

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image






class        UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Optional[Any]	,   __lowerCamelCase						:		str	,   __lowerCamelCase						:		str=2	,   __lowerCamelCase						:		Optional[Any]=True	,   __lowerCamelCase						:		List[str]=False	,   __lowerCamelCase						:		Tuple=10	,   __lowerCamelCase						:		List[Any]=3	,   __lowerCamelCase						:		Tuple=32 * 8	,   __lowerCamelCase						:		Dict=32 * 8	,   __lowerCamelCase						:		Optional[Any]=4	,   __lowerCamelCase						:		int=64	,   )		->							Dict:
       SCREAMING_SNAKE_CASE__    =    parent
       SCREAMING_SNAKE_CASE__    =    batch_size
       SCREAMING_SNAKE_CASE__    =    is_training
       SCREAMING_SNAKE_CASE__    =    use_auxiliary_loss
       SCREAMING_SNAKE_CASE__    =    num_queries
       SCREAMING_SNAKE_CASE__    =    num_channels
       SCREAMING_SNAKE_CASE__    =    min_size
       SCREAMING_SNAKE_CASE__    =    max_size
       SCREAMING_SNAKE_CASE__    =    num_labels
       SCREAMING_SNAKE_CASE__    =    hidden_dim
       SCREAMING_SNAKE_CASE__    =    hidden_dim






  def       lowercase_		(							self						:		str					)		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__    =    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]					).to(
           __lowerCamelCase					)

       SCREAMING_SNAKE_CASE__    =    torch.ones([self.batch_size, self.min_size, self.max_size]	,   device=__lowerCamelCase					)

       SCREAMING_SNAKE_CASE__    =    (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,   device=__lowerCamelCase					) > 0.5
       ).float()
       SCREAMING_SNAKE_CASE__    =    (torch.rand((self.batch_size, self.num_labels)	,   device=__lowerCamelCase					) > 0.5).long()

       SCREAMING_SNAKE_CASE__    =    self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels






  def       lowercase_		(							self						:		List[Any]					)		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerConfig(
           hidden_size=self.hidden_dim	,   )
       SCREAMING_SNAKE_CASE__    =    self.num_queries
       SCREAMING_SNAKE_CASE__    =    self.num_labels

       SCREAMING_SNAKE_CASE__    =    [1, 1, 1, 1]
       SCREAMING_SNAKE_CASE__    =    self.num_channels

       SCREAMING_SNAKE_CASE__    =    64
       SCREAMING_SNAKE_CASE__    =    128
       SCREAMING_SNAKE_CASE__    =    self.hidden_dim
       SCREAMING_SNAKE_CASE__    =    self.hidden_dim
       SCREAMING_SNAKE_CASE__    =    self.hidden_dim
       return config






  def       lowercase_		(							self						:		Tuple					)		->							List[str]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__    =    {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
       return config, inputs_dict






  def       lowercase_		(							self						:		Optional[int]	,   __lowerCamelCase						:		List[str]	,   __lowerCamelCase						:		Dict					)		->							Optional[int]:
       SCREAMING_SNAKE_CASE__    =    output.encoder_hidden_states
       SCREAMING_SNAKE_CASE__    =    output.pixel_decoder_hidden_states
       SCREAMING_SNAKE_CASE__    =    output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(__lowerCamelCase					)	,   len(config.backbone_config.depths					)					)
       self.parent.assertTrue(len(__lowerCamelCase					)	,   len(config.backbone_config.depths					)					)
       self.parent.assertTrue(len(__lowerCamelCase					)	,   config.decoder_layers					)






  def       lowercase_		(							self						:		str	,   __lowerCamelCase						:		Any	,   __lowerCamelCase						:		Tuple	,   __lowerCamelCase						:		List[str]	,   __lowerCamelCase						:		Dict=False					)		->							str:
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__    =    MaskaFormerModel(config=__lowerCamelCase					)
            model.to(__lowerCamelCase					)
            model.eval()

