   sample, sample
         for t in range(_lowerCAmelCase      ,  time_step + scheduler.config.solver_order + 1		):
             _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
             _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self		)     ->				int:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
         _lowerCAmelCase						      =   self.get_scheduler_config()
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)
         scheduler.set_timesteps(_lowerCAmelCase		)

         # copy over dummy past residuals (must be after setting timesteps)
         _lowerCAmelCase						      =   dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCAmelCase		)
             _lowerCAmelCase						      =   scheduler_class.from_pretrained(_lowerCAmelCase		)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(_lowerCAmelCase		)

             # copy over dummy past residual (must be after setting timesteps)
             _lowerCAmelCase						      =   dummy_past_residuals[: new_scheduler.config.solver_order]

         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
         _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Tuple:
     if scheduler is None:
         _lowerCAmelCase						      =   self.scheduler_classes[0]
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

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

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     return sample




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   50
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2574		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3

     _lowerCAmelCase						      =   DEISMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   UniPCMultistepScheduler.from_config(scheduler.config		)
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for order in [1, 2, 3]:
         for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  algorithm_type="dpmsolver++"      ,  solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     _lowerCAmelCase						      =   self.full_loop(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(variance_type=_lowerCAmelCase		)
     self.check_over_configs(variance_type="learned_range"		)




 def 				_snake_case (						self		)     ->				int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=_lowerCAmelCase      ,  time_step=0		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop()
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2248		) < 1E-3




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.1453		) < 1E-3




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"      ,  use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.0649		) < 1E-3





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(thresholding=_lowerCAmelCase      ,  dynamic_thresholding_ratio=0		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase		)

     for i, t in enumerate(scheduler.timesteps		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

     assert sample.dtype == torch.floataa


