

"""simple docstring"""






import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY







@is_pipeline_test
class 						__UpperCamelCase     (     unittest.TestCase    ):
  _UpperCAmelCase        =		MODEL_FOR_MASKED_LM_MAPPING
  _UpperCAmelCase        =		TF_MODEL_FOR_MASKED_LM_MAPPING

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

      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      if is_torch_available():
          import torch

          torch.cuda.empty_cache()

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

      _lowerCAmelCase							: List[Any]	=     pipeline(task='fill-mask'     ,model='sshleifer/tiny-distilroberta-base'     ,top_k=2     ,framework='tf'   )
      _lowerCAmelCase							: int	=     unmasker('My name is <mask>'   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {'sequence': 'My name is grouped', 'score': 2.1E-05, 'token': 3_8015, 'token_str': ' grouped'},
              {'sequence': 'My name is accuser', 'score': 2.1E-05, 'token': 2_5506, 'token_str': ' accuser'},
          ]     ,)

      _lowerCAmelCase							: List[Any]	=     unmasker('The largest city in France is <mask>'   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {
                  'sequence': 'The largest city in France is grouped',
                  'score': 2.1E-05,
                  'token': 3_8015,
                  'token_str': ' grouped',
              },
              {
                  'sequence': 'The largest city in France is accuser',
                  'score': 2.1E-05,
                  'token': 2_5506,
                  'token_str': ' accuser',
              },
          ]     ,)

      _lowerCAmelCase							: Any	=     unmasker('My name is <mask>'     ,targets=[' Patrick', ' Clara', ' Teven']     ,top_k=3   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 1_3606, 'token_str': ' Clara'},
              {'sequence': 'My name is Patrick', 'score': 2E-05, 'token': 3499, 'token_str': ' Patrick'},
              {'sequence': 'My name is Te', 'score': 1.9E-05, 'token': 2941, 'token_str': ' Te'},
          ]     ,)

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

      _lowerCAmelCase							: str	=     pipeline(task='fill-mask'     ,model='sshleifer/tiny-distilroberta-base'     ,top_k=2     ,framework='pt'   )

      _lowerCAmelCase							: Tuple	=     unmasker('My name is <mask>'   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {'sequence': 'My name is Maul', 'score': 2.2E-05, 'token': 3_5676, 'token_str': ' Maul'},
              {'sequence': 'My name isELS', 'score': 2.2E-05, 'token': 1_6416, 'token_str': 'ELS'},
          ]     ,)

      _lowerCAmelCase							: str	=     unmasker('The largest city in France is <mask>'   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {
                  'sequence': 'The largest city in France is Maul',
                  'score': 2.2E-05,
                  'token': 3_5676,
                  'token_str': ' Maul',
              },
              {'sequence': 'The largest city in France isELS', 'score': 2.2E-05, 'token': 1_6416, 'token_str': 'ELS'},
          ]     ,)

      _lowerCAmelCase							: Union[str, Any]	=     unmasker('My name is <mask>'     ,targets=[' Patrick', ' Clara', ' Teven']     ,top_k=3   )
      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              {'sequence': 'My name is Patrick', 'score': 2.1E-05, 'token': 3499, 'token_str': ' Patrick'},
              {'sequence': 'My name is Te', 'score': 2E-05, 'token': 2941, 'token_str': ' Te'},
              {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 1_3606, 'token_str': ' Clara'},
          ]     ,)

      _lowerCAmelCase							: Tuple	=     unmasker('My name is <mask> <mask>'     ,top_k=2   )

      self.assertEqual(
          nested_simplify(_a     ,decimals=6   )     ,[
              [
                  {
                      'score': 2.2E-05,
                      'token': 3_5676,
                      'token_str': ' Maul',
                      'sequence': '<s>My name is Maul<mask></s>',
                  },
                  {'score': 2.2E-05, 'token': 1_6416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
              ],
              [
                  {
                      'score': 2.2E-05,
                      'token': 3_5676,
                      'token_str': ' Maul',
                      'sequence': '<s>My name is<mask> Maul</s>',
                  },
                  {'score': 2.2E-05, 'token': 1_6416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
              ],
          ]     ,)

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

      _lowerCAmelCase							: Tuple	=     pipeline('fill-mask'     ,model='hf-internal-testing/tiny-random-distilbert'     ,device=0     ,framework='pt'   )

      # convert model to fp16
      pipe.model.half()

      _lowerCAmelCase							: List[str]	=     pipe('Paris is the [MASK] of France.'   )
      # We actually don't care about the result, we just want to make sure
      # it works, meaning the float16 tensor got casted back to float32
      # for postprocessing.
      self.assertIsInstance(_a     ,_a   )

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

      _lowerCAmelCase							: Union[str, Any]	=     pipeline(task='fill-mask'     ,model='distilroberta-base'     ,top_k=2     ,framework='pt'   )
      self.run_large_test(_a   )

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

      _lowerCAmelCase							: Dict	=     pipeline(task='fill-mask'     ,model='distilroberta-base'     ,top_k=2     ,framework='tf'   )
      self.run_large_test(_a   )

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

      _lowerCAmelCase							: Optional[Any]	=     unmasker('My name is <mask>'   )
      self.assertEqual(
          nested_simplify(_a   )     ,[
              {'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 610, 'token_str': ' John'},
              {'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 1573, 'token_str': ' Chris'},
          ]     ,)
      _lowerCAmelCase							: str	=     unmasker('The largest city in France is <mask>'   )
      self.assertEqual(
          nested_simplify(_a   )     ,[
              {
                  'sequence': 'The largest city in France is Paris',
                  'score': 0.2_5_1,
                  'token': 2201,
                  'token_str': ' Paris',
              },
              {
                  'sequence': 'The largest city in France is Lyon',
                  'score': 0.2_1_4,
                  'token': 1_2790,
                  'token_str': ' Lyon',
              },
          ]     ,)

