





from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 import (
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertModel,
      )





@require_tf
class 				A_				(      SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : str      =  (
           (
               TFMobileBertModel,
               TFMobileBertForMaskedLM,
               TFMobileBertForNextSentencePrediction,
               TFMobileBertForPreTraining,
               TFMobileBertForQuestionAnswering,
               TFMobileBertForSequenceClassification,
               TFMobileBertForTokenClassification,
               TFMobileBertForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  (
           {
               '''feature-extraction''': TFMobileBertModel,
               '''fill-mask''': TFMobileBertForMaskedLM,
               '''question-answering''': TFMobileBertForQuestionAnswering,
               '''text-classification''': TFMobileBertForSequenceClassification,
               '''token-classification''': TFMobileBertForTokenClassification,
               '''zero-shot''': TFMobileBertForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_      : Tuple      =  False
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  False







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Optional[Any]						,__A	:	List[str]						,__A	:	int=False  )						->					Union[str, Any]:
         _lowercase							= super()._prepare_for_class(UpperCamelCase__						,UpperCamelCase__						,return_labels=UpperCamelCase__  )

         if return_labels:
           if model_class in get_values(UpperCamelCase__  ):
             _lowercase							= tf.zeros(self.model_tester.batch_size						,dtype=tf.intaa  )

         return inputs_dict





       class 				A_				(      SCREAMING_SNAKE_CASE_	):







              """simple docstring"""





              def __init__(    self	:	int						,__A	:	List[Any]						,__A	:	Union[str, Any]=13						,__A	:	Optional[int]=7						,__A	:	Optional[Any]=True						,__A	:	Union[str, Any]=True						,__A	:	Any=True						,__A	:	Tuple=True						,__A	:	int=99						,__A	:	Optional[int]=32						,__A	:	int=32						,__A	:	List[Any]=2						,__A	:	Any=4						,__A	:	Dict=37						,__A	:	List[Any]="gelu"						,__A	:	str=0.1						,__A	:	Optional[int]=0.1						,__A	:	int=512						,__A	:	Tuple=16						,__A	:	Optional[int]=2						,__A	:	List[Any]=0.02						,__A	:	Dict=3						,__A	:	Tuple=4						,__A	:	str=None						,)						->					Tuple:
                _lowercase							= parent
                _lowercase							= batch_size
                _lowercase							= seq_length
                _lowercase							= is_training
                _lowercase							= use_input_mask
                _lowercase							= use_token_type_ids
                _lowercase							= use_labels
                _lowercase							= vocab_size
                _lowercase							= hidden_size
                _lowercase							= num_hidden_layers
                _lowercase							= num_attention_heads
                _lowercase							= intermediate_size
                _lowercase							= hidden_act
                _lowercase							= hidden_dropout_prob
                _lowercase							= attention_probs_dropout_prob
                _lowercase							= max_position_embeddings
                _lowercase							= type_vocab_size
                _lowercase							= type_sequence_label_size
                _lowercase							= initializer_range
                _lowercase							= num_labels
                _lowercase							= num_choices
                _lowercase							= scope
                _lowercase							= embedding_size







              def    __UpperCAmelCase (    self	:	Tuple  )						->					str:
                _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

                _lowercase							= None
                if self.use_input_mask:
                  _lowercase							= random_attention_mask([self.batch_size, self.seq_length]  )

                _lowercase							= None
                if self.use_token_type_ids:
                  _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.type_vocab_size  )

                _lowercase							= None
                _lowercase							= None
                _lowercase							= None
                if self.use_labels:
                  _lowercase							= ids_tensor([self.batch_size]						,self.type_sequence_label_size  )
                  _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.num_labels  )
                  _lowercase							= ids_tensor([self.batch_size]						,self.num_choices  )

                _lowercase							= MobileBertConfig(
                    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						,embedding_size=self.embedding_size						,)

                return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







              def    __UpperCAmelCase (    self	:	Any						,__A	:	Dict						,__A	:	Union[str, Any]						,__A	:	Union[str, Any]						,__A	:	int						,__A	:	int						,__A	:	Dict						,__A	:	Optional[int]  )						->					Optional[Any]:
                _lowercase							= TFMobileBertModel(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )

                _lowercase							= [input_ids, input_mask]
                _lowercase							= model(UpperCamelCase__  )

                _lowercase							= model(UpperCamelCase__  )

                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    __UpperCAmelCase (    self	:	str						,__A	:	Optional[int]						,__A	:	Union[str, Any]						,__A	:	Any						,__A	:	Optional[Any]						,__A	:	Optional[Any]						,__A	:	Optional[Any]						,__A	:	int  )						->					Tuple:
                _lowercase							= TFMobileBertForMaskedLM(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.vocab_size)  )







              def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	str						,__A	:	Optional[Any]						,__A	:	str						,__A	:	Optional[int]						,__A	:	str						,__A	:	Dict						,__A	:	Union[str, Any]  )						->					List[Any]:
                _lowercase							= TFMobileBertForNextSentencePrediction(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(result.logits.shape						,(self.batch_size, 2)  )







              def    __UpperCAmelCase (    self	:	List[str]						,__A	:	Optional[int]						,__A	:	Tuple						,__A	:	List[str]						,__A	:	List[Any]						,__A	:	Union[str, Any]						,__A	:	Optional[Any]						,__A	:	str  )						->					Union[str, Any]:
                _lowercase							= TFMobileBertForPreTraining(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(
                    result.prediction_logits.shape						,(self.batch_size, self.seq_length, self.vocab_size)  )
                self.parent.assertEqual(result.seq_relationship_logits.shape						,(self.batch_size, 2)  )







