

"""simple docstring"""






from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
 from transformers import (
     TFAutoModel,
     TFAutoModelForCausalLM,
     TFAutoModelForMaskedLM,
     TFAutoModelForPreTraining,
     TFAutoModelForQuestionAnswering,
     TFAutoModelForSeqaSeqLM,
     TFAutoModelForSequenceClassification,
     TFAutoModelForTableQuestionAnswering,
     TFAutoModelForTokenClassification,
     TFAutoModelWithLMHead,
     TFBertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
     TFBertModel,
     TFFunnelBaseModel,
     TFFunnelModel,
     TFGPTaLMHeadModel,
     TFRobertaForMaskedLM,
     TFTaForConditionalGeneration,
     TFTapasForQuestionAnswering,
 )
 from transformers.models.auto.modeling_tf_auto import (
     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_MASKED_LM_MAPPING,
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     TF_MODEL_MAPPING,
 )
 from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		"new-model"


if is_tf_available():







 class 	__UpperCamelCase    (    a__						):
   _UpperCAmelCase          =		NewModelConfig







@require_tf
class 	__UpperCamelCase    (    unittest.TestCase						):

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    'bert-base-cased'
      _lowerCAmelCase       :   Tuple  =    AutoConfig.from_pretrained(_A      )
      self.assertIsNotNone(_A      )
      self.assertIsInstance(_A    ,_A      )

      _lowerCAmelCase       :   Dict  =    TFAutoModel.from_pretrained(_A      )
      self.assertIsNotNone(_A      )
      self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    'bert-base-cased'
      _lowerCAmelCase       :   Optional[Any]  =    AutoConfig.from_pretrained(_A      )
      self.assertIsNotNone(_A      )
      self.assertIsInstance(_A    ,_A      )

      _lowerCAmelCase       :   Optional[int]  =    TFAutoModelForPreTraining.from_pretrained(_A      )
      self.assertIsNotNone(_A      )
      self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   int  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   Dict  =    TFAutoModelForCausalLM.from_pretrained(_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    TFAutoModelForCausalLM.from_pretrained(_A    ,output_loading_info=_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   int  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   Dict  =    TFAutoModelWithLMHead.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   Optional[int]  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   Dict  =    TFAutoModelForMaskedLM.from_pretrained(_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    TFAutoModelForMaskedLM.from_pretrained(_A    ,output_loading_info=_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   Optional[Any]  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   Union[str, Any]  =    TFAutoModelForSeqaSeqLM.from_pretrained(_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    TFAutoModelForSeqaSeqLM.from_pretrained(_A    ,output_loading_info=_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in ["bert-base-uncased"]:
          _lowerCAmelCase       :   Dict  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   List[Any]  =    TFAutoModelForSequenceClassification.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in ["bert-base-uncased"]:
          _lowerCAmelCase       :   Union[str, Any]  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   Union[str, Any]  =    TFAutoModelForQuestionAnswering.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  @slow
  @require_tensorflow_probability
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
          _lowerCAmelCase       :   Union[str, Any]  =    AutoConfig.from_pretrained(_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

          _lowerCAmelCase       :   str  =    TFAutoModelForTableQuestionAnswering.from_pretrained(_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    TFAutoModelForTableQuestionAnswering.from_pretrained(
              _A    ,output_loading_info=_A      )
          self.assertIsNotNone(_A      )
          self.assertIsInstance(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    TFAutoModelWithLMHead.from_pretrained(_A      )
      self.assertIsInstance(_A    ,_A      )
      self.assertEqual(model.num_parameters()    ,1_4410      )
      self.assertEqual(model.num_parameters(only_trainable=_A      )    ,1_4410      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    TFAutoModelWithLMHead.from_pretrained(_A      )
      self.assertIsInstance(_A    ,_A      )
      self.assertEqual(model.num_parameters()    ,1_4410      )
      self.assertEqual(model.num_parameters(only_trainable=_A      )    ,1_4410      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'      )
      self.assertIsInstance(_A    ,_A      )

      _lowerCAmelCase       :   Optional[int]  =    copy.deepcopy(model.config      )
      _lowerCAmelCase       :   Union[str, Any]  =    ['FunnelBaseModel']
      _lowerCAmelCase       :   List[str]  =    TFAutoModel.from_config(_A      )
      self.assertIsInstance(_A    ,_A      )

      with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_pretrained(_A      )
          _lowerCAmelCase       :   List[Any]  =    TFAutoModel.from_pretrained(_A      )
          self.assertIsInstance(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      try:
          AutoConfig.register('new-model'    ,_A      )