            SCREAMING_SNAKE_CASE__    =    model(pixel_values=__lowerCamelCase	,   pixel_mask=__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    model(__lowerCamelCase	,   output_hidden_states=__lowerCamelCase					)

       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape	,   (self.batch_size, self.num_queries, self.hidden_dim)	,   )
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None					)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None					)

       if output_hidden_states:
            self.check_output_hidden_state(__lowerCamelCase	,   __lowerCamelCase					)







  def       lowercase_		(							self						:		Union[str, Any]	,   __lowerCamelCase						:		int	,   __lowerCamelCase						:		Dict	,   __lowerCamelCase						:		str	,   __lowerCamelCase						:		List[str]	,   __lowerCamelCase						:		List[str]					)		->							Optional[int]:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerForUniversalSegmentation(config=__lowerCamelCase					)
       model.to(__lowerCamelCase					)
       model.eval()

       def comm_check_on_output(__lowerCamelCase						:		str					):
            # let's still check that all the required stuff is there
            self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None					)
            self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None					)
            self.parent.assertTrue(result.encoder_last_hidden_state is not None					)
            # okay, now we need to check the logits shape
            # due to the encoder compression, masks have a //4 spatial size
            self.parent.assertEqual(
                result.masks_queries_logits.shape	,   (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,   )
            # + 1 for null class
            self.parent.assertEqual(
                result.class_queries_logits.shape	,   (self.batch_size, self.num_queries, self.num_labels + 1)					)

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__    =    model(pixel_values=__lowerCamelCase	,   pixel_mask=__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    model(__lowerCamelCase					)

            comm_check_on_output(__lowerCamelCase					)

            SCREAMING_SNAKE_CASE__    =    model(
                pixel_values=__lowerCamelCase	,   pixel_mask=__lowerCamelCase	,   mask_labels=__lowerCamelCase	,   class_labels=__lowerCamelCase					)

       comm_check_on_output(__lowerCamelCase					)

       self.parent.assertTrue(result.loss is not None					)
       self.parent.assertEqual(result.loss.shape	,   torch.Size([1]					)					)






@require_torch
class        UpperCAmelCase__		(       UpperCamelCase__						,     UpperCamelCase__						,     unittest.TestCase       ):






  """simple docstring"""


  a           =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  a           =					{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

  a           =					False
  a           =					False
  a           =					False
  a           =					False






  def       lowercase_		(							self						:		Tuple					)		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerModelTester(self					)
       SCREAMING_SNAKE_CASE__    =    ConfigTester(self	,   config_class=__lowerCamelCase	,   has_text_modality=__lowerCamelCase					)






  def       lowercase_		(							self						:		Tuple					)		->							Any:
       self.config_tester.run_common_tests()






  def       lowercase_		(							self						:		Optional[int]					)		->							List[str]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase	,   **__lowerCamelCase	,   output_hidden_states=__lowerCamelCase					)






  def       lowercase_		(							self						:		Dict					)		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__lowerCamelCase					)






  @unittest.skip(reason='''Mask2Former does not use inputs_embeds'''					)
  def       lowercase_		(							self						:		int					)		->							Union[str, Any]:
       pass






  @unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''					)
  def       lowercase_		(							self						:		Union[str, Any]					)		->							str:
       pass






  @unittest.skip(reason='''Mask2Former is not a generative model'''					)
  def       lowercase_		(							self						:		Optional[int]					)		->							List[Any]:
       pass






  @unittest.skip(reason='''Mask2Former does not use token embeddings'''					)
  def       lowercase_		(							self						:		Union[str, Any]					)		->							Union[str, Any]:
       pass






  @require_torch_multi_gpu
  @unittest.skip(
      reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
  def       lowercase_		(							self						:		List[Any]					)		->							str:
       pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
  def       lowercase_		(							self						:		str					)		->							int:
       pass






  def       lowercase_		(							self						:		Tuple					)		->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__    =    ['''pixel_values''']
            self.assertListEqual(arg_names[:1]	,   __lowerCamelCase					)






