





'''simple docstring'''


import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=30      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=2      ,  )     ->				int:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scope
     _lowerCAmelCase						      =   encoder_stride

     # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     _lowerCAmelCase						      =   (image_size // patch_size) ** 2
     _lowerCAmelCase						      =   num_patches + 1




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase						      =   None
     if self.use_labels:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size]      ,  self.type_sequence_label_size		)

     _lowerCAmelCase						      =   self.get_config()

     return config, pixel_values, labels




 def 				_snake_case (						self		)     ->				Any:
     return ViTConfig(
         image_size=self.image_size      ,  patch_size=self.patch_size      ,  num_channels=self.num_channels      ,  hidden_size=self.hidden_size      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  is_decoder=_lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   ViTModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   ViTForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.reconstruction.shape      ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   ViTForMaskedImageModeling(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.reconstruction.shape      ,  (self.batch_size, 1, self.image_size, self.image_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.type_sequence_label_size
     _lowerCAmelCase						      =   ViTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   ViTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)





 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				List[Any]								=       (
     (
         ViTModel,
         ViTForImageClassification,
         ViTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				Tuple								=       (
     {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
     if is_torch_available()
     else {}
 )
 __lowerCamelCase    :				Optional[Any]								=       True

 __lowerCamelCase    :				str								=       False
 __lowerCamelCase    :				List[Any]								=       False
 __lowerCamelCase    :				int								=       False




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   ViTModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  has_text_modality=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				str:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="ViT does not use inputs_embeds"		)
 def 				_snake_case (						self		)     ->				Any:
     pass




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)		)
         _lowerCAmelCase						      =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCAmelCase      ,  nn.Linear		)		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         _lowerCAmelCase						      =   inspect.signature(model.forward		)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						      =   [*signature.parameters.keys()]

         _lowerCAmelCase						      =   ["pixel_values"]
         self.assertListEqual(arg_names[:1]      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)





 @slow
 def 				_snake_case (						self		)     ->				Tuple:
     for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   ViTModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
    return image





@require_torch
@require_vision
class  lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 				_snake_case (						self		)     ->				Any:
     return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"		) if is_vision_available() else None




 @slow
 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"		).to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.default_image_processor
     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor([-0.2744, 0.8215, -0.0836]		).to(_lowerCAmelCase		)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)




 @slow
 def 				_snake_case (						self		)     ->				List[Any]:
     # ViT models have an `interpolate_pos_encoding` argument in their forward method,
     # allowing to interpolate the pre-trained position embeddings in order to use
     # the model on higher resolutions. The DINO model by Facebook AI leverages this
     # to visualize self-attention on higher resolution images.
     _lowerCAmelCase						      =   ViTModel.from_pretrained("facebook/dino-vits8"		).to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   ViTImageProcessor.from_pretrained("facebook/dino-vits8"      ,  size=480		)
     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   inputs.pixel_values.to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  interpolate_pos_encoding=_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 3601, 384)		)
     self.assertEqual(outputs.last_hidden_state.shape      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor(
         [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]		).to(_lowerCAmelCase		)

     self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)





 @slow
 @require_accelerate
 @require_torch_gpu
 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   ViTModel.from_pretrained("facebook/dino-vits8"      ,  torch_dtype=torch.floataa      ,  device_map="auto"		)
     _lowerCAmelCase						      =   self.default_image_processor

     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   inputs.pixel_values.to(_lowerCAmelCase		)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase		)









'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       (DPMSolverSinglestepScheduler,)
 __lowerCamelCase    :				int								=       (("num_inference_steps", 25),)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "solver_order": 2,
         "prediction_type": "epsilon",
         "thresholding": False,
         "sample_max_value": 1.0,
         "algorithm_type": "dpmsolver++",
         "solver_type": "midpoint",
         "lambda_min_clipped": -float("inf"		),
         "variance_type": None,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				List[Any]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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




 def 				_snake_case (						self		)     ->				int:
     pass




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  **_lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   dict(self.forward_default_kwargs		)
     _lowerCAmelCase						      =   kwargs.pop("num_inference_steps"      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   self.dummy_sample
     _lowerCAmelCase						      =   0.1 * sample
     _lowerCAmelCase						      =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample
         _lowerCAmelCase						      =   new_scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		).prev_sample

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




 def 				_snake_case (						self      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase		)     ->				Tuple:
     if scheduler is None:
         _lowerCAmelCase						      =   self.scheduler_classes[0]
         _lowerCAmelCase						      =   self.get_scheduler_config(**_lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

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

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

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

     return sample




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   50
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase		)

     # make sure that the first t is uneven
     for i, t in enumerate(scheduler.timesteps[3:]		):
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		).prev_sample

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

     assert abs(result_mean.item() - 0.2574		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for timesteps in [25, 50, 100, 999, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     _lowerCAmelCase						      =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3

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

     _lowerCAmelCase						      =   self.full_loop(scheduler=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for order in [1, 2, 3]:
         for solver_type in ["midpoint", "heun"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  algorithm_type="dpmsolver++"      ,  solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     _lowerCAmelCase						      =   self.full_loop(
                         solver_order=_lowerCAmelCase      ,  solver_type=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  algorithm_type=_lowerCAmelCase      ,  )
                     assert not torch.isnan(_lowerCAmelCase		).any(), "Samples have nan numbers"




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)
     self.check_over_configs(lower_order_final=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
     self.check_over_configs(lambda_min_clipped=-5.1		)




 def 				_snake_case (						self		)     ->				str:
     self.check_over_configs(variance_type=_lowerCAmelCase		)
     self.check_over_configs(variance_type="learned_range"		)




 def 				_snake_case (						self		)     ->				int:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
         self.check_over_forward(num_inference_steps=_lowerCAmelCase      ,  time_step=0		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop()
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2791		) < 1E-3




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   self.full_loop(use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.2248		) < 1E-3




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.1453		) < 1E-3




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.full_loop(prediction_type="v_prediction"      ,  use_karras_sigmas=_lowerCAmelCase		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_mean.item() - 0.0649		) < 1E-3





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(thresholding=_lowerCAmelCase      ,  dynamic_thresholding_ratio=0		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   10
     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase		)

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

     assert sample.dtype == torch.floataa


