

"""simple docstring"""






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     LayoutLMvaForQuestionAnswering,
     LayoutLMvaForSequenceClassification,
     LayoutLMvaForTokenClassification,
     LayoutLMvaModel,
 )
 from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor







class 	__UpperCamelCase    :

  def __init__(		self    ,_A    ,_A=2    ,_A=3    ,_A=4    ,_A=2    ,_A=7    ,_A=True    ,_A=True    ,_A=True    ,_A=True    ,_A=99    ,_A=36    ,_A=3    ,_A=4    ,_A=37    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=512    ,_A=16    ,_A=2    ,_A=0.0_2    ,_A=6    ,_A=6    ,_A=3    ,_A=4    ,_A=None    ,_A=1000    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    parent
      _lowerCAmelCase       :   Tuple  =    batch_size
      _lowerCAmelCase       :   Tuple  =    num_channels
      _lowerCAmelCase       :   Any  =    image_size
      _lowerCAmelCase       :   Optional[Any]  =    patch_size
      _lowerCAmelCase       :   str  =    text_seq_length
      _lowerCAmelCase       :   Optional[Any]  =    is_training
      _lowerCAmelCase       :   List[Any]  =    use_input_mask
      _lowerCAmelCase       :   List[Any]  =    use_token_type_ids
      _lowerCAmelCase       :   Tuple  =    use_labels
      _lowerCAmelCase       :   int  =    vocab_size
      _lowerCAmelCase       :   Dict  =    hidden_size
      _lowerCAmelCase       :   Any  =    num_hidden_layers
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    intermediate_size
      _lowerCAmelCase       :   str  =    hidden_act
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_dropout_prob
      _lowerCAmelCase       :   Union[str, Any]  =    attention_probs_dropout_prob
      _lowerCAmelCase       :   Optional[Any]  =    max_position_embeddings
      _lowerCAmelCase       :   str  =    type_vocab_size
      _lowerCAmelCase       :   Tuple  =    type_sequence_label_size
      _lowerCAmelCase       :   int  =    initializer_range
      _lowerCAmelCase       :   Optional[int]  =    coordinate_size
      _lowerCAmelCase       :   Optional[Any]  =    shape_size
      _lowerCAmelCase       :   Union[str, Any]  =    num_labels
      _lowerCAmelCase       :   str  =    num_choices
      _lowerCAmelCase       :   Optional[Any]  =    scope
      _lowerCAmelCase       :   str  =    range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      _lowerCAmelCase       :   Dict  =    text_seq_length
      _lowerCAmelCase       :   Optional[int]  =    (image_size // patch_size) ** 2 + 1
      _lowerCAmelCase       :   int  =    self.text_seq_length + self.image_seq_length

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.vocab_size      )

      _lowerCAmelCase       :   List[str]  =    ids_tensor([self.batch_size, self.text_seq_length, 4]    ,self.range_bbox      )
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]      ):
          for j in range(bbox.shape[1]      ):
              if bbox[i, j, 3] < bbox[i, j, 1]:
                  _lowerCAmelCase       :   Union[str, Any]  =    bbox[i, j, 3]
                  _lowerCAmelCase       :   Any  =    bbox[i, j, 1]
                  _lowerCAmelCase       :   int  =    t
              if bbox[i, j, 2] < bbox[i, j, 0]:
                  _lowerCAmelCase       :   Tuple  =    bbox[i, j, 2]
                  _lowerCAmelCase       :   Any  =    bbox[i, j, 0]
                  _lowerCAmelCase       :   Any  =    t

      _lowerCAmelCase       :   Optional[Any]  =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

      _lowerCAmelCase       :   Dict  =    None
      if self.use_input_mask:
          _lowerCAmelCase       :   Dict  =    random_attention_mask([self.batch_size, self.text_seq_length]      )

      _lowerCAmelCase       :   str  =    None
      if self.use_token_type_ids:
          _lowerCAmelCase       :   Union[str, Any]  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.type_vocab_size      )

      _lowerCAmelCase       :   Optional[Any]  =    None
      _lowerCAmelCase       :   Union[str, Any]  =    None
      if self.use_labels:
          _lowerCAmelCase       :   int  =    ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
          _lowerCAmelCase       :   Dict  =    ids_tensor([self.batch_size, self.text_seq_length]    ,self.num_labels      )

      _lowerCAmelCase       :   Any  =    LayoutLMvaConfig(
          vocab_size=self.vocab_size    ,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    ,max_position_embeddings=self.max_position_embeddings    ,type_vocab_size=self.type_vocab_size    ,initializer_range=self.initializer_range    ,coordinate_size=self.coordinate_size    ,shape_size=self.shape_size    ,input_size=self.image_size    ,patch_size=self.patch_size    ,)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    LayoutLMvaModel(config=_A      )
      model.to(_A      )
      model.eval()

