




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 ConditionalDetrImageProcessor
class 						_A (  unittest.TestCase				):






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






       """simple docstring"""







       lowercase :       List[str]       =			size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
       lowercase :       Any       =			parent
       lowercase :       Optional[Any]       =			batch_size
       lowercase :       str       =			num_channels
       lowercase :       str       =			min_resolution
       lowercase :       str       =			max_resolution
       lowercase :       Dict       =			do_resize
       lowercase :       Optional[int]       =			size
       lowercase :       str       =			do_normalize
       lowercase :       List[Any]       =			image_mean
       lowercase :       str       =			image_std
       lowercase :       Dict       =			do_rescale
       lowercase :       List[str]       =			rescale_factor
       lowercase :       Optional[Any]       =			do_pad






  def  __a      (  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  __a      (  self		:     int				,						_A		:     int				,						_A		:     Any=False				)		->   Optional[int]:






       """simple docstring"""







       if not batched:
            lowercase :       Union[str, Any]       =			image_inputs[0]
            if isinstance(_A				,						Image.Image				):
                 lowercase      ,	lowercase :       Union[str, Any]       =			image.size
            else:
                 lowercase      ,	lowercase :       List[Any]       =			image.shape[1], image.shape[2]
            if w < h:
                 lowercase :       int       =			int(self.size['''shortest_edge'''] * h / w				)
                 lowercase :       Tuple       =			self.size['''shortest_edge''']
            elif w > h:
                 lowercase :       Dict       =			self.size['''shortest_edge''']
                 lowercase :       Dict       =			int(self.size['''shortest_edge'''] * w / h				)
            else:
                 lowercase :       Tuple       =			self.size['''shortest_edge''']
                 lowercase :       Dict       =			self.size['''shortest_edge''']

       else:
            lowercase :       Optional[int]       =			[]
            for image in image_inputs:
                 lowercase      ,	lowercase :       Optional[Any]       =			self.get_expected_values([image]				)
                 expected_values.append((expected_height, expected_width)				)
            lowercase :       List[Any]       =			max(_A				,						key=lambda _A				: item[0]				)[0]
            lowercase :       Any       =			max(_A				,						key=lambda _A				: item[1]				)[1]

       return expected_height, expected_width


@require_torch
@require_vision
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Dict			=      ConditionalDetrImageProcessor if is_vision_available() else None






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			ConditionalDetrImageProcessingTester(self				)






  @property
  def  __a      (  self		:     List[str]				)		->   str:






       """simple docstring"""







       return self.image_processor_tester.prepare_image_processor_dict()






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






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(_A				,						'''image_mean'''				)				)
       self.assertTrue(hasattr(_A				,						'''image_std'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_normalize'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_resize'''				)				)
       self.assertTrue(hasattr(_A				,						'''size'''				)				)






  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







       lowercase :       Any       =			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				,						_A				)

       lowercase :       int       =			self.image_processing_class.from_dict(
           self.image_processor_dict				,						size=42				,						max_size=84				,						pad_and_return_pixel_mask=_A				)
       self.assertEqual(image_processor.size				,						{'''shortest_edge''': 42, '''longest_edge''': 84}				)
       self.assertEqual(image_processor.do_pad				,						_A				)






  def  __a      (  self		:     Tuple				)		->   List[Any]:






       """simple docstring"""







       pass






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






       """simple docstring"""







       lowercase :       List[str]       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       lowercase :       Union[str, Any]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						Image.Image				)

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

       lowercase      ,	lowercase :       List[str]       =			self.image_processor_tester.get_expected_values(_A				)

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

       # Test batched
       lowercase      ,	lowercase :       Any       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)

       lowercase :       Union[str, Any]       =			image_processing(_A				,						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		:     int				)		->   List[Any]:






       """simple docstring"""







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

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

       lowercase      ,	lowercase :       str       =			self.image_processor_tester.get_expected_values(_A				)

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

       # Test batched
       lowercase :       Tuple       =			image_processing(_A				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       Any       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)

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






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase :       int       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						torchify=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						torch.Tensor				)

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

       lowercase      ,	lowercase :       Any       =			self.image_processor_tester.get_expected_values(_A				)

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

       # Test batched
       lowercase :       List[Any]       =			image_processing(_A				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       str       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)

       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[str]				)		->   int:






       """simple docstring"""







       lowercase :       Optional[int]       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,						'''r'''				) as f:
            lowercase :       Tuple       =			json.loads(f.read()				)

       lowercase :       Tuple       =			{'''image_id''': 39_769, '''annotations''': target}

       # encode them
       lowercase :       int       =			ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''				)
       lowercase :       Union[str, Any]       =			image_processing(images=_A				,						annotations=_A				,						return_tensors='''pt'''				)

       # verify pixel values
       lowercase :       Any       =			torch.Size([1, 3, 800, 1_066]				)
       self.assertEqual(encoding['''pixel_values'''].shape				,						_A				)

       lowercase :       Any       =			torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,						_A				,						atol=1E-4				)				)

