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 						lowercase    (     unittest.TestCase     ):




       def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=7	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					Optional[int]=30	,							_UpperCamelCase   :					List[Any]=400	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					List[Any]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[Any]=1 / 255	,							_UpperCamelCase   :					Optional[Any]=True	,							)					->				List[str]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
            SCREAMING_SNAKE_CASE    			=       parent
            SCREAMING_SNAKE_CASE    			=       batch_size
            SCREAMING_SNAKE_CASE    			=       num_channels
            SCREAMING_SNAKE_CASE    			=       min_resolution
            SCREAMING_SNAKE_CASE    			=       max_resolution
            SCREAMING_SNAKE_CASE    			=       do_resize
            SCREAMING_SNAKE_CASE    			=       size
            SCREAMING_SNAKE_CASE    			=       do_normalize
            SCREAMING_SNAKE_CASE    			=       image_mean
            SCREAMING_SNAKE_CASE    			=       image_std
            SCREAMING_SNAKE_CASE    			=       do_rescale
            SCREAMING_SNAKE_CASE    			=       rescale_factor
            SCREAMING_SNAKE_CASE    			=       do_pad




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



            '''simple docstring'''

            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 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



            '''simple docstring'''

            if not batched:
                 SCREAMING_SNAKE_CASE    			=       image_inputs[0]
                 if isinstance(_UpperCamelCase	,							Image.Image      ):
                      SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.size
                 else:
                      SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.shape[1], image.shape[2]
                 if w < h:
                      SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * h / w      )
                      SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
                 elif w > h:
                      SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
                      SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * w / h      )
                 else:
                      SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
                      SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]

            else:
                 SCREAMING_SNAKE_CASE    			=       []
                 for image in image_inputs:
                      SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.get_expected_values([image]      )
                      expected_values.append((expected_height, expected_width)      )
                 SCREAMING_SNAKE_CASE    			=       max(_UpperCamelCase	,							key=lambda _UpperCamelCase      : item[0]      )[0]
                 SCREAMING_SNAKE_CASE    			=       max(_UpperCamelCase	,							key=lambda _UpperCamelCase      : item[1]      )[1]

            return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
       lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




       @property
       def 	__snake_case(						self   :					int      )					->				Tuple:



            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"do_rescale"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"do_pad"      )      )
            self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
            self.assertEqual(image_processor.size	,							{"shortest_edge": 18, "longest_edge": 1_333}      )
            self.assertEqual(image_processor.do_pad	,							_UpperCamelCase      )




       def 	__snake_case(						self   :					str      )					->				List[Any]:



            '''simple docstring'''

            pass




       def 	__snake_case(						self   :					List[Any]      )					->				int:



            '''simple docstring'''

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

            # Test not batched input
            SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

            # Test batched
            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

            SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



            '''simple docstring'''

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

            # Test not batched input
            SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

            # Test batched
            SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

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




       def 	__snake_case(						self   :					str      )					->				Dict:



            '''simple docstring'''

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

            # Test not batched input
            SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

            # Test batched
            SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

            SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

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




       @slow
       def 	__snake_case(						self   :					Dict      )					->				str:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
            with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,							"r"      ) as f:
                 SCREAMING_SNAKE_CASE    			=       json.loads(f.read()      )

            SCREAMING_SNAKE_CASE    			=       {"image_id": 39_769, "annotations": target}

            # encode them
            SCREAMING_SNAKE_CASE    			=       DetaImageProcessor()
            SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

            # verify pixel values
            SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
            self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

            SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

            # verify area
            SCREAMING_SNAKE_CASE    			=       torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
            # verify boxes
            SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
            self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
            SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-3      )      )
            # verify image_id
            SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
            # verify is_crowd
            SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
            # verify class_labels
            SCREAMING_SNAKE_CASE    			=       torch.tensor([75, 75, 63, 65, 17, 17]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
            # verify orig_size
            SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
            # verify size
            SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





       @slow
       def 	__snake_case(						self   :					Dict      )					->				List[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
            with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,							"r"      ) as f:
                 SCREAMING_SNAKE_CASE    			=       json.loads(f.read()      )

            SCREAMING_SNAKE_CASE    			=       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

            SCREAMING_SNAKE_CASE    			=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

            # encode them
            SCREAMING_SNAKE_CASE    			=       DetaImageProcessor(format="coco_panoptic"      )
            SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							masks_path=_UpperCamelCase	,							return_tensors="pt"      )

            # verify pixel values
            SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
            self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

            SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

            # verify area
            SCREAMING_SNAKE_CASE    			=       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
            # verify boxes
            SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
            self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
            SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-3      )      )
            # verify image_id
            SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
            # verify is_crowd
            SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
            # verify class_labels
            SCREAMING_SNAKE_CASE    			=       torch.tensor([17, 17, 63, 75, 75, 93]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
            # verify masks
            SCREAMING_SNAKE_CASE    			=       822_873
            self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							_UpperCamelCase      )
            # verify orig_size
            SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
            # verify size
            SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
            self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase      )




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



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




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



												'''simple docstring'''

												for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=_UpperCamelCase      )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07      ) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps	,							device=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if str(_UpperCamelCase      ).startswith("cpu"      ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