  @slow
  def       lowercase_		(							self						:		Optional[Any]					)		->							Any:
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
            SCREAMING_SNAKE_CASE__    =    MaskaFormerModel.from_pretrained(__lowerCamelCase					)
            self.assertIsNotNone(__lowerCamelCase					)






  def       lowercase_		(							self						:		Optional[int]					)		->							Tuple:
       SCREAMING_SNAKE_CASE__    =    (self.model_tester.min_size,) * 2
       SCREAMING_SNAKE_CASE__    =    {
           '''pixel_values''': torch.randn((2, 3, *size)	,   device=__lowerCamelCase					),
           '''mask_labels''': torch.randn((2, 10, *size)	,   device=__lowerCamelCase					),
           '''class_labels''': torch.zeros(2	,   10	,   device=__lowerCamelCase					).long(),
       }
       SCREAMING_SNAKE_CASE__    =    self.model_tester.get_config()

       SCREAMING_SNAKE_CASE__    =    MaskaFormerForUniversalSegmentation(__lowerCamelCase					).to(__lowerCamelCase					)
       SCREAMING_SNAKE_CASE__    =    model(**__lowerCamelCase					)
       self.assertTrue(outputs.loss is not None					)






  def       lowercase_		(							self						:		Any					)		->							Any:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(__lowerCamelCase	,   **__lowerCamelCase	,   output_hidden_states=__lowerCamelCase					)






  def       lowercase_		(							self						:		Union[str, Any]					)		->							Dict:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__    =    model_class(__lowerCamelCase					).to(__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    model(**__lowerCamelCase	,   output_attentions=__lowerCamelCase					)
            self.assertTrue(outputs.attentions is not None					)






  def       lowercase_		(							self						:		Optional[int]					)		->							Any:
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__    =    self.all_model_classes[1]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE__    =    model_class(__lowerCamelCase					)
       model.to(__lowerCamelCase					)
       model.train()

       SCREAMING_SNAKE_CASE__    =    model(__lowerCamelCase	,   mask_labels=__lowerCamelCase	,   class_labels=__lowerCamelCase					).loss
       loss.backward()







  def       lowercase_		(							self						:		Optional[Any]					)		->							str:
       SCREAMING_SNAKE_CASE__    =    self.all_model_classes[1]
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__    =    self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__    =    True
       SCREAMING_SNAKE_CASE__    =    True

       SCREAMING_SNAKE_CASE__    =    model_class(__lowerCamelCase					).to(__lowerCamelCase					)
       model.train()

       SCREAMING_SNAKE_CASE__    =    model(__lowerCamelCase	,   mask_labels=__lowerCamelCase	,   class_labels=__lowerCamelCase					)

       SCREAMING_SNAKE_CASE__    =    outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE__    =    outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE__    =    outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE__    =    outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=__lowerCamelCase					)

       self.assertIsNotNone(encoder_hidden_states.grad					)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad					)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad					)
       self.assertIsNotNone(attentions.grad					)



_SCREAMING_SNAKE_CASE     :  str   			=			1e-4
def 				UpperCAmelCase_   (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__    =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image







@require_vision
@slow
class        UpperCAmelCase__		(       unittest.TestCase       ):






  """simple docstring"""


  @cached_property
  def       lowercase_		(							self						:		Optional[int]					)		->							Dict:
       return "facebook/mask2former-swin-small-coco-instance"






  @cached_property
  def       lowercase_		(							self						:		Union[str, Any]					)		->							Dict:
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints					) if is_vision_available() else None






  def       lowercase_		(							self						:		Union[str, Any]					)		->							int:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerModel.from_pretrained(self.model_checkpoints					).to(__lowerCamelCase					)
       SCREAMING_SNAKE_CASE__    =    self.default_image_processor
       SCREAMING_SNAKE_CASE__    =    prepare_img()
       SCREAMING_SNAKE_CASE__    =    image_processor(__lowerCamelCase	,   return_tensors='''pt'''					).to(__lowerCamelCase					)
       SCREAMING_SNAKE_CASE__    =    inputs['''pixel_values'''].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
       # check size
       self.assertEqual(__lowerCamelCase	,   (1, 3, 384, 384)					)