       # verify area
       lowercase :       Dict       =			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''']				,						_A				)				)
       # verify boxes
       lowercase :       Union[str, Any]       =			torch.Size([6, 4]				)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,						_A				)
       lowercase :       Union[str, Any]       =			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,						_A				,						atol=1E-3				)				)
       # verify image_id
       lowercase :       int       =			torch.tensor([39_769]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,						_A				)				)
       # verify is_crowd
       lowercase :       List[Any]       =			torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,						_A				)				)
       # verify class_labels
       lowercase :       List[str]       =			torch.tensor([75, 75, 63, 65, 17, 17]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,						_A				)				)
       # verify orig_size
       lowercase :       List[str]       =			torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,						_A				)				)
       # verify size
       lowercase :       Union[str, Any]       =			torch.tensor([800, 1_066]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,						_A				)				)






  @slow
  def  __a      (  self		:     List[str]				)		->   Optional[int]:






       """simple docstring"""







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

       lowercase :       Union[str, Any]       =			{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

       lowercase :       Any       =			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

       # encode them
       lowercase :       Any       =			ConditionalDetrImageProcessor(format='''coco_panoptic'''				)
       lowercase :       str       =			image_processing(images=_A				,						annotations=_A				,						masks_path=_A				,						return_tensors='''pt'''				)

       # verify pixel values
       lowercase :       Union[str, Any]       =			torch.Size([1, 3, 800, 1_066]				)
       self.assertEqual(encoding['''pixel_values'''].shape				,						_A				)

       lowercase :       int       =			torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,						_A				,						atol=1E-4				)				)

       # verify area
       lowercase :       List[str]       =			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''']				,						_A				)				)
       # verify boxes
       lowercase :       List[str]       =			torch.Size([6, 4]				)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,						_A				)
       lowercase :       Union[str, Any]       =			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,						_A				,						atol=1E-3				)				)
       # verify image_id
       lowercase :       str       =			torch.tensor([39_769]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,						_A				)				)
       # verify is_crowd
       lowercase :       Any       =			torch.tensor([0, 0, 0, 0, 0, 0]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,						_A				)				)
       # verify class_labels
       lowercase :       Union[str, Any]       =			torch.tensor([17, 17, 63, 75, 75, 93]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,						_A				)				)
       # verify masks
       lowercase :       Any       =			822_873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,						_A				)
       # verify orig_size
       lowercase :       Any       =			torch.tensor([480, 640]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,						_A				)				)
       # verify size
       lowercase :       Dict       =			torch.tensor([800, 1_066]				)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,						_A				)				)





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      (PNDMScheduler,)
  _UpperCamelCase       :      Optional[int]			=      (('''num_inference_steps''', 5_0),)






  def  __a      (  self		:     Union[str, Any]				,						**_A		:     Optional[Any]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       List[str]       =			{
           '''num_train_timesteps''': 1_000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_A				)
       return config






  def  __a      (  self		:     Optional[Any]				,						_A		:     Any=0				,						**_A		:     int				)		->   Dict:






       """simple docstring"""







       lowercase :       List[Any]       =			dict(self.forward_default_kwargs				)
       lowercase :       Union[str, Any]       =			kwargs.pop('''num_inference_steps'''				,						_A				)
       lowercase :       Optional[Any]       =			self.dummy_sample
       lowercase :       str       =			0.1 * sample
       lowercase :       Union[str, Any]       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            lowercase :       Dict       =			self.get_scheduler_config(**_A				)
            lowercase :       int       =			scheduler_class(**_A				)
            scheduler.set_timesteps(_A				)
            # copy over dummy past residuals
            lowercase :       List[str]       =			dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_A				)
                 lowercase :       Union[str, Any]       =			scheduler_class.from_pretrained(_A				)
                 new_scheduler.set_timesteps(_A				)
                 # copy over dummy past residuals
                 lowercase :       int       =			dummy_past_residuals[:]

            lowercase :       List[str]       =			scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       List[Any]       =			new_scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample

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

            lowercase :       Dict       =			scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       List[Any]       =			new_scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample

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






  def  __a      (  self		:     int				)		->   List[str]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[Any]				,						_A		:     Optional[int]=0				,						**_A		:     str				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Optional[int]       =			dict(self.forward_default_kwargs				)
       lowercase :       Optional[int]       =			kwargs.pop('''num_inference_steps'''				,						_A				)
       lowercase :       Union[str, Any]       =			self.dummy_sample
       lowercase :       Any       =			0.1 * sample
       lowercase :       int       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            lowercase :       List[str]       =			self.get_scheduler_config()
            lowercase :       List[str]       =			scheduler_class(**_A				)
            scheduler.set_timesteps(_A				)

            # copy over dummy past residuals (must be after setting timesteps)
            lowercase :       Union[str, Any]       =			dummy_past_residuals[:]

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

                 # copy over dummy past residual (must be after setting timesteps)
                 lowercase :       Optional[Any]       =			dummy_past_residuals[:]

            lowercase :       int       =			scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       Tuple       =			new_scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample

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

            lowercase :       Any       =			scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       Union[str, Any]       =			new_scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample

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






  def  __a      (  self		:     Union[str, Any]				,						**_A		:     Optional[int]				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			self.scheduler_classes[0]
       lowercase :       Optional[int]       =			self.get_scheduler_config(**_A				)
       lowercase :       int       =			scheduler_class(**_A				)

       lowercase :       Any       =			10
       lowercase :       Optional[int]       =			self.dummy_model()
       lowercase :       Tuple       =			self.dummy_sample_deter
       scheduler.set_timesteps(_A				)

       for i, t in enumerate(scheduler.prk_timesteps				):
            lowercase :       List[str]       =			model(_A				,						_A				)
            lowercase :       Any       =			scheduler.step_prk(_A				,						_A				,						_A				).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps				):
            lowercase :       Dict       =			model(_A				,						_A				)
            lowercase :       List[Any]       =			scheduler.step_plms(_A				,						_A				,						_A				).prev_sample

       return sample






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






       """simple docstring"""







       lowercase :       int       =			dict(self.forward_default_kwargs				)

       lowercase :       Union[str, Any]       =			kwargs.pop('''num_inference_steps'''				,						_A				)

       for scheduler_class in self.scheduler_classes:
            lowercase :       Optional[Any]       =			self.get_scheduler_config()
            lowercase :       List[Any]       =			scheduler_class(**_A				)

            lowercase :       Dict       =			self.dummy_sample
            lowercase :       str       =			0.1 * sample

            if num_inference_steps is not None and hasattr(_A				,						'''set_timesteps'''				):
                 scheduler.set_timesteps(_A				)
            elif num_inference_steps is not None and not hasattr(_A				,						'''set_timesteps'''				):
                 lowercase :       List[str]       =			num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            lowercase :       Any       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            lowercase :       Optional[Any]       =			dummy_past_residuals[:]

            lowercase :       Optional[int]       =			scheduler.step_prk(_A				,						0				,						_A				,						**_A				).prev_sample
            lowercase :       str       =			scheduler.step_prk(_A				,						1				,						_A				,						**_A				).prev_sample

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

            lowercase :       Dict       =			scheduler.step_plms(_A				,						0				,						_A				,						**_A				).prev_sample
            lowercase :       Tuple       =			scheduler.step_plms(_A				,						1				,						_A				,						**_A				).prev_sample

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






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






       """simple docstring"""







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






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







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

       lowercase :       str       =			self.scheduler_classes[0]
       lowercase :       int       =			self.get_scheduler_config(steps_offset=1				)
       lowercase :       Union[str, Any]       =			scheduler_class(**_A				)
       scheduler.set_timesteps(10				)
       assert torch.equal(
           scheduler.timesteps				,						torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)				,						)






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






       """simple docstring"""







       for beta_start, beta_end in zip([0.0_001, 0.001]				,						[0.002, 0.02]				):
            self.check_over_configs(beta_start=_A				,						beta_end=_A				)






  def  __a      (  self		:     List[str]				)		->   Tuple:






       """simple docstring"""







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






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






       """simple docstring"""







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






  def  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       for t in [1, 5, 10]:
            self.check_over_forward(time_step=_A				)






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






       """simple docstring"""







       for t, num_inference_steps in zip([1, 5, 10]				,						[10, 50, 100]				):
            self.check_over_forward(num_inference_steps=_A				)






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






       """simple docstring"""







       lowercase :       str       =			27

       for scheduler_class in self.scheduler_classes:
            lowercase :       List[str]       =			self.dummy_sample
            lowercase :       Union[str, Any]       =			0.1 * sample

            lowercase :       int       =			self.get_scheduler_config()
            lowercase :       Union[str, Any]       =			scheduler_class(**_A				)

            scheduler.set_timesteps(_A				)

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]				):
                 lowercase :       Union[str, Any]       =			scheduler.step_prk(_A				,						_A				,						_A				).prev_sample






  def  __a      (  self		:     List[Any]				)		->   List[Any]:






       """simple docstring"""







       with self.assertRaises(_A				):
            lowercase :       Dict       =			self.scheduler_classes[0]
            lowercase :       Union[str, Any]       =			self.get_scheduler_config()
            lowercase :       Optional[int]       =			scheduler_class(**_A				)

            scheduler.step_plms(self.dummy_sample				,						1				,						self.dummy_sample				).prev_sample






  def  __a      (  self		:     str				)		->   Dict:






       """simple docstring"""







       lowercase :       List[str]       =			self.full_loop()
       lowercase :       List[Any]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Any       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 198.1_318				) < 1E-2
       assert abs(result_mean.item() - 0.2_580				) < 1E-3






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






       """simple docstring"""







       lowercase :       List[str]       =			self.full_loop(prediction_type='''v_prediction'''				)
       lowercase :       Optional[int]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Tuple       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 67.3_986				) < 1E-2
       assert abs(result_mean.item() - 0.0_878				) < 1E-3






  def  __a      (  self		:     str				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Any       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       List[str]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Optional[int]       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 230.0_399				) < 1E-2
       assert abs(result_mean.item() - 0.2_995				) < 1E-3






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






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       int       =			torch.sum(torch.abs(_A				)				)
       lowercase :       int       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 186.9_482				) < 1E-2
       assert abs(result_mean.item() - 0.2_434				) < 1E-3