





'''simple docstring'''


import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import (
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetModel,
    )






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Tuple       ,     a__					:				Tuple       ,     a__					:				Optional[Any]=13       ,     a__					:				Tuple=7       ,     a__					:				Dict=True       ,     a__					:				Dict=True       ,     a__					:				List[Any]=False       ,     a__					:				int=True       ,     a__					:				Optional[Any]=99       ,     a__					:				Union[str, Any]=64       ,     a__					:				Optional[int]=5       ,     a__					:				Union[str, Any]=4       ,     a__					:				Any=64       ,     a__					:				Optional[int]="gelu"       ,     a__					:				Optional[int]=0.1       ,     a__					:				Dict=0.1       ,     a__					:				Optional[Any]=512       ,     a__					:				Optional[Any]=16       ,     a__					:				Dict=2       ,     a__					:				int=0.02       ,     a__					:				int=3       ,     a__					:				str=4       ,     a__					:				List[str]=None       ,     ):
              UpperCAmelCase  =						parent
              UpperCAmelCase  =						batch_size
              UpperCAmelCase  =						seq_length
              UpperCAmelCase  =						is_training
              UpperCAmelCase  =						use_input_mask
              UpperCAmelCase  =						use_token_type_ids
              UpperCAmelCase  =						use_labels
              UpperCAmelCase  =						vocab_size
              UpperCAmelCase  =						hidden_size
              UpperCAmelCase  =						num_hidden_layers
              UpperCAmelCase  =						num_attention_heads
              UpperCAmelCase  =						intermediate_size
              UpperCAmelCase  =						hidden_act
              UpperCAmelCase  =						hidden_dropout_prob
              UpperCAmelCase  =						attention_probs_dropout_prob
              UpperCAmelCase  =						max_position_embeddings
              UpperCAmelCase  =						type_vocab_size
              UpperCAmelCase  =						type_sequence_label_size
              UpperCAmelCase  =						initializer_range
              UpperCAmelCase  =						num_labels
              UpperCAmelCase  =						num_choices
              UpperCAmelCase  =						scope

       def 			__snake_case      ( self					:				List[str]						):
              return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''						)

       def 			__snake_case      ( self					:				str						):
              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
              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  =						self.get_config()
              return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

       def 			__snake_case      ( self					:				Union[str, Any]						):
              return MPNetConfig(
                  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       ,     initializer_range=self.initializer_range       ,     )

       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				Dict       ,     a__					:				List[Any]       ,     a__					:				List[Any]       ,     a__					:				List[str]       ,     a__					:				str       ,     a__					:				str						):
              UpperCAmelCase  =						MPNetModel(config=a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						model(a__       ,     a__						)
              UpperCAmelCase  =						model(a__						)
              self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)						)
              self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.hidden_size)						)

       def 			__snake_case      ( self					:				str       ,     a__					:				Tuple       ,     a__					:				List[str]       ,     a__					:				int       ,     a__					:				List[Any]       ,     a__					:				List[Any]       ,     a__					:				Union[str, Any]						):
              UpperCAmelCase  =						MPNetForQuestionAnswering(config=a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						model(
                  a__       ,     attention_mask=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 			__snake_case      ( self					:				Union[str, Any]       ,     a__					:				Any       ,     a__					:				Optional[int]       ,     a__					:				Any       ,     a__					:				Union[str, Any]       ,     a__					:				int       ,     a__					:				Optional[Any]						):
              UpperCAmelCase  =						self.num_labels
              UpperCAmelCase  =						MPNetForSequenceClassification(a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						model(a__       ,     attention_mask=a__       ,     labels=a__						)
              self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)						)

       def 			__snake_case      ( self					:				Any       ,     a__					:				int       ,     a__					:				Any       ,     a__					:				str       ,     a__					:				Dict       ,     a__					:				Tuple       ,     a__					:				List[Any]						):
              UpperCAmelCase  =						self.num_choices
              UpperCAmelCase  =						MPNetForMultipleChoice(config=a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						input_ids.unsqueeze(1						).expand(-1       ,     self.num_choices       ,     -1						).contiguous()
              UpperCAmelCase  =						input_mask.unsqueeze(1						).expand(-1       ,     self.num_choices       ,     -1						).contiguous()
              UpperCAmelCase  =						model(
                  a__       ,     attention_mask=a__       ,     labels=a__       ,     )
              self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)						)

       def 			__snake_case      ( self					:				List[str]       ,     a__					:				Dict       ,     a__					:				List[Any]       ,     a__					:				List[Any]       ,     a__					:				int       ,     a__					:				Dict       ,     a__					:				Any						):
              UpperCAmelCase  =						self.num_labels
              UpperCAmelCase  =						MPNetForTokenClassification(config=a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						model(a__       ,     attention_mask=a__       ,     labels=a__						)
              self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)						)



       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.prepare_config_and_inputs()
              ((UpperCAmelCase),					(UpperCAmelCase),					(UpperCAmelCase),					(UpperCAmelCase),					(UpperCAmelCase),					(UpperCAmelCase))  =						config_and_inputs
              UpperCAmelCase  =						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
              return config, inputs_dict






