code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
     import tensorflow as tf
  
     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      def __init__(							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    )			->       Optional[int]:
             __UpperCAmelCase    							=parent
             __UpperCAmelCase    							=13
             __UpperCAmelCase    							=7
             __UpperCAmelCase    							=True
             __UpperCAmelCase    							=True
             __UpperCAmelCase    							=True
             __UpperCAmelCase    							=True
             __UpperCAmelCase    							=True
             __UpperCAmelCase    							=99
             __UpperCAmelCase    							=32
             __UpperCAmelCase    							=2
             __UpperCAmelCase    							=4
             __UpperCAmelCase    							=37
             __UpperCAmelCase    							="""gelu"""
             __UpperCAmelCase    							=0.1
             __UpperCAmelCase    							=0.1
             __UpperCAmelCase    							=512
             __UpperCAmelCase    							=16
             __UpperCAmelCase    							=2
             __UpperCAmelCase    							=0.02
             __UpperCAmelCase    							=3
             __UpperCAmelCase    							=4
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=self.vocab_size - 1
  
  
  
      def _a       (							self			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)
  
             __UpperCAmelCase    							=None
             if self.use_input_mask:
                    __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)
  
             __UpperCAmelCase    							=None
             if self.use_token_type_ids:
                    __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.type_vocab_size			)
  
             __UpperCAmelCase    							=None
             if self.use_mc_token_ids:
                    __UpperCAmelCase    							=ids_tensor([self.batch_size, self.num_choices]   ,    self.seq_length			)
  
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             if self.use_labels:
                    __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.type_sequence_label_size			)
                    __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels			)
                    __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.num_choices			)
  
             __UpperCAmelCase    							=CTRLConfig(
                 vocab_size=self.vocab_size   ,    n_embd=self.hidden_size   ,    n_layer=self.num_hidden_layers   ,    n_head=self.num_attention_heads   ,    n_positions=self.max_position_embeddings   ,    pad_token_id=self.pad_token_id   ,    )
  
             __UpperCAmelCase    							=ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,    2			)
  
             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
                 token_type_ids,
                 mc_token_ids,
                 sequence_labels,
                 token_labels,
                 choice_labels,
             )
  
  
  
      def _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    *__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Dict:
             __UpperCAmelCase    							=TFCTRLModel(config=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=[input_ids, None, input_mask]  # None is the input for 'past'
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)
  
             self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
      def _a       (							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    *__SCREAMING_SNAKE_CASE			:    List[Any]			)			->       List[str]:
             __UpperCAmelCase    							=TFCTRLLMHeadModel(config=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
      def _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    *__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Tuple:
             __UpperCAmelCase    							=self.num_labels
             __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.type_sequence_label_size			)
             __UpperCAmelCase    							={
                 """input_ids""": input_ids,
                 """token_type_ids""": token_type_ids,
                 """labels""": sequence_labels,
             }
             __UpperCAmelCase    							=TFCTRLForSequenceClassification(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)
  
  
  
      def _a       (							self			:    str			)			->       Tuple:
             __UpperCAmelCase    							=self.prepare_config_and_inputs()
  
             (
                 (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   
             )    							=config_and_inputs
  
             __UpperCAmelCase    							={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
             return config, inputs_dict
  
  
  
  
  @require_tf
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[int]   =   (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
      lowerCamelCase  :       Union[str, Any]   =   (TFCTRLLMHeadModel,) if is_tf_available() else ()
      lowerCamelCase  :       Union[str, Any]   =   (
          {
              'feature-extraction': TFCTRLModel,
              'text-classification': TFCTRLForSequenceClassification,
              'text-generation': TFCTRLLMHeadModel,
              'zero-shot': TFCTRLForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase  :       Optional[int]   =   False
      lowerCamelCase  :       Union[str, Any]   =   False
  
  
  
      def _a       (							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       int:
             if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
                    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
                    # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
                    # config could not be created.
                    return True
  
             return False
  
  
  
      def _a       (							self			:    Optional[int]			)			->       Union[str, Any]:
             __UpperCAmelCase    							=TFCTRLModelTester(self			)
             __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    n_embd=37			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       Any:
             self.config_tester.run_common_tests()
  