          _lowerCAmelCase       :   Any  =    [
              TFAutoModel,
              TFAutoModelForCausalLM,
              TFAutoModelForMaskedLM,
              TFAutoModelForPreTraining,
              TFAutoModelForQuestionAnswering,
              TFAutoModelForSequenceClassification,
              TFAutoModelForTokenClassification,
          ]

          for auto_class in auto_classes:
              with self.subTest(auto_class.__name__      ):
                  # Wrong config class will raise an error
                  with self.assertRaises(_A      ):
                      auto_class.register(_A    ,_A      )
                  auto_class.register(_A    ,_A      )
                  # Trying to register something existing in the Transformers library will raise an error
                  with self.assertRaises(_A      ):
                      auto_class.register(_A    ,_A      )

                  # Now that the config is registered, it can be used as any other config with the auto-API
                  _lowerCAmelCase       :   Optional[Any]  =    BertModelTester(self      ).get_config()
                  _lowerCAmelCase       :   Optional[int]  =    NewModelConfig(**tiny_config.to_dict()      )
                  _lowerCAmelCase       :   str  =    auto_class.from_config(_A      )
                  self.assertIsInstance(_A    ,_A      )

                  with tempfile.TemporaryDirectory() as tmp_dir:
                      model.save_pretrained(_A      )
                      _lowerCAmelCase       :   Union[str, Any]  =    auto_class.from_pretrained(_A      )
                      self.assertIsInstance(_A    ,_A      )

      finally:
          if "new-model" in CONFIG_MAPPING._extra_content:
              del CONFIG_MAPPING._extra_content["new-model"]
          for mapping in (
              TF_MODEL_MAPPING,
              TF_MODEL_FOR_PRETRAINING_MAPPING,
              TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_CAUSAL_LM_MAPPING,
              TF_MODEL_FOR_MASKED_LM_MAPPING,
          ):
              if NewModelConfig in mapping._extra_content:
                  del mapping._extra_content[NewModelConfig]

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with self.assertRaisesRegex(
          _A    ,'bert-base is not a local folder and is not a valid model identifier'      ):
          _lowerCAmelCase       :   Optional[int]  =    TFAutoModel.from_pretrained('bert-base'      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with self.assertRaisesRegex(
          _A    ,r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
          _lowerCAmelCase       :   List[Any]  =    TFAutoModel.from_pretrained(_A    ,revision='aaaaaa'      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with self.assertRaisesRegex(
          _A    ,'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'    ,):
          _lowerCAmelCase       :   Any  =    TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      with self.assertRaisesRegex(_A    ,'Use `from_pt=True` to load this model'      ):
          _lowerCAmelCase       :   List[str]  =    TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'      )
      with RequestCounter() as counter:
          _lowerCAmelCase       :   List[Any]  =    TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'      )
          self.assertEqual(counter.get_request_count    ,0      )
          self.assertEqual(counter.head_request_count    ,1      )
          self.assertEqual(counter.other_request_count    ,0      )

      # With a sharded checkpoint
      _lowerCAmelCase       :   Optional[Any]  =    TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'      )
      with RequestCounter() as counter:
          _lowerCAmelCase       :   List[str]  =    TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'      )
          self.assertEqual(counter.get_request_count    ,0      )
          self.assertEqual(counter.head_request_count    ,1      )
          self.assertEqual(counter.other_request_count    ,0      )








"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class.from_pretrained(_A      )
              new_scheduler.set_timesteps(_A      )
              # copy over dummy past residuals
              _lowerCAmelCase       :   Union[str, Any]  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, Any]  =    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 __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

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

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   int  =    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)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, Any]  =    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 __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
      _lowerCAmelCase       :   int  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

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

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    dict(self.forward_default_kwargs      )

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   str  =    self.get_scheduler_config()
          _lowerCAmelCase       :   List[str]  =    scheduler_class(**_A      )

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    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'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _lowerCAmelCase       :   Optional[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          _lowerCAmelCase       :   Optional[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

          _lowerCAmelCase       :   Any  =    scheduler.timesteps[5]
          _lowerCAmelCase       :   List[str]  =    scheduler.timesteps[6]

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for timesteps in [25, 50, 100, 999, 1000]:
          self.check_over_configs(num_train_timesteps=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              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    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          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    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      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 __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.1_0_1_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Tuple  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
          assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





