
"""simple docstring"""





import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import (
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraModel,
    )
    from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class A_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=2				,   lowercase_=8				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=99				,   lowercase_=16				,   lowercase_=5				,   lowercase_=2				,   lowercase_=36				,   lowercase_="gelu"				,   lowercase_=0.0				,   lowercase_=0.0				,   lowercase_=512				,   lowercase_=16				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=3				,   lowercase_=4				,   lowercase_=None				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    parent
     UpperCAmelCase_					:					Optional[Any]			     =    batch_size
     UpperCAmelCase_					:					Any			     =    seq_length
     UpperCAmelCase_					:					Optional[int]			     =    is_training
     UpperCAmelCase_					:					int			     =    use_input_mask
     UpperCAmelCase_					:					Optional[Any]			     =    use_token_type_ids
     UpperCAmelCase_					:					Dict			     =    use_labels
     UpperCAmelCase_					:					Union[str, Any]			     =    vocab_size
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_size
     UpperCAmelCase_					:					List[Any]			     =    num_hidden_layers
     UpperCAmelCase_					:					Optional[int]			     =    num_attention_heads
     UpperCAmelCase_					:					Dict			     =    intermediate_size
     UpperCAmelCase_					:					List[Any]			     =    hidden_act
     UpperCAmelCase_					:					int			     =    hidden_dropout_prob
     UpperCAmelCase_					:					List[str]			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Any			     =    max_position_embeddings
     UpperCAmelCase_					:					int			     =    type_vocab_size
     UpperCAmelCase_					:					int			     =    type_sequence_label_size
     UpperCAmelCase_					:					Tuple			     =    initializer_range
     UpperCAmelCase_					:					Tuple			     =    num_labels
     UpperCAmelCase_					:					List[str]			     =    num_choices
     UpperCAmelCase_					:					Any			     =    scope







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )

     UpperCAmelCase_					:					Tuple			     =    None
     if self.use_input_mask:
        UpperCAmelCase_					:					List[str]			     =    random_attention_mask([self.batch_size, self.seq_length]  )

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

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

     UpperCAmelCase_					:					int			     =    self.get_config()

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return MraConfig(
         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				,   is_decoder=lowercase_				,   initializer_range=self.initializer_range				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.get_config()
     UpperCAmelCase_					:					List[Any]			     =    300
     return config







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Union[str, Any]			     =    self.prepare_config_and_inputs()

     UpperCAmelCase_					:					str			     =    True
     UpperCAmelCase_					:					Union[str, Any]			     =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
     UpperCAmelCase_					:					Dict			     =    ids_tensor([self.batch_size, self.seq_length]				,   vocab_size=2  )

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    MraModel(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					Tuple			     =    model(lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_				,   token_type_ids=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    model(lowercase_  )
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    True
     UpperCAmelCase_					:					Union[str, Any]			     =    MraModel(lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[str]			     =    model(
         lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   encoder_hidden_states=lowercase_				,   encoder_attention_mask=lowercase_				,   )
     UpperCAmelCase_					:					Union[str, Any]			     =    model(
         lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   encoder_hidden_states=lowercase_				,   )
     UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_  )
     self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    MraForMaskedLM(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[str]			     =    model(lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   labels=lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    MraForQuestionAnswering(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					int			     =    model(
         lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   start_positions=lowercase_				,   end_positions=lowercase_				,   )
     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				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.num_labels
     UpperCAmelCase_					:					Dict			     =    MraForSequenceClassification(lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   labels=lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.num_labels
     UpperCAmelCase_					:					int			     =    MraForTokenClassification(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   labels=lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.num_choices
     UpperCAmelCase_					:					List[Any]			     =    MraForMultipleChoice(config=lowercase_  )
     model.to(lowercase_  )
     model.eval()
     UpperCAmelCase_					:					Optional[int]			     =    input_ids.unsqueeze(1  ).expand(-1				,   self.num_choices				,   -1  ).contiguous()
     UpperCAmelCase_					:					Union[str, Any]			     =    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_					:					int			     =    model(
         lowercase_				,   attention_mask=lowercase_				,   token_type_ids=lowercase_				,   labels=lowercase_				,   )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_choices)  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Optional[Any]			     =    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_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		False
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		False
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False

  SCREAMING_SNAKE_CASE__			:						Dict       =		()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    MraModelTester(self  )
     UpperCAmelCase_					:					Tuple			     =    ConfigTester(self				,   config_class=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCAmelCase_					:					Tuple			     =    type
        self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_					:					Optional[int]			     =    MraModel.from_pretrained(lowercase_  )
        self.assertIsNotNone(lowercase_  )







  @unittest.skip(reason="MRA does not output attentions"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return




@require_torch
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    MraModel.from_pretrained("uw-madison/mra-base-512-4"  )
     UpperCAmelCase_					:					List[str]			     =    torch.arange(256  ).unsqueeze(0  )

     with torch.no_grad():
        UpperCAmelCase_					:					Optional[Any]			     =    model(lowercase_  )[0]

     UpperCAmelCase_					:					str			     =    torch.Size((1, 256, 768)  )
     self.assertEqual(output.shape				,   lowercase_  )

     UpperCAmelCase_					:					Optional[Any]			     =    torch.tensor(
         [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3]				,   lowercase_				,   atol=1E-4  )  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"  )
     UpperCAmelCase_					:					Optional[int]			     =    torch.arange(256  ).unsqueeze(0  )

     with torch.no_grad():
        UpperCAmelCase_					:					Dict			     =    model(lowercase_  )[0]

     UpperCAmelCase_					:					Dict			     =    5_0265

     UpperCAmelCase_					:					str			     =    torch.Size((1, 256, vocab_size)  )
     self.assertEqual(output.shape				,   lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    torch.tensor(
         [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3]				,   lowercase_				,   atol=1E-4  )  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"  )
     UpperCAmelCase_					:					List[Any]			     =    torch.arange(4096  ).unsqueeze(0  )

     with torch.no_grad():
        UpperCAmelCase_					:					str			     =    model(lowercase_  )[0]

     UpperCAmelCase_					:					List[str]			     =    5_0265

     UpperCAmelCase_					:					Tuple			     =    torch.Size((1, 4096, vocab_size)  )
     self.assertEqual(output.shape				,   lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    torch.tensor(
         [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]  )

     self.assertTrue(torch.allclose(output[:, :3, :3]				,   lowercase_				,   atol=1E-4  )  )




"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,   [0.0_02, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]  ):
        self.check_over_forward(time_step=lowercase_				,   num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0				,   0  ) - 0.0  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487				,   486  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_model()
     UpperCAmelCase_					:					List[str]			     =    self.dummy_sample_deter
     UpperCAmelCase_					:					Any			     =    self.dummy_sample_deter + 0.1
     UpperCAmelCase_					:					int			     =    self.dummy_sample_deter - 0.1

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3


