



import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class _lowerCamelCase:
        @staticmethod
        def     UpperCamelCase  (      *lowerCamelCase,   **lowerCamelCase) ->     str:




            """simple docstring"""

            pass






@is_pipeline_test
@require_vision
@require_torch
class _lowerCamelCase(		unittest.TestCase     ):
       lowercase_				:			List[Any]									=    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Dict   	=	pipeline(
               'zero-shot-object-detection',   model='hf-internal-testing/tiny-random-owlvit-object-detection')

           _lowercase :							List[Any]   	=	[
               {
                   'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                   'candidate_labels': ['cat', 'remote', 'couch'],
               }
           ]
           return object_detector, examples
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	object_detector(examples[0],   threshold=0.0)

           _lowercase :							Optional[int]   	=	len(_A)
           self.assertGreater(_A,   0)
           self.assertEqual(
               _A,   [
                   {
                       'score': ANY(_A),
                       'label': ANY(_A),
                       'box': {'xmin': ANY(_A), 'ymin': ANY(_A), 'xmax': ANY(_A), 'ymax': ANY(_A)},
                   }
                   for i in range(_A)
               ],   )
       @require_tf
       @unittest.skip('Zero Shot Object Detection not implemented in TF')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           pass
       @require_torch
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	pipeline(
               'zero-shot-object-detection',   model='hf-internal-testing/tiny-random-owlvit-object-detection')

           _lowercase :							Any   	=	object_detector(
               './tests/fixtures/tests_samples/COCO/000000039769.png',   candidate_labels=['cat', 'remote', 'couch'],   threshold=0.6_4,   )

           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                   {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                   {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                   {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                   {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                   {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                   {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                   {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
                   {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
               ],   )

           _lowercase :							str   	=	object_detector(
               [
                   {
                       'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                       'candidate_labels': ['cat', 'remote', 'couch'],
                   }
               ],   threshold=0.6_4,   )

           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   [
                       {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                       {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                       {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                       {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                       {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                       {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                       {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                       {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
                       {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                   ]
               ],   )
       @require_torch
       @slow
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	pipeline('zero-shot-object-detection')

           _lowercase :							List[str]   	=	object_detector(
               'http://images.cocodataset.org/val2017/000000039769.jpg',   candidate_labels=['cat', 'remote', 'couch'],   )
           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                   {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                   {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                   {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                   {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
               ],   )

           _lowercase :							Union[str, Any]   	=	object_detector(
               [
                   {
                       'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                       'candidate_labels': ['cat', 'remote', 'couch'],
                   },
                   {
                       'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                       'candidate_labels': ['cat', 'remote', 'couch'],
                   },
               ],   )
           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   [
                       {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                       {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                       {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                       {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                       {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
                   ],
                   [
                       {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                       {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                       {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                       {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                       {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
                   ],
               ],   )
       @require_tf
       @unittest.skip('Zero Shot Object Detection not implemented in TF')
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           pass
       @require_torch
       @slow
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

           _lowercase :							int   	=	0.2
           _lowercase :							Union[str, Any]   	=	pipeline('zero-shot-object-detection')

           _lowercase :							int   	=	object_detector(
               'http://images.cocodataset.org/val2017/000000039769.jpg',   candidate_labels=['cat', 'remote', 'couch'],   threshold=_A,   )
           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                   {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                   {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
               ],   )




       @require_torch
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Tuple   	=	2
           _lowercase :							List[str]   	=	pipeline('zero-shot-object-detection')

           _lowercase :							Any   	=	object_detector(
               'http://images.cocodataset.org/val2017/000000039769.jpg',   candidate_labels=['cat', 'remote', 'couch'],   top_k=_A,   )
           self.assertEqual(
               nested_simplify(_A,   decimals=4),   [
                   {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                   {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
               ],   )






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[Any]									=    (DPMSolverSinglestepScheduler,)
       lowercase_				:			List[str]									=    (("""num_inference_steps""", 25),)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	{
               'num_train_timesteps': 10_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               '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     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	dict(self.forward_default_kwargs)
           _lowercase :							Union[str, Any]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.dummy_sample
           _lowercase :							Optional[int]   	=	0.1 * sample
           _lowercase :							Optional[int]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