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__    =    model(**__lowerCamelCase					)

       SCREAMING_SNAKE_CASE__    =    torch.tensor(
           [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]					).to(__lowerCamelCase					)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]	,   __lowerCamelCase	,   atol=__lowerCamelCase					)					)

       SCREAMING_SNAKE_CASE__    =    torch.tensor(
           [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]					).to(__lowerCamelCase					)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,   __lowerCamelCase	,   atol=__lowerCamelCase					)					)

       SCREAMING_SNAKE_CASE__    =    torch.tensor(
           [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]					).to(__lowerCamelCase					)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,   __lowerCamelCase	,   atol=__lowerCamelCase					)					)






  def       lowercase_		(							self						:		int					)		->							str:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(__lowerCamelCase					).eval()
       SCREAMING_SNAKE_CASE__    =    self.default_image_processor
       SCREAMING_SNAKE_CASE__    =    prepare_img()
       SCREAMING_SNAKE_CASE__    =    image_processor(__lowerCamelCase	,   return_tensors='''pt'''					).to(__lowerCamelCase					)
       SCREAMING_SNAKE_CASE__    =    inputs['''pixel_values'''].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0					)
       # check size
       self.assertEqual(__lowerCamelCase	,   (1, 3, 384, 384)					)

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__    =    model(**__lowerCamelCase					)
       # masks_queries_logits
       SCREAMING_SNAKE_CASE__    =    outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape	,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)					)
       SCREAMING_SNAKE_CASE__    =    [
           [-8.7839, -9.0056, -8.8121],
           [-7.4104, -7.0313, -6.5401],
           [-6.6105, -6.3427, -6.4675],
       ]
       SCREAMING_SNAKE_CASE__    =    torch.tensor(__lowerCamelCase					).to(__lowerCamelCase					)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,   __lowerCamelCase	,   atol=__lowerCamelCase					)					)
       # class_queries_logits
       SCREAMING_SNAKE_CASE__    =    outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape	,   (1, model.config.num_queries, model.config.num_labels + 1)					)
       SCREAMING_SNAKE_CASE__    =    torch.tensor(
           [
               [1.8324, -8.0835, -4.1922],
               [0.8450, -9.0050, -3.6053],
               [0.3045, -7.7293, -3.0275],
           ]					).to(__lowerCamelCase					)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,   __lowerCamelCase	,   atol=__lowerCamelCase					)					)







  def       lowercase_		(							self						:		Dict					)		->							str:
       SCREAMING_SNAKE_CASE__    =    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints					).to(__lowerCamelCase					).eval()
       SCREAMING_SNAKE_CASE__    =    self.default_image_processor

       SCREAMING_SNAKE_CASE__    =    image_processor(
           [np.zeros((3, 800, 1333)					), np.zeros((3, 800, 1333)					)]	,   segmentation_maps=[np.zeros((384, 384)					).astype(np.floataa					), np.zeros((384, 384)					).astype(np.floataa					)]	,   return_tensors='''pt'''	,   )

       SCREAMING_SNAKE_CASE__    =    inputs['''pixel_values'''].to(__lowerCamelCase					)
       SCREAMING_SNAKE_CASE__    =    [el.to(__lowerCamelCase					) for el in inputs['''mask_labels''']]
       SCREAMING_SNAKE_CASE__    =    [el.to(__lowerCamelCase					) for el in inputs['''class_labels''']]

       with torch.no_grad():
            SCREAMING_SNAKE_CASE__    =    model(**__lowerCamelCase					)

       self.assertTrue(outputs.loss is not None					)









import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''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 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						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 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						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)
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						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 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

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

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

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

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

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

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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

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

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



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



 def 				UpperCamelCase			(							self ):
        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,)
                                    A__											=						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 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

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




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

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

        assert sample.dtype == torch.floataa





