


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_       :



      '''simple docstring'''







      @staticmethod
      def A      (				*snake_case_ ,       **snake_case_						)   ->			Dict:







         '''simple docstring'''

         pass
@is_pipeline_test
@require_vision
@require_torch
class 	lowerCamelCase_       (				unittest.TestCase				):



     '''simple docstring'''







     __UpperCAmelCase            =				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






     def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			List[Any]:







        '''simple docstring'''

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

        __lowercase 					=   [
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples






     def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Union[str, Any]:







        '''simple docstring'''

        __lowercase 					=   object_detector(examples[0] ,       threshold=0.0						)

        __lowercase 					=   len(lowerCamelCase__						)
        self.assertGreater(lowerCamelCase__ ,       0						)
        self.assertEqual(
            lowerCamelCase__ ,       [
                {
                    '''score''': ANY(lowerCamelCase__						),
                    '''label''': ANY(lowerCamelCase__						),
                    '''box''': {'''xmin''': ANY(lowerCamelCase__						), '''ymin''': ANY(lowerCamelCase__						), '''xmax''': ANY(lowerCamelCase__						), '''ymax''': ANY(lowerCamelCase__						)},
                }
                for i in range(lowerCamelCase__						)
            ] ,       )






     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
     def A      (				self						)   ->			str:







        '''simple docstring'''

        pass






     @require_torch
     def A      (				self						)   ->			Any:







        '''simple docstring'''

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

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

        self.assertEqual(
            nested_simplify(lowerCamelCase__ ,       decimals=4						) ,       [
                {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
                {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
                {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
            ] ,       )

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

        self.assertEqual(
            nested_simplify(lowerCamelCase__ ,       decimals=4						) ,       [
                [
                    {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                    {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                    {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}},
                    {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                    {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                    {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}},
                    {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
                    {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}},
                    {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}},
                ]
            ] ,       )






     @require_torch
     @slow
     def A      (				self						)   ->			List[str]:







        '''simple docstring'''

        __lowercase 					=   pipeline('''zero-shot-object-detection'''						)

        __lowercase 					=   object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,       candidate_labels=['''cat''', '''remote''', '''couch'''] ,       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__ ,       decimals=4						) ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
                {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
                {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
                {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
            ] ,       )

        __lowercase 					=   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(lowerCamelCase__ ,       decimals=4						) ,       [
                [
                    {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
                    {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
                    {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
                    {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
                    {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
                ],
                [
                    {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
                    {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
                    {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
                    {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}},
                    {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}},
                ],
            ] ,       )






     @require_tf
     @unittest.skip('''Zero Shot Object Detection not implemented in TF'''						)
     def A      (				self						)   ->			Optional[int]:







        '''simple docstring'''

        pass






     @require_torch
     @slow
     def A      (				self						)   ->			str:







        '''simple docstring'''

        __lowercase 					=   0.2
        __lowercase 					=   pipeline('''zero-shot-object-detection'''						)

        __lowercase 					=   object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,       candidate_labels=['''cat''', '''remote''', '''couch'''] ,       threshold=lowerCamelCase__ ,       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__ ,       decimals=4						) ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
                {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}},
            ] ,       )






     @require_torch
     @slow
     def A      (				self						)   ->			int:







        '''simple docstring'''

        __lowercase 					=   2
        __lowercase 					=   pipeline('''zero-shot-object-detection'''						)

        __lowercase 					=   object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''' ,       candidate_labels=['''cat''', '''remote''', '''couch'''] ,       top_k=lowerCamelCase__ ,       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__ ,       decimals=4						) ,       [
                {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}},
                {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}},
            ] ,       )






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DEISMultistepScheduler,)
					__UpperCAmelCase            =				(("num_inference_steps", 25),)






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''solver_order''': 2,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								pass






					def A      (				self ,       snake_case_=0 ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)
								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)
								__lowercase 					=   self.dummy_sample
								__lowercase 					=   0.1 * sample
								__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   new_scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self ,       snake_case_=None ,       **snake_case_						)   ->			Optional[int]:







								'''simple docstring'''

								if scheduler is None:
											__lowercase 					=   self.scheduler_classes[0]
											__lowercase 					=   self.get_scheduler_config(**snake_case_						)
											__lowercase 					=   scheduler_class(**snake_case_						)

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

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_						)

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

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   dict(self.forward_default_kwargs						)

								__lowercase 					=   kwargs.pop('''num_inference_steps''' ,       snake_case_						)

								for scheduler_class in self.scheduler_classes:
											__lowercase 					=   self.get_scheduler_config()
											__lowercase 					=   scheduler_class(**snake_case_						)

											__lowercase 					=   self.dummy_sample
											__lowercase 					=   0.1 * sample

											if num_inference_steps is not None and hasattr(snake_case_ ,       '''set_timesteps'''						):
														scheduler.set_timesteps(snake_case_						)
											elif num_inference_steps is not None and not hasattr(snake_case_ ,       '''set_timesteps'''						):
														__lowercase 					=   num_inference_steps

											# copy over dummy past residuals (must be done after set_timesteps)
											__lowercase 					=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
											__lowercase 					=   dummy_past_residuals[: scheduler.config.solver_order]

											__lowercase 					=   scheduler.timesteps[5]
											__lowercase 					=   scheduler.timesteps[6]

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       **snake_case_						).prev_sample

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






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   DEISMultistepScheduler(**self.get_scheduler_config()						)
								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3

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

								__lowercase 					=   self.full_loop(scheduler=snake_case_						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






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







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for order in [1, 2, 3]:
											for solver_type in ["logrho"]:
														for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       algorithm_type='''deis''' ,       solver_order=snake_case_ ,       solver_type=snake_case_ ,       )






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







								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_						)






					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								for algorithm_type in ["deis"]:
											for solver_type in ["logrho"]:
														for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																				self.check_over_configs(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				__lowercase 					=   self.full_loop(
																				    solver_order=snake_case_ ,       solver_type=snake_case_ ,       prediction_type=snake_case_ ,       algorithm_type=snake_case_ ,       )
																				assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								self.check_over_configs(lower_order_final=snake_case_						)
								self.check_over_configs(lower_order_final=snake_case_						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
											self.check_over_forward(num_inference_steps=snake_case_ ,       time_step=0						)






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.full_loop()
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.2_3_9_1_6						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_mean.item() - 0.0_9_1						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(thresholding=snake_case_ ,       dynamic_thresholding_ratio=0						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase 					=   1_0
								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case_						)

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

								assert sample.dtype == torch.floataa