               _lowercase	,       _lowercase :							List[Any]   	=	sample, sample
               for t in range(lowerCamelCase,   time_step + scheduler.config.solver_order + 1):
                   _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
                   _lowercase :							int   	=	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     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self,   lowerCamelCase=0,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	dict(self.forward_default_kwargs)
           _lowercase :							List[str]   	=	kwargs.pop('num_inference_steps',   lowerCamelCase)
           _lowercase :							List[str]   	=	self.dummy_sample
           _lowercase :							str   	=	0.1 * sample
           _lowercase :							Optional[Any]   	=	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

           for scheduler_class in self.scheduler_classes:
               _lowercase :							Any   	=	self.get_scheduler_config()
               _lowercase :							List[str]   	=	scheduler_class(**lowerCamelCase)
               scheduler.set_timesteps(lowerCamelCase)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(lowerCamelCase)
                   _lowercase :							List[Any]   	=	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)
                   _lowercase :							List[str]   	=	dummy_past_residuals[: new_scheduler.config.solver_order]

               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase).prev_sample
               _lowercase :							List[Any]   	=	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     UpperCamelCase  (      self,   lowerCamelCase=None,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           if scheduler is None:
               _lowercase :							str   	=	self.scheduler_classes[0]
               _lowercase :							int   	=	self.get_scheduler_config(**lowerCamelCase)
               _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							List[Any]   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Dict   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Optional[int]   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Optional[int]   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           return sample
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           _lowercase :							Dict   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							Optional[int]   	=	50
           _lowercase :							Union[str, Any]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter
           scheduler.set_timesteps(lowerCamelCase)

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

           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_5_7_4) < 1E-3
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							List[str]   	=	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           _lowercase :							List[str]   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3

           _lowercase :							str   	=	DEISMultistepScheduler.from_config(scheduler.config)
           _lowercase :							List[str]   	=	DPMSolverMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Tuple   	=	UniPCMultistepScheduler.from_config(scheduler.config)
           _lowercase :							Any   	=	DPMSolverSinglestepScheduler.from_config(scheduler.config)

           _lowercase :							Any   	=	self.full_loop(scheduler=lowerCamelCase)
           _lowercase :							Optional[int]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

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




           """simple docstring"""

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           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,   )
                           _lowercase :							Optional[Any]   	=	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     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           self.check_over_configs(lower_order_final=lowerCamelCase)
           self.check_over_configs(lower_order_final=lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           self.check_over_configs(lambda_min_clipped=-float('inf'))
           self.check_over_configs(lambda_min_clipped=-5.1)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.check_over_configs(variance_type=lowerCamelCase)
           self.check_over_configs(variance_type='learned_range')
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
               self.check_over_forward(num_inference_steps=lowerCamelCase,   time_step=0)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.full_loop()
           _lowercase :							Union[str, Any]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_7_9_1) < 1E-3
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(use_karras_sigmas=lowerCamelCase)
           _lowercase :							List[str]   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.2_2_4_8) < 1E-3
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction')
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.1_4_5_3) < 1E-3
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.full_loop(prediction_type='v_prediction',   use_karras_sigmas=lowerCamelCase)
           _lowercase :							str   	=	torch.mean(torch.abs(lowerCamelCase))

           assert abs(result_mean.item() - 0.0_6_4_9) < 1E-3




       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.scheduler_classes[0]
           _lowercase :							Optional[int]   	=	self.get_scheduler_config(thresholding=lowerCamelCase,   dynamic_thresholding_ratio=0)
           _lowercase :							Any   	=	scheduler_class(**lowerCamelCase)

           _lowercase :							str   	=	10
           _lowercase :							List[str]   	=	self.dummy_model()
           _lowercase :							Tuple   	=	self.dummy_sample_deter.half()
           scheduler.set_timesteps(lowerCamelCase)

           for i, t in enumerate(scheduler.timesteps):
               _lowercase :							Tuple   	=	model(lowerCamelCase,   lowerCamelCase)
               _lowercase :							Dict   	=	scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           assert sample.dtype == torch.floataa


