code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
        import tensorflow as tf
  
        from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  @require_tf
  class 							_A :
      """simple docstring"""
  
      lowerCamelCase  :       List[Any]   =   MarianConfig
      lowerCamelCase  :       Dict   =   {}
      lowerCamelCase  :       Optional[int]   =   "gelu"
  
  
  
      def __init__(							self			:    int   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=13   ,    __SCREAMING_SNAKE_CASE			:    List[str]=7   ,    __SCREAMING_SNAKE_CASE			:    int=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=False   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=99   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]=32   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=2   ,    __SCREAMING_SNAKE_CASE			:    str=4   ,    __SCREAMING_SNAKE_CASE			:    Dict=37   ,    __SCREAMING_SNAKE_CASE			:    int=0.1   ,    __SCREAMING_SNAKE_CASE			:    str=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=20   ,    __SCREAMING_SNAKE_CASE			:    str=2   ,    __SCREAMING_SNAKE_CASE			:    Any=1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0   ,    )			->       Optional[Any]:
             __UpperCAmelCase    							=parent
             __UpperCAmelCase    							=batch_size
             __UpperCAmelCase    							=seq_length
             __UpperCAmelCase    							=is_training
             __UpperCAmelCase    							=use_labels
             __UpperCAmelCase    							=vocab_size
             __UpperCAmelCase    							=hidden_size
             __UpperCAmelCase    							=num_hidden_layers
             __UpperCAmelCase    							=num_attention_heads
             __UpperCAmelCase    							=intermediate_size
  
             __UpperCAmelCase    							=hidden_dropout_prob
             __UpperCAmelCase    							=attention_probs_dropout_prob
             __UpperCAmelCase    							=max_position_embeddings
             __UpperCAmelCase    							=eos_token_id
             __UpperCAmelCase    							=pad_token_id
             __UpperCAmelCase    							=bos_token_id
  
  
  
      def _a       (							self			:    Any			)			->       str:
             __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length - 1]   ,    self.vocab_size			)
             __UpperCAmelCase    							=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)   ,    1			)
             __UpperCAmelCase    							=tf.concat([input_ids, eos_tensor]   ,    axis=1			)
  
             __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)
  
             __UpperCAmelCase    							=self.config_cls(
                 vocab_size=self.vocab_size   ,    d_model=self.hidden_size   ,    encoder_layers=self.num_hidden_layers   ,    decoder_layers=self.num_hidden_layers   ,    encoder_attention_heads=self.num_attention_heads   ,    decoder_attention_heads=self.num_attention_heads   ,    encoder_ffn_dim=self.intermediate_size   ,    decoder_ffn_dim=self.intermediate_size   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    eos_token_ids=[2]   ,    bos_token_id=self.bos_token_id   ,    pad_token_id=self.pad_token_id   ,    decoder_start_token_id=self.pad_token_id   ,    **self.config_updates   ,    )
             __UpperCAmelCase    							=prepare_marian_inputs_dict(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_			)
             return config, inputs_dict
  
  
  
      def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Optional[int]:
             __UpperCAmelCase    							=TFMarianModel(config=UpperCAmelCase_			).get_decoder()
             __UpperCAmelCase    							=inputs_dict['input_ids']
  
             __UpperCAmelCase    							=input_ids[:1, :]
             __UpperCAmelCase    							=inputs_dict['attention_mask'][:1, :]
             __UpperCAmelCase    							=inputs_dict['head_mask']
             __UpperCAmelCase    							=1
  
             # first forward pass
             __UpperCAmelCase    							=model(UpperCAmelCase_   ,    attention_mask=UpperCAmelCase_   ,    head_mask=UpperCAmelCase_   ,    use_cache=UpperCAmelCase_			)
  
             __UpperCAmelCase    							=outputs.to_tuple()
  
             # create hypothetical next token and extent to next_input_ids
             __UpperCAmelCase    							=ids_tensor((self.batch_size, 3)   ,    config.vocab_size			)
             __UpperCAmelCase    							=tf.cast(ids_tensor((self.batch_size, 3)   ,    2			)   ,    tf.inta			)
  
             # append to next input_ids and
             __UpperCAmelCase    							=tf.concat([input_ids, next_tokens]   ,    axis=-1			)
             __UpperCAmelCase    							=tf.concat([attention_mask, next_attn_mask]   ,    axis=-1			)
  
             __UpperCAmelCase    							=model(UpperCAmelCase_   ,    attention_mask=UpperCAmelCase_			)[0]
             __UpperCAmelCase    							=model(UpperCAmelCase_   ,    attention_mask=UpperCAmelCase_   ,    past_key_values=UpperCAmelCase_			)[0]
  
             self.parent.assertEqual(next_tokens.shape[1]   ,    output_from_past.shape[1]			)
  
             # select random slice
             __UpperCAmelCase    							=int(ids_tensor((1,)   ,    output_from_past.shape[-1]			)			)
             __UpperCAmelCase    							=output_from_no_past[:, -3:, random_slice_idx]
             __UpperCAmelCase    							=output_from_past[:, :, random_slice_idx]
  
