




'''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 DetrImageProcessor


class 			_a  (	unittest.TestCase ):
       def __init__(	self						:	Optional[Any]   ,				lowercase						:	Optional[Any]   ,				lowercase						:	Optional[int]=7   ,				lowercase						:	str=3   ,				lowercase						:	Union[str, Any]=30   ,				lowercase						:	Union[str, Any]=400   ,				lowercase						:	List[Any]=True   ,				lowercase						:	List[str]=None   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	int=1 / 255   ,				lowercase						:	Dict=True   ,				lowercase						:	str=[0.5, 0.5, 0.5]   ,				lowercase						:	Optional[int]=[0.5, 0.5, 0.5]   ,				lowercase						:	Dict=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		min_resolution
           UpperCAmelCase			       =		max_resolution
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean
           UpperCAmelCase			       =		image_std
           UpperCAmelCase			       =		do_pad
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_pad": self.do_pad,
           }







       def     A      (	self						:	Any   ,				lowercase						:	Any   ,				lowercase						:	List[str]=False				):



           '''simple docstring'''

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class 			_a  (	SCREAMING_SNAKE_CASE__							,							unittest.TestCase ):
       __a				:     List[Any]   				=					DetrImageProcessor if is_vision_available() else None
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		DetrImageProcessingTester(self				)
       @property
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''image_mean'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''image_std'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''do_rescale'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''rescale_factor'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''do_resize'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''size'''				)				)
           self.assertTrue(hasattr(lowerCamelCase__   ,				'''do_pad'''				)				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		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   ,				lowerCamelCase__				)

           UpperCAmelCase			       =		self.image_processing_class.from_dict(
               self.image_processor_dict   ,				size=42   ,				max_size=84   ,				pad_and_return_pixel_mask=lowerCamelCase__				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 42, '''longest_edge''': 84}				)
           self.assertEqual(image_processor.do_pad   ,				lowerCamelCase__				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

           # Test batched
           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__   ,				batched=lowerCamelCase__				)

           UpperCAmelCase			       =		image_processing(lowerCamelCase__   ,				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     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

           # Test batched
           UpperCAmelCase			       =		image_processing(lowerCamelCase__   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__   ,				batched=lowerCamelCase__				)

           self.assertEqual(
               encoded_images.shape   ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

           # Test batched
           UpperCAmelCase			       =		image_processing(lowerCamelCase__   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowerCamelCase__   ,				batched=lowerCamelCase__				)

           self.assertEqual(
               encoded_images.shape   ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )   ,				)
       @slow
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

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

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

           # encode them
           UpperCAmelCase			       =		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''				)
           UpperCAmelCase			       =		image_processing(images=lowerCamelCase__   ,				annotations=lowerCamelCase__   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowerCamelCase__				)

           UpperCAmelCase			       =		torch.tensor([0.2796, 0.3138, 0.3481]				)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				lowerCamelCase__   ,				atol=1E-4				)				)

           # verify area
           UpperCAmelCase			       =		torch.tensor([5887.9600, 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''']   ,				lowerCamelCase__				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowerCamelCase__				)
           UpperCAmelCase			       =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowerCamelCase__   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowerCamelCase__				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowerCamelCase__				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([75, 75, 63, 65, 17, 17]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowerCamelCase__				)				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowerCamelCase__				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowerCamelCase__				)				)







       @slow
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

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

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

           UpperCAmelCase			       =		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

           # encode them
           UpperCAmelCase			       =		DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''				)
           UpperCAmelCase			       =		image_processing(images=lowerCamelCase__   ,				annotations=lowerCamelCase__   ,				masks_path=lowerCamelCase__   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowerCamelCase__				)

           UpperCAmelCase			       =		torch.tensor([0.2796, 0.3138, 0.3481]				)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,				lowerCamelCase__   ,				atol=1E-4				)				)

           # verify area
           UpperCAmelCase			       =		torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,				lowerCamelCase__				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowerCamelCase__				)
           UpperCAmelCase			       =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowerCamelCase__   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowerCamelCase__				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowerCamelCase__				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([17, 17, 63, 75, 75, 93]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowerCamelCase__				)				)
           # verify masks
           UpperCAmelCase			       =		822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,				lowerCamelCase__				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowerCamelCase__				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowerCamelCase__				)				)









import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      (DDIMParallelScheduler,)
    __lowerCamelCase			: int					=      (("eta", 0.0), ("num_inference_steps", 50))






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       int          =   {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Dict          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       Tuple          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       Tuple          =   10, 0.0

        A :       Optional[int]          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Optional[int]          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       str          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def 			_lowerCAmelCase					(       self		):
        for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Tuple          =   self.scheduler_classes[0]
        A :       Optional[Any]          =   self.get_scheduler_config(steps_offset=1		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






    def 			_lowerCAmelCase					(       self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],		[0.002, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=lowerCamelCase__,		beta_end=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase__		)






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






    def 			_lowerCAmelCase					(       self		):
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        self.check_over_configs(thresholding=lowerCamelCase__		)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCamelCase__,		prediction_type=lowerCamelCase__,		sample_max_value=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, num_inference_steps in zip([1, 10, 50],		[10, 50, 500]		):
            self.check_over_forward(time_step=lowerCamelCase__,		num_inference_steps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, eta in zip([1, 10, 49],		[0.0, 0.5, 1.0]		):
            self.check_over_forward(time_step=lowerCamelCase__,		eta=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       List[str]          =   self.get_scheduler_config()
        A :       Dict          =   scheduler_class(**lowerCamelCase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,		400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,		960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,		486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,		998		) - 0.02		)		) < 1e-5






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.scheduler_classes[0]
        A :       Any          =   self.get_scheduler_config()
        A :       Union[str, Any]          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       List[str]          =   10, 0.0
        scheduler.set_timesteps(lowerCamelCase__		)

        A :       Any          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter
        A :       Dict          =   self.dummy_sample_deter + 0.1
        A :       Tuple          =   self.dummy_sample_deter - 0.1

        A :       Tuple          =   samplea.shape[0]
        A :       List[Any]          =   torch.stack([samplea, samplea, samplea],		dim=0		)
        A :       str          =   torch.arange(lowerCamelCase__		)[0:3, None].repeat(1,		lowerCamelCase__		)

        A :       Any          =   model(samples.flatten(0,		1		),		timesteps.flatten(0,		1		)		)
        A :       Any          =   scheduler.batch_step_no_noise(lowerCamelCase__,		timesteps.flatten(0,		1		),		samples.flatten(0,		1		),		lowerCamelCase__		)

        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.full_loop()

        A :       str          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.full_loop(prediction_type="""v_prediction"""		)

        A :       Any          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       Dict          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       str          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       int          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       List[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3


