
from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__lowerCamelCase							=       logging.get_logger(__name__)


__lowerCamelCase							=       OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

__lowerCamelCase							=       OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

__lowerCamelCase							=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__lowerCamelCase							=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__lowerCamelCase							=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__lowerCamelCase							=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__lowerCamelCase							=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Any						     =     FLAX_MODEL_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModel)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Any						     =     FLAX_MODEL_FOR_PRETRAINING_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Optional[Any]						     =     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		int						     =     FLAX_MODEL_FOR_MASKED_LM_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		str						     =     FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Dict						     =     FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Tuple						     =     FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		str						     =     FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		int						     =     FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Optional[Any]						     =     FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Tuple						     =     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		int						     =     FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



__lowerCamelCase							=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")






class   UpperCAmelCase		(						_BaseAutoModelClass ):
   A__					:		Tuple						     =     FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



__lowerCamelCase							=       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     (DDIMParallelScheduler,)
   A__					:		Tuple						     =     (("eta", 0.0), ("num_inference_steps", 50))



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**snake_case__ )
          return config



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						**snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.scheduler_classes[0]
          snake_case       :   Any							=      self.get_scheduler_config(**snake_case__ )
          snake_case       :   Any							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Union[str, Any]							=      10, 0.0

          snake_case       :   List[Any]							=      self.dummy_model()
          snake_case       :   Any							=      self.dummy_sample_deter

          scheduler.set_timesteps(snake_case__ )

          for t in scheduler.timesteps:
                 snake_case       :   Optional[int]							=      model(snake_case__    ,						snake_case__ )
                 snake_case       :   List[str]							=      scheduler.step(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ ).prev_sample

          return sample



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	str:


          '''simple docstring'''


          for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=snake_case__ )

          snake_case       :   Optional[int]							=      self.scheduler_classes[0]
          snake_case       :   Optional[int]							=      self.get_scheduler_config(steps_offset=1 )
          snake_case       :   Union[str, Any]							=      scheduler_class(**snake_case__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,						[0.002, 0.02, 0.2, 2] ):
                 self.check_over_configs(beta_start=snake_case__    ,						beta_end=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=snake_case__ )



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


          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          self.check_over_configs(thresholding=snake_case__ )
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=snake_case__    ,						prediction_type=snake_case__    ,						sample_max_value=snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          for t in [1, 10, 49]:
                 self.check_over_forward(time_step=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Any:


          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 5_00] ):
                 self.check_over_forward(time_step=snake_case__    ,						num_inference_steps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[Any]:


          '''simple docstring'''


          for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=snake_case__    ,						eta=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.scheduler_classes[0]
          snake_case       :   Tuple							=      self.get_scheduler_config()
          snake_case       :   Dict							=      scheduler_class(**snake_case__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,						4_00 ) - 0.14771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,						9_60 ) - 0.32460 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,						4_86 ) - 0.00979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,						9_98 ) - 0.02 ) ) < 1e-5



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.scheduler_classes[0]
          snake_case       :   List[Any]							=      self.get_scheduler_config()
          snake_case       :   int							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Any							=      10, 0.0
          scheduler.set_timesteps(snake_case__ )

          snake_case       :   Optional[Any]							=      self.dummy_model()
          snake_case       :   str							=      self.dummy_sample_deter
          snake_case       :   Dict							=      self.dummy_sample_deter + 0.1
          snake_case       :   Dict							=      self.dummy_sample_deter - 0.1

          snake_case       :   Optional[Any]							=      samplea.shape[0]
          snake_case       :   str							=      torch.stack([samplea, samplea, samplea]    ,						dim=0 )
          snake_case       :   Tuple							=      torch.arange(snake_case__ )[0:3, None].repeat(1    ,						snake_case__ )

          snake_case       :   Tuple							=      model(samples.flatten(0    ,						1 )    ,						timesteps.flatten(0    ,						1 ) )
          snake_case       :   List[str]							=      scheduler.batch_step_no_noise(snake_case__    ,						timesteps.flatten(0    ,						1 )    ,						samples.flatten(0    ,						1 )    ,						snake_case__ )

          snake_case       :   Dict							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 1147.7904 ) < 1e-2
          assert abs(result_mean.item() - 0.4982 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.full_loop()

          snake_case       :   Optional[Any]							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.223967 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(prediction_type="v_prediction" )

          snake_case       :   int							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[int]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   str							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   Tuple							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3





