





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      A       (	unittest.TestCase						):



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

         UpperCAmelCase_											=size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         UpperCAmelCase_											=parent
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=min_resolution
         UpperCAmelCase_											=max_resolution
         UpperCAmelCase_											=do_resize
         UpperCAmelCase_											=size
         UpperCAmelCase_											=do_normalize
         UpperCAmelCase_											=image_mean
         UpperCAmelCase_											=image_std
         UpperCAmelCase_											=do_rescale
         UpperCAmelCase_											=rescale_factor
         UpperCAmelCase_											=do_pad



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    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 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						int=False       )						->    int:
         '''simple docstring'''

         if not batched:
            UpperCAmelCase_											=image_inputs[0]
            if isinstance(_lowerCAmelCase		,  Image.Image       ):
               UpperCAmelCase_						,       UpperCAmelCase_											=image.size
            else:
               UpperCAmelCase_						,       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_						,       UpperCAmelCase_											=self.get_expected_values([image]       )
               expected_values.append((expected_height, expected_width)       )
            UpperCAmelCase_											=max(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : item[0]       )[0]
            UpperCAmelCase_											=max(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : item[1]       )[1]

         return expected_height, expected_width

@require_torch
@require_vision
class      A       (	__lowercase		,      unittest.TestCase						):
      _snake_case							     =DetaImageProcessor if is_vision_available() else None



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=DetaImageProcessingTester(self       )



      @property
      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''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_resize"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_rescale"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "do_pad"       )       )
         self.assertTrue(hasattr(_lowerCAmelCase		,  "size"       )       )



      def 	lowerCAmelCase__    (	self:						Tuple       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size		,  {"shortest_edge": 18, "longest_edge": 1333}       )
         self.assertEqual(image_processor.do_pad		,  _lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         pass



      def 	lowerCAmelCase__    (	self:						Any       )						->    Dict:
         '''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_						,       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_						,       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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''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_						,       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_						,       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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[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_						,       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_						,       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 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[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": 3_9769, "annotations": target}

         # encode them
         UpperCAmelCase_											=DetaImageProcessor()
         UpperCAmelCase_											=image_processing(images=_lowerCAmelCase		,  annotations=_lowerCAmelCase		,  return_tensors="pt"       )

         # verify pixel values
         UpperCAmelCase_											=torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["pixel_values"].shape		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         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([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         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, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )






      @slow
      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    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": 3_9769, "segments_info": target}

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

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

         # verify pixel values
         UpperCAmelCase_											=torch.Size([1, 3, 800, 1066]       )
         self.assertEqual(encoding["pixel_values"].shape		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,  _lowerCAmelCase		,  atol=1e-4       )       )

         # verify area
         UpperCAmelCase_											=torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 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([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,  _lowerCAmelCase		,  atol=1e-3       )       )
         # verify image_id
         UpperCAmelCase_											=torch.tensor([3_9769]       )
         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_											=82_2873
         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, 1066]       )
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,  _lowerCAmelCase       )       )











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

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

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

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

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,  [0.0_02, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

         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:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         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:						int       )						->    Tuple:
         '''simple docstring'''

         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:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=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_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 0.3_24_60       )       ) < 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_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

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

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=self.dummy_sample_deter + 0.1
         UpperCAmelCase_											=self.dummy_sample_deter - 0.1

         UpperCAmelCase_											=samplea.shape[0]
         UpperCAmelCase_											=torch.stack([samplea, samplea, samplea]		,  dim=0       )
         UpperCAmelCase_											=torch.arange(_lowerCAmelCase       )[0:3, None].repeat(1		,  _lowerCAmelCase       )

         UpperCAmelCase_											=model(samples.flatten(0		,  1       )		,  timesteps.flatten(0		,  1       )       )
         UpperCAmelCase_											=scheduler.batch_step_no_noise(_lowerCAmelCase		,  timesteps.flatten(0		,  1       )		,  samples.flatten(0		,  1       )		,  _lowerCAmelCase       )

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

         assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
         assert abs(result_mean.item() - 0.49_82       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

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

         assert abs(result_sum.item() - 1_72.00_67       ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

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

         assert abs(result_sum.item() - 52.53_02       ) < 1e-2
         assert abs(result_mean.item() - 0.06_84       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.82_95       ) < 1e-2
         assert abs(result_mean.item() - 0.19_51       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.07_84       ) < 1e-2
         assert abs(result_mean.item() - 0.19_41       ) < 1e-3