  
  
      def _a       (							self			:    str			)			->       int:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       List[str]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_lm_head(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       Dict:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_ctrl_for_sequence_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    int			)			->       List[Any]:
             __UpperCAmelCase							,   __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
             __UpperCAmelCase    							=[TFCTRLLMHeadModel]
             __UpperCAmelCase    							=[TFCTRLForSequenceClassification]
  
             for model_class in self.all_model_classes:
                    __UpperCAmelCase    							=model_class(__SCREAMING_SNAKE_CASE			)
                    model.build()  # may be needed for the get_bias() call below
                    assert isinstance(model.get_input_embeddings()   ,    tf.keras.layers.Layer			)
  
                    if model_class in list_lm_models:
                           __UpperCAmelCase    							=model.get_output_embeddings()
                           assert isinstance(__SCREAMING_SNAKE_CASE   ,    tf.keras.layers.Layer			)
                           __UpperCAmelCase    							=model.get_bias()
                           assert isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                           for k, v in name.items():
                                  assert isinstance(__SCREAMING_SNAKE_CASE   ,    tf.Variable			)
                    elif model_class in list_other_models_with_output_ebd:
                           __UpperCAmelCase    							=model.get_output_embeddings()
                           assert isinstance(__SCREAMING_SNAKE_CASE   ,    tf.keras.layers.Layer			)
                           __UpperCAmelCase    							=model.get_bias()
                           assert name is None
                    else:
                           __UpperCAmelCase    							=model.get_output_embeddings()
                           assert x is None
                           __UpperCAmelCase    							=model.get_bias()
                           assert name is None
  
  
  
      @slow
      def _a       (							self			:    List[str]			)			->       Optional[int]:
             for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __UpperCAmelCase    							=TFCTRLModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
                    self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
  
  
  
  
  @require_tf
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      @slow
      def _a       (							self			:    str			)			->       Any:
             __UpperCAmelCase    							=TFCTRLLMHeadModel.from_pretrained("""ctrl"""			)
             __UpperCAmelCase    							=tf.convert_to_tensor([[11859, 0, 1611, 8]]   ,    dtype=tf.intaa			)  # Legal the president is
             __UpperCAmelCase    							=[
                 11859,
                 0,
                 1611,
                 8,
                 5,
                 150,
                 26449,
                 2,
                 19,
                 348,
                 469,
                 3,
                 2595,
                 48,
                 20740,
                 246533,
                 246533,
                 19,
                 30,
                 5,
             ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
             __UpperCAmelCase    							=model.generate(__SCREAMING_SNAKE_CASE   ,    do_sample=__SCREAMING_SNAKE_CASE			)
             self.assertListEqual(output_ids[0].numpy().tolist()   ,    __SCREAMING_SNAKE_CASE			)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       str   =   (DPMSolverMultistepScheduler,)
      lowerCamelCase  :       List[str]   =   (('num_inference_steps', 25),)
  
  
  
      def _a       (							self			:    Dict   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
             __UpperCAmelCase    							={
                 """num_train_timesteps""": 1000,
                 """beta_start""": 0.0_001,
                 """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""",
                 """lower_order_final""": False,
                 """lambda_min_clipped""": -float("""inf"""			),
                 """variance_type""": None,
             }
  
             config.update(**__SCREAMING_SNAKE_CASE			)
             return config
  
  
  
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       Dict:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.dummy_sample
             __UpperCAmelCase    							=0.1 * sample
             __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                    # copy over dummy past residuals
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE			)
                           new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                           # copy over dummy past residuals
                           __UpperCAmelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]
  
                    __UpperCAmelCase							,   __UpperCAmelCase    							=sample, sample
                    for t in range(__SCREAMING_SNAKE_CASE   ,    time_step + scheduler.config.solver_order + 1			):
                           __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                           __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
  
  
  
      def _a       (							self			:    Any			)			->       Union[str, Any]:
             pass
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0   ,    **__SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[Any]:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.dummy_sample
             __UpperCAmelCase    							=0.1 * sample
             __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config()
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
                    # copy over dummy past residuals (must be after setting timesteps)
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE			)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
                           # copy over dummy past residual (must be after setting timesteps)
                           __UpperCAmelCase    							=dummy_past_residuals[: new_scheduler.config.solver_order]
  
