




import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=99       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE="None"       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Optional[int]:
         UpperCAmelCase_      :							Any       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							List[Any]       =      is_training
         UpperCAmelCase_      :							Union[str, Any]       =      use_input_mask
         UpperCAmelCase_      :							Optional[Any]       =      use_token_type_ids
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							Optional[Any]       =      vocab_size
         UpperCAmelCase_      :							str       =      hidden_size
         UpperCAmelCase_      :							Optional[int]       =      num_hidden_layers
         UpperCAmelCase_      :							Dict       =      num_attention_heads
         UpperCAmelCase_      :							Any       =      intermediate_size
         UpperCAmelCase_      :							Dict       =      hidden_act
         UpperCAmelCase_      :							Tuple       =      hidden_dropout_prob
         UpperCAmelCase_      :							int       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Tuple       =      max_position_embeddings
         UpperCAmelCase_      :							Union[str, Any]       =      type_vocab_size
         UpperCAmelCase_      :							str       =      type_sequence_label_size
         UpperCAmelCase_      :							List[Any]       =      initializer_range
         UpperCAmelCase_      :							List[Any]       =      num_labels
         UpperCAmelCase_      :							str       =      num_choices
         UpperCAmelCase_      :							Union[str, Any]       =      relative_attention
         UpperCAmelCase_      :							List[Any]       =      position_biased_input
         UpperCAmelCase_      :							Union[str, Any]       =      pos_att_type
         UpperCAmelCase_      :							Dict       =      scope





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							Any       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							Union[str, Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,vocab_size=2  )

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

         UpperCAmelCase_      :							int       =      None
         UpperCAmelCase_      :							Any       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Dict       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							Tuple       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							Any       =      self.get_config()

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





    def 							a__				(				self  )			->		Optional[Any]:
         return DebertaVaConfig(
             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       ,relative_attention=self.relative_attention       ,position_biased_input=self.position_biased_input       ,pos_att_type=self.pos_att_type       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         self.parent.assertListEqual(list(result.loss.size()  )       ,[]  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Optional[int]       =      DebertaVaModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							str       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE  )[0]
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE  )[0]
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )[0]

         self.parent.assertListEqual(list(sequence_output.size()  )       ,[self.batch_size, self.seq_length, self.hidden_size]  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							str       =      DebertaVaForMaskedLM(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[str]:
         UpperCAmelCase_      :							Tuple       =      self.num_labels
         UpperCAmelCase_      :							int       =      DebertaVaForSequenceClassification(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertListEqual(list(result.logits.size()  )       ,[self.batch_size, self.num_labels]  )
         self.check_loss_output(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							Optional[Any]       =      self.num_labels
         UpperCAmelCase_      :							str       =      DebertaVaForTokenClassification(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Optional[Any]       =      DebertaVaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Dict       =      model(
             _SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,start_positions=_SCREAMING_SNAKE_CASE       ,end_positions=_SCREAMING_SNAKE_CASE       ,)
         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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							int       =      DebertaVaForMultipleChoice(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      input_ids.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							List[str]       =      token_type_ids.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							Optional[int]       =      input_mask.unsqueeze(1  ).expand(-1       ,self.num_choices       ,-1  ).contiguous()
         UpperCAmelCase_      :							List[Any]       =      model(
             _SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE       ,)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_choices)  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							str       =      config_and_inputs
         UpperCAmelCase_      :							List[str]       =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				(
        {
            '''feature-extraction''': DebertaVaModel,
            '''fill-mask''': DebertaVaForMaskedLM,
            '''question-answering''': DebertaVaForQuestionAnswering,
            '''text-classification''': DebertaVaForSequenceClassification,
            '''token-classification''': DebertaVaForTokenClassification,
            '''zero-shot''': DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    lowerCAmelCase        =				True
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Tuple       =      DebertaVaModelTester(self  )
         UpperCAmelCase_      :							List[Any]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		int:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							List[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_multiple_choice(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		List[Any]:
         for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							Optional[int]       =      DebertaVaModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )


@require_torch
@require_sentencepiece
@require_tokenizers
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @unittest.skip(reason='''Model not available yet'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @slow
    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge'''  )

         UpperCAmelCase_      :							Optional[Any]       =      torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
         UpperCAmelCase_      :							List[Any]       =      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
         with torch.no_grad():
              UpperCAmelCase_      :							Dict       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )[0]
         # compare the actual values for a slice.
         UpperCAmelCase_      :							Tuple       =      torch.tensor(
             [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]  )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )       ,f'''{output[:, 1:4, 1:4]}'''  )





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(IPNDMScheduler,)
    lowerCAmelCase        =				(('''num_inference_steps''', 50),)





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Tuple       =      {'''num_train_timesteps''': 1_000}
         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      self.dummy_sample
         UpperCAmelCase_      :							Optional[Any]       =      0.1 * sample
         UpperCAmelCase_      :							Optional[int]       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							Tuple       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              # copy over dummy past residuals
              UpperCAmelCase_      :							Optional[Any]       =      dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_      :							Tuple       =      scheduler.timesteps[len(scheduler.timesteps  ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE  )
                   new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
                   # copy over dummy past residuals
                   UpperCAmelCase_      :							Optional[int]       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							str       =      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"

              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[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 							a__				(				self  )			->		List[Any]:
         pass





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.dummy_sample
         UpperCAmelCase_      :							Tuple       =      0.1 * sample
         UpperCAmelCase_      :							int       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							str       =      self.get_scheduler_config()
              UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCAmelCase_      :							Tuple       =      dummy_past_residuals[:]

              if time_step is None:
                   UpperCAmelCase_      :							Dict       =      scheduler.timesteps[len(scheduler.timesteps  ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Optional[int]       =      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_      :							str       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							Tuple       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[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"

              UpperCAmelCase_      :							str       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							List[str]       =      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       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Tuple       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[Any]       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      10
         UpperCAmelCase_      :							Tuple       =      self.dummy_model()
         UpperCAmelCase_      :							List[str]       =      self.dummy_sample_deter
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         return sample





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      dict(self.forward_default_kwargs  )

         UpperCAmelCase_      :							Any       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							int       =      self.get_scheduler_config()
              UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Optional[int]       =      self.dummy_sample
              UpperCAmelCase_      :							List[Any]       =      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_      :							Union[str, Any]       =      num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase_      :							str       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              UpperCAmelCase_      :							int       =      dummy_past_residuals[:]

              UpperCAmelCase_      :							List[Any]       =      scheduler.timesteps[5]
              UpperCAmelCase_      :							Dict       =      scheduler.timesteps[6]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      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  )

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      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  )			->		Any:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         for t, num_inference_steps in zip([1, 5, 10]       ,[10, 50, 100]  ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.full_loop()
         UpperCAmelCase_      :							str       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_mean.item() - 2_540_529  ) < 10