@require_torch
class     lowerCAmelCase__ (				UpperCAmelCase_      , UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=(
           (
               MPNetForMaskedLM,
               MPNetForMultipleChoice,
               MPNetForQuestionAnswering,
               MPNetForSequenceClassification,
               MPNetForTokenClassification,
               MPNetModel,
           )
           if is_torch_available()
           else ()
       )
       _lowerCamelCase								=(
           {
               "feature-extraction": MPNetModel,
               "fill-mask": MPNetForMaskedLM,
               "question-answering": MPNetForQuestionAnswering,
               "text-classification": MPNetForSequenceClassification,
               "token-classification": MPNetForTokenClassification,
               "zero-shot": MPNetForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       _lowerCamelCase								=False
       _lowerCamelCase								=True

       def 			__snake_case      ( self					:				Optional[int]						):
              UpperCAmelCase  =						MPNetModelTester(self						)
              UpperCAmelCase  =						ConfigTester(self       ,     config_class=a__       ,     hidden_size=37						)

       def 			__snake_case      ( self					:				Optional[int]						):
              self.config_tester.run_common_tests()

       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_model(*a__						)

       def 			__snake_case      ( self					:				Dict						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_sequence_classification(*a__						)

       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_multiple_choice(*a__						)

       def 			__snake_case      ( self					:				Tuple						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_token_classification(*a__						)



       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_mpnet_for_question_answering(*a__						)






@require_torch
class     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''

       @slow
       def 			__snake_case      ( self					:				Tuple						):
              UpperCAmelCase  =						MPNetModel.from_pretrained('''microsoft/mpnet-base'''						)
              UpperCAmelCase  =						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
              UpperCAmelCase  =						model(a__						)[0]
              UpperCAmelCase  =						torch.Size((1, 11, 768)						)
              self.assertEqual(output.shape       ,     a__						)
              UpperCAmelCase  =						torch.tensor(
                  [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]						)
              # compare the actual values for a slice.
              self.assertTrue(torch.allclose(output[:, :3, :3]       ,     a__       ,     atol=1e-4						)						)




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(DEISMultistepScheduler,)
     SCREAMING_SNAKE_CASE_		 =	(('''num_inference_steps''', 25),)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
           }

           config.update(**__SCREAMING_SNAKE_CASE	)
           return config






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Dict											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					self.dummy_sample
           UpperCamelCase__     : Optional[int]											=					0.1 * sample
           UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 UpperCamelCase__					,UpperCamelCase__     : Any											=					sample, sample
                 for t in range(__SCREAMING_SNAKE_CASE    ,    time_step + scheduler.config.solver_order + 1	):
                       UpperCamelCase__     : Union[str, Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                       UpperCamelCase__     : int											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           pass






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					dict(self.forward_default_kwargs	)
           UpperCamelCase__     : Tuple											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					self.dummy_sample
           UpperCamelCase__     : List[Any]											=					0.1 * sample
           UpperCamelCase__     : Dict											=					[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Union[str, Any]											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE	)
                       UpperCamelCase__     : str											=					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__     : Optional[int]											=					dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Dict											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if scheduler is None:
                 UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
                 UpperCamelCase__     : str											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Dict											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[str]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Tuple											=					self.get_scheduler_config(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Tuple											=					self.dummy_model()
           UpperCamelCase__     : Dict											=					self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Optional[Any]											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           return sample






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					dict(self.forward_default_kwargs	)

           UpperCamelCase__     : List[Any]											=					kwargs.pop('''num_inference_steps'''    ,    __SCREAMING_SNAKE_CASE	)

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__     : Optional[int]											=					self.get_scheduler_config()
                 UpperCamelCase__     : Any											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

                 UpperCamelCase__     : int											=					self.dummy_sample
                 UpperCamelCase__     : int											=					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__     : str											=					num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 UpperCamelCase__     : Tuple											=					[residual + 0.2, residual + 0.15, residual + 0.10]
                 UpperCamelCase__     : Dict											=					dummy_past_residuals[: scheduler.config.solver_order]

                 UpperCamelCase__     : int											=					scheduler.timesteps[5]
                 UpperCamelCase__     : Optional[int]											=					scheduler.timesteps[6]

                 UpperCamelCase__     : Any											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	).prev_sample
                 UpperCamelCase__     : Any											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler(**self.get_scheduler_config()	)
           UpperCamelCase__     : Tuple											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3

           UpperCamelCase__     : List[Any]											=					DPMSolverSinglestepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : Union[str, Any]											=					DPMSolverMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : int											=					UniPCMultistepScheduler.from_config(scheduler.config	)
           UpperCamelCase__     : List[str]											=					DEISMultistepScheduler.from_config(scheduler.config	)

           UpperCamelCase__     : Dict											=					self.full_loop(scheduler=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE	)
           for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                       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='''deis'''    ,    solver_order=__SCREAMING_SNAKE_CASE    ,    solver_type=__SCREAMING_SNAKE_CASE    ,    )






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   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__     : Any											=					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  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE    ,    time_step=0	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.full_loop()
           UpperCamelCase__     : Optional[int]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.23916	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					self.full_loop(prediction_type='''v_prediction'''	)
           UpperCamelCase__     : List[Any]											=					torch.mean(torch.abs(__SCREAMING_SNAKE_CASE	)	)

           assert abs(result_mean.item() - 0.091	) < 1e-3






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.scheduler_classes[0]
           UpperCamelCase__     : Optional[Any]											=					self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE    ,    dynamic_thresholding_ratio=0	)
           UpperCamelCase__     : Tuple											=					scheduler_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					1_0
           UpperCamelCase__     : Dict											=					self.dummy_model()
           UpperCamelCase__     : Tuple											=					self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)

           for i, t in enumerate(scheduler.timesteps	):
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
                 UpperCamelCase__     : Tuple											=					scheduler.step(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	).prev_sample

           assert sample.dtype == torch.floataa