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                    __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                    assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"
  
  
  
      def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    **__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
             if scheduler is None:
                    __UpperCAmelCase    							=self.scheduler_classes[0]
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=10
             __UpperCAmelCase    							=self.dummy_model()
             __UpperCAmelCase    							=self.dummy_sample_deter
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
             for i, t in enumerate(scheduler.timesteps			):
                    __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).prev_sample
  
             return sample
  
  
  
      def _a       (							self			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
  
             __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
  
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config()
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
                    __UpperCAmelCase    							=self.dummy_sample
                    __UpperCAmelCase    							=0.1 * sample
  
                    if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
                    elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE   ,    """set_timesteps"""			):
                           __UpperCAmelCase    							=num_inference_steps
  
                    # copy over dummy past residuals (must be done after set_timesteps)
                    __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
                    __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]
  
                    __UpperCAmelCase    							=scheduler.timesteps[5]
                    __UpperCAmelCase    							=scheduler.timesteps[6]
  
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                    self.assertEqual(output_a.shape   ,    sample.shape			)
                    self.assertEqual(output_a.shape   ,    output_a.shape			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       Tuple:
             for timesteps in [25, 50, 100, 999, 1000]:
                    self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       int:
             self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE			)
             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=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    sample_max_value=__SCREAMING_SNAKE_CASE   ,    algorithm_type="""dpmsolver++"""   ,    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    )
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       List[str]:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Tuple:
             for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                    for solver_type in ["midpoint", "heun"]:
                           for order in [1, 2, 3]:
                                  for prediction_type in ["epsilon", "sample"]:
                                         if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                                if order == 3:
                                                       continue
                                         else:
                                                self.check_over_configs(
                                                    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                         __UpperCAmelCase    							=self.full_loop(
                                             solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                         assert not torch.isnan(__SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"
  
  
  
      def _a       (							self			:    Dict			)			->       str:
             self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
             self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
             self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
             self.check_over_configs(lambda_min_clipped=-5.1			)
  
  
  
      def _a       (							self			:    Any			)			->       List[str]:
             self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE			)
             self.check_over_configs(variance_type="""learned_range"""			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       int:
             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                    self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE   ,    time_step=0			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       str:
             __UpperCAmelCase    							=self.full_loop()
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
  
  
      def _a       (							self			:    Union[str, Any]			)			->       str:
             __UpperCAmelCase    							=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0.87   ,    sample_max_value=0.5			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 1.1_364			) < 1e-3
  
  
  
      def _a       (							self			:    List[Any]			)			->       List[Any]:
             __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.2_251			) < 1e-3
  
  
  
      def _a       (							self			:    int			)			->       Union[str, Any]:
             __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""   ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.2_096			) < 1e-3
  
  
  
      def _a       (							self			:    int			)			->       Optional[Any]:
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             __UpperCAmelCase    							=DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
             __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
             __UpperCAmelCase    							=DPMSolverSinglestepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=UniPCMultistepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=DEISMultistepScheduler.from_config(scheduler.config			)
             __UpperCAmelCase    							=DPMSolverMultistepScheduler.from_config(scheduler.config			)
  
             __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)
  
             assert abs(result_mean.item() - 0.3_301			) < 1e-3
  
  
  
      def _a       (							self			:    List[Any]			)			->       str:
             __UpperCAmelCase    							=self.scheduler_classes[0]
             __UpperCAmelCase    							=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0			)
             __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=10
             __UpperCAmelCase    							=self.dummy_model()
             __UpperCAmelCase    							=self.dummy_sample_deter.half()
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)
  
             for i, t in enumerate(scheduler.timesteps			):
                    __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			).prev_sample
  
             assert sample.dtype == torch.floataa
  
  
  
      def _a       (							self			:    Optional[int]   ,    **__SCREAMING_SNAKE_CASE			:    Tuple			)			->       Optional[int]:
             for scheduler_class in self.scheduler_classes:
                    __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
  
                    scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
                    assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps
  
  
  
  
  