             # test that outputs are equal for slice
             tf.debugging.assert_near(UpperCAmelCase_   ,    UpperCAmelCase_   ,    rtol=1e-3			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Union[str, Any]     ,			A_:	Union[str, Any]     ,			A_:	Tuple     ,			A_:	Tuple=None     ,			A_:	Optional[int]=None     ,			A_:	Union[str, Any]=None     ,			A_:	List[str]=None     ,			A_:	Optional[int]=None     ,			)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if attention_mask is None:
                __UpperCAmelCase    							=tf.cast(tf.math.not_equal(_UpperCAmelCase     ,			config.pad_token_id		)     ,			tf.inta		)
         if decoder_attention_mask is None:
                __UpperCAmelCase    							=tf.concat(
                    [
                        tf.ones(decoder_input_ids[:, :1].shape     ,			dtype=tf.inta		),
                        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,			config.pad_token_id		)     ,			tf.inta		),
                    ]     ,			axis=-1     ,			)
         if head_mask is None:
                __UpperCAmelCase    							=tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
         if decoder_head_mask is None:
                __UpperCAmelCase    							=tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
         if cross_attn_head_mask is None:
                __UpperCAmelCase    							=tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
         return {
             "input_ids": input_ids,
             "decoder_input_ids": decoder_input_ids,
             "attention_mask": attention_mask,
             "decoder_attention_mask": decoder_attention_mask,
             "head_mask": head_mask,
             "decoder_head_mask": decoder_head_mask,
             "cross_attn_head_mask": cross_attn_head_mask,
         }
  
  
  
  @require_tf
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Any   =   (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      lowerCamelCase  :       Optional[Any]   =   (TFMarianMTModel,) if is_tf_available() else ()
      lowerCamelCase  :       Union[str, Any]   =   (
          {
              "conversational": TFMarianMTModel,
              "feature-extraction": TFMarianModel,
              "summarization": TFMarianMTModel,
              "text2text-generation": TFMarianMTModel,
              "translation": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      lowerCamelCase  :       int   =   True
      lowerCamelCase  :       Union[str, Any]   =   False
      lowerCamelCase  :       List[Any]   =   False
  
  
  
      def _a       (							self			:    str			)			->       Dict:
             __UpperCAmelCase    							=TFMarianModelTester(self			)
             __UpperCAmelCase    							=ConfigTester(self   ,    config_class=UpperCAmelCase_			)
  
  
  
      def _a       (							self			:    int			)			->       Tuple:
             self.config_tester.run_common_tests()
  
  
  
      def _a       (							self			:    List[str]			)			->       Optional[int]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_			)
  
  
  
  
  @require_tf
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[int]   =   1_000  # show more chars for failing integration tests
  
  
  
      @classmethod
      def _a       (							cls			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
             return cls
  
  
  
      @cached_property
      def _a       (							self			:    List[str]			)			->       Tuple:
             return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
      @property
      def _a       (							self			:    int			)			->       Union[str, Any]:
             return self.tokenizer.eos_token_id
  
  
  
      @cached_property
      def _a       (							self			:    Optional[int]			)			->       Tuple:
             warnings.simplefilter("""error"""			)
             __UpperCAmelCase    							=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
             assert isinstance(UpperCAmelCase_   ,    UpperCAmelCase_			)
             __UpperCAmelCase    							=model.config
             self.assertListEqual(c.bad_words_ids   ,    [[c.pad_token_id]]			)
             self.assertEqual(c.max_length   ,    512			)
             self.assertEqual(c.decoder_start_token_id   ,    c.pad_token_id			)
             return model
  
  
  
      def _a       (							self			:    int   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
             __UpperCAmelCase    							=self.translate_src_text(**UpperCAmelCase_			)
             self.assertListEqual(self.expected_text   ,    UpperCAmelCase_			)
  
  
  
      def _a       (							self			:    List[str]   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Union[str, Any]:
             __UpperCAmelCase    							=self.tokenizer(self.src_text   ,    **UpperCAmelCase_   ,    padding=UpperCAmelCase_   ,    return_tensors="""tf"""			)
             __UpperCAmelCase    							=self.model.generate(
                 model_inputs.input_ids   ,    attention_mask=model_inputs.attention_mask   ,    num_beams=2   ,    max_length=128			)
             __UpperCAmelCase    							=self.tokenizer.batch_decode(generated_ids.numpy()   ,    skip_special_tokens=UpperCAmelCase_			)
             return generated_words
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[Any]   =   "mt"
      lowerCamelCase  :       Dict   =   "en"
      lowerCamelCase  :       Union[str, Any]   =   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
      lowerCamelCase  :       Dict   =   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Tuple			)			->       Optional[int]:
             self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Optional[Any]   =   "en"
      lowerCamelCase  :       int   =   "zh"
      lowerCamelCase  :       int   =   ["My name is Wolfgang and I live in Berlin"]
      lowerCamelCase  :       str   =   ["我叫沃尔夫冈 我住在柏林"]
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    List[Any]			)			->       List[Any]:
             self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       str   =   "en"
      lowerCamelCase  :       Tuple   =   "ROMANCE"
      lowerCamelCase  :       Tuple   =   [
          ">>fr<< Don't spend so much time watching TV.",
          ">>pt<< Your message has been sent.",
          ">>es<< He's two years older than me.",
      ]
      lowerCamelCase  :       Union[str, Any]   =   [
          "Ne passez pas autant de temps à regarder la télé.",
          "A sua mensagem foi enviada.",
          "Es dos años más viejo que yo.",
      ]
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Union[str, Any]			)			->       str:
             self._assert_generated_batch_equal_expected()
  
  
  
      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def _a       (							self			:    Dict			)			->       Dict:
             __UpperCAmelCase    							=TranslationPipeline(self.model   ,    self.tokenizer   ,    framework="""tf"""			)
             __UpperCAmelCase    							=pipeline(self.src_text			)
             self.assertEqual(self.expected_text   ,    [x["""translation_text"""] for x in output]			)
  
  
  
  
  
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
  
  
  
  
  