              def    __UpperCAmelCase (    self	:	int						,__A	:	Dict						,__A	:	Dict						,__A	:	Optional[Any]						,__A	:	Any						,__A	:	Union[str, Any]						,__A	:	Optional[Any]						,__A	:	List[str]  )						->					Union[str, Any]:
                _lowercase							= self.num_labels
                _lowercase							= TFMobileBertForSequenceClassification(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)  )







              def    __UpperCAmelCase (    self	:	str						,__A	:	Optional[int]						,__A	:	Optional[Any]						,__A	:	Dict						,__A	:	Dict						,__A	:	int						,__A	:	List[str]						,__A	:	Tuple  )						->					List[Any]:
                _lowercase							= self.num_choices
                _lowercase							= TFMobileBertForMultipleChoice(config=UpperCamelCase__  )
                _lowercase							= tf.tile(tf.expand_dims(UpperCamelCase__						,1  )						,(1, self.num_choices, 1)  )
                _lowercase							= tf.tile(tf.expand_dims(UpperCamelCase__						,1  )						,(1, self.num_choices, 1)  )
                _lowercase							= tf.tile(tf.expand_dims(UpperCamelCase__						,1  )						,(1, self.num_choices, 1)  )
                _lowercase							= {
                    '''input_ids''': multiple_choice_inputs_ids,
                    '''attention_mask''': multiple_choice_input_mask,
                    '''token_type_ids''': multiple_choice_token_type_ids,
                }
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_choices)  )







              def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Any						,__A	:	Optional[Any]						,__A	:	int						,__A	:	Dict						,__A	:	int						,__A	:	Optional[int]						,__A	:	Any  )						->					int:
                _lowercase							= self.num_labels
                _lowercase							= TFMobileBertForTokenClassification(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.num_labels)  )







              def    __UpperCAmelCase (    self	:	Dict						,__A	:	Tuple						,__A	:	Optional[Any]						,__A	:	int						,__A	:	Tuple						,__A	:	Optional[int]						,__A	:	str						,__A	:	str  )						->					Tuple:
                _lowercase							= TFMobileBertForQuestionAnswering(config=UpperCamelCase__  )
                _lowercase							= {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
                _lowercase							= model(UpperCamelCase__  )
                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    __UpperCAmelCase (    self	:	int  )						->					Tuple:
                _lowercase							= self.prepare_config_and_inputs()
                (
                    _lowercase							
                )							= config_and_inputs
                _lowercase							= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
                return config, inputs_dict







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         _lowercase							= TFMobileBertModelTest.TFMobileBertModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=UpperCamelCase__						,hidden_size=37  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         self.config_tester.run_common_tests()







       def    __UpperCAmelCase (    self	:	List[str]  )						->					List[str]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_model(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Optional[int]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_masked_lm(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Union[str, Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Dict:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Union[str, Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					str:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					List[Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__  )







       @slow
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Dict:
         # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         for model_name in ["google/mobilebert-uncased"]:
           _lowercase							= TFMobileBertModel.from_pretrained(UpperCamelCase__  )
           self.assertIsNotNone(UpperCamelCase__  )





@require_tf
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @slow
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         _lowercase							= TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'  )
         _lowercase							= tf.constant([[0, 1, 2, 3, 4, 5]]  )
         _lowercase							= model(UpperCamelCase__  )[0]

         _lowercase							= [1, 6, 3_0522]
         self.assertEqual(output.shape						,UpperCamelCase__  )

         _lowercase							= tf.constant(
             [
                 [
                     [-4.5919547, -9.248295, -9.645256],
                     [-6.7306175, -6.440284, -6.6052837],
                     [-7.2743506, -6.7847915, -6.024673],
                 ]
             ]  )
         tf.debugging.assert_near(output[:, :3, :3]						,UpperCamelCase__						,atol=1e-4  )






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE_      : List[Any]      =  (('''num_inference_steps''', 2_5),)







       def    __UpperCAmelCase (    self	:	List[Any]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str=0						,**__A	:	Optional[int]  )						->					Tuple:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

           _lowercase						,		_lowercase							= sample, sample
           for t in range(__A						,time_step + scheduler.config.solver_order + 1  ):
             _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
             _lowercase							= 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    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         pass







       def    __UpperCAmelCase (    self	:	int						,__A	:	Dict=0						,**__A	:	Optional[Any]  )						->					Dict:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )

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

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

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= 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    __UpperCAmelCase (    self	:	Tuple						,__A	:	Tuple=None						,**__A	:	Optional[Any]  )						->					Tuple:
         if scheduler is None:
           _lowercase							= self.scheduler_classes[0]
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )

         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(**__A  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter
         scheduler.set_timesteps(__A  )

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

         return sample







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= dict(self.forward_default_kwargs  )

         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )

           _lowercase							= self.dummy_sample
           _lowercase							= 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'  ):
             _lowercase							= num_inference_steps

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

           _lowercase							= scheduler.timesteps[5]
           _lowercase							= scheduler.timesteps[6]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

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







       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _lowercase							= DEISMultistepScheduler(**self.get_scheduler_config()  )
         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

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

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

         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

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







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         self.check_over_configs(thresholding=__A  )
         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=__A						,prediction_type=__A						,sample_max_value=__A						,algorithm_type='deis'						,solver_order=__A						,solver_type=__A						,)







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         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=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 _lowercase							= self.full_loop(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 assert not torch.isnan(__A  ).any(), "Samples have nan numbers"







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.check_over_configs(lower_order_final=__A  )
         self.check_over_configs(lower_order_final=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         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    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.full_loop()
         _lowercase							= torch.mean(torch.abs(__A  )  )

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







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.full_loop(prediction_type='v_prediction'  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

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







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(thresholding=__A						,dynamic_thresholding_ratio=0  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.half()
         scheduler.set_timesteps(__A  )

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

         assert sample.dtype == torch.floataa