      # text + image
      _lowerCAmelCase       :   str  =    model(_A    ,pixel_values=_A      )
      _lowerCAmelCase       :   Tuple  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,bbox=_A    ,pixel_values=_A    ,token_type_ids=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    model(_A    ,bbox=_A    ,pixel_values=_A      )

      self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)      )

      # text only
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A      )
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,(self.batch_size, self.text_seq_length, self.hidden_size)      )

      # image only
      _lowerCAmelCase       :   str  =    model(pixel_values=_A      )
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,(self.batch_size, self.image_seq_length, self.hidden_size)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.num_labels
      _lowerCAmelCase       :   Dict  =    LayoutLMvaForSequenceClassification(_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A    ,)
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.num_labels
      _lowerCAmelCase       :   int  =    LayoutLMvaForTokenClassification(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A    ,)
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.text_seq_length, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    LayoutLMvaForQuestionAnswering(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(
          _A    ,bbox=_A    ,pixel_values=_A    ,attention_mask=_A    ,token_type_ids=_A    ,start_positions=_A    ,end_positions=_A    ,)
      self.parent.assertEqual(result.start_logits.shape    ,(self.batch_size, self.seq_length)      )
      self.parent.assertEqual(result.end_logits.shape    ,(self.batch_size, self.seq_length)      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.prepare_config_and_inputs()
      (
          (
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					
      )       :   Optional[Any]  =    config_and_inputs
      _lowerCAmelCase       :   List[str]  =    {
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict







@require_torch
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  _UpperCAmelCase          =		(
      (
          LayoutLMvaModel,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  _UpperCAmelCase          =		(
      {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
      if is_torch_available()
      else {}
  )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      return True

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    LayoutLMvaModelTester(self      )
      _lowerCAmelCase       :   Any  =    ConfigTester(self    ,config_class=_A    ,hidden_size=37      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A=False      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    copy.deepcopy(_A      )
      if model_class in get_values(_A      ):
          _lowerCAmelCase       :   int  =    {
              k: v.unsqueeze(1      ).expand(-1    ,self.model_tester.num_choices    ,-1      ).contiguous()
              if isinstance(_A    ,torch.Tensor      ) and v.ndim > 1
              else v
              for k, v in inputs_dict.items()
          }
      if return_labels:
          if model_class in get_values(_A      ):
              _lowerCAmelCase       :   int  =    torch.ones(self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in get_values(_A      ):
              _lowerCAmelCase       :   List[Any]  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
              _lowerCAmelCase       :   Dict  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in [
              *get_values(_A      ),
          ]:
              _lowerCAmelCase       :   Optional[Any]  =    torch.zeros(
                  self.model_tester.batch_size    ,dtype=torch.long    ,device=_A      )
          elif model_class in [
              *get_values(_A      ),
          ]:
              _lowerCAmelCase       :   Dict  =    torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,dtype=torch.long    ,device=_A    ,)

      return inputs_dict

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.config_tester.run_common_tests()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          _lowerCAmelCase       :   Dict  =    type
          self.model_tester.create_and_check_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_A      )







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   List[str]  =    LayoutLMvaModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
    return image







@require_torch
class 	__UpperCamelCase    (    unittest.TestCase						):

  @cached_property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return LayoutLMvaImageProcessor(apply_ocr=_A      ) if is_vision_available() else None







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      ).to(_A      )

      _lowerCAmelCase       :   str  =    self.default_image_processor
      _lowerCAmelCase       :   Tuple  =    prepare_img()
      _lowerCAmelCase       :   Optional[Any]  =    image_processor(images=_A    ,return_tensors='pt'      ).pixel_values.to(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor([[1, 2]]      )
      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]      ).unsqueeze(0      )

      # forward pass
      _lowerCAmelCase       :   Union[str, Any]  =    model(
          input_ids=input_ids.to(_A      )    ,bbox=bbox.to(_A      )    ,pixel_values=pixel_values.to(_A      )    ,)

      # verify the logits
      _lowerCAmelCase       :   Tuple  =    torch.Size((1, 199, 768)      )
      self.assertEqual(outputs.last_hidden_state.shape    ,_A      )

      _lowerCAmelCase       :   str  =    torch.tensor(
          [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]      ).to(_A      )

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








"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Optional[int]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )
          # copy over dummy past residuals
          _lowerCAmelCase       :   Tuple  =    dummy_past_residuals[: scheduler.config.solver_order]

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

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

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

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   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)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

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

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Any  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    dict(self.forward_default_kwargs      )

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

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

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    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'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _lowerCAmelCase       :   Optional[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          _lowerCAmelCase       :   Optional[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

          _lowerCAmelCase       :   Any  =    scheduler.timesteps[5]
          _lowerCAmelCase       :   List[str]  =    scheduler.timesteps[6]

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=_A    ,prediction_type=_A    ,sample_max_value=_A    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
          self.check_over_forward(num_inference_steps=_A    ,time_step=0      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.1_0_1_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

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

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
          assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