      _lowerCAmelCase							: Any	=     unmasker('My name is <mask>'     ,targets=[' Patrick', ' Clara', ' Teven']     ,top_k=3   )
      self.assertEqual(
          nested_simplify(_a   )     ,[
              {'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 3499, 'token_str': ' Patrick'},
              {'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 1_3606, 'token_str': ' Clara'},
              {'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 2941, 'token_str': ' Te'},
          ]     ,)

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

      _lowerCAmelCase							: int	=     pipeline(task='fill-mask'     ,model='sshleifer/tiny-distilroberta-base'     ,framework='pt'   )
      _lowerCAmelCase							: int	=     None
      _lowerCAmelCase							: List[Any]	=     None
      self.run_pipeline_test(_a     ,[]   )

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

      _lowerCAmelCase							: List[str]	=     pipeline(task='fill-mask'     ,model='sshleifer/tiny-distilroberta-base'     ,framework='tf'   )
      _lowerCAmelCase							: int	=     None
      _lowerCAmelCase							: str	=     None
      self.run_pipeline_test(_a     ,[]   )

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

      if tokenizer is None or tokenizer.mask_token_id is None:
          self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'   )

      _lowerCAmelCase							: Optional[int]	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )
      _lowerCAmelCase							: str	=     [
          F"""This is another {tokenizer.mask_token} test""",
      ]
      return fill_masker, examples

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

      _lowerCAmelCase							: List[str]	=     fill_masker.tokenizer
      _lowerCAmelCase							: Optional[Any]	=     fill_masker.model

      _lowerCAmelCase							: Tuple	=     fill_masker(
          F"""This is a {tokenizer.mask_token}"""     ,)
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)

      _lowerCAmelCase							: List[str]	=     fill_masker([F"""This is a {tokenizer.mask_token}"""]   )
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)

      _lowerCAmelCase							: Optional[Any]	=     fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""]   )
      self.assertEqual(
          _a     ,[
              [
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              ],
              [
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              ],
          ]     ,)

      with self.assertRaises(_a   ):
          fill_masker([None]   )
      # No mask_token is not supported
      with self.assertRaises(_a   ):
          fill_masker('This is'   )

      self.run_test_top_k(_a     ,_a   )
      self.run_test_targets(_a     ,_a   )
      self.run_test_top_k_targets(_a     ,_a   )
      self.fill_mask_with_duplicate_targets_and_top_k(_a     ,_a   )
      self.fill_mask_with_multiple_masks(_a     ,_a   )

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

      _lowerCAmelCase							: Tuple	=     tokenizer.get_vocab()
      _lowerCAmelCase							: Optional[int]	=     sorted(vocab.keys()   )[:2]
      # Pipeline argument
      _lowerCAmelCase							: List[Any]	=     FillMaskPipeline(model=_a     ,tokenizer=_a     ,targets=_a   )
      _lowerCAmelCase							: Union[str, Any]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""   )
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)
      _lowerCAmelCase							: List[Any]	=     {vocab[el] for el in targets}
      self.assertEqual({el['token'] for el in outputs}     ,_a   )
      _lowerCAmelCase							: int	=     [tokenizer.decode([x]   ) for x in target_ids]
      self.assertEqual({el['token_str'] for el in outputs}     ,set(_a   )   )

      # Call argument
      _lowerCAmelCase							: List[Any]	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )
      _lowerCAmelCase							: Any	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=_a   )
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)
      _lowerCAmelCase							: Optional[Any]	=     {vocab[el] for el in targets}
      self.assertEqual({el['token'] for el in outputs}     ,_a   )
      _lowerCAmelCase							: List[Any]	=     [tokenizer.decode([x]   ) for x in target_ids]
      self.assertEqual({el['token_str'] for el in outputs}     ,set(_a   )   )

      # Score equivalence
      _lowerCAmelCase							: Optional[Any]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=_a   )
      _lowerCAmelCase							: Optional[int]	=     [top_mask["token_str"] for top_mask in outputs]
      _lowerCAmelCase							: Tuple	=     [top_mask["score"] for top_mask in outputs]

      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(_a   ) == set(_a   ):
          _lowerCAmelCase							: Dict	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=_a   )
          _lowerCAmelCase							: int	=     [top_mask["score"] for top_mask in unmasked_targets]
          self.assertEqual(nested_simplify(_a   )     ,nested_simplify(_a   )   )

      # Raises with invalid
      with self.assertRaises(_a   ):
          _lowerCAmelCase							: Union[str, Any]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=[]   )
      # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
      if "" not in tokenizer.get_vocab():
          with self.assertRaises(_a   ):
              _lowerCAmelCase							: str	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=['']   )
          with self.assertRaises(_a   ):
              _lowerCAmelCase							: List[str]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,targets=''   )

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

      _lowerCAmelCase							: Any	=     FillMaskPipeline(model=_a     ,tokenizer=_a     ,top_k=2   )
      _lowerCAmelCase							: Optional[Any]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""   )
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)

      _lowerCAmelCase							: str	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )
      _lowerCAmelCase							: str	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,top_k=2   )
      self.assertEqual(
          _a     ,[
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
          ]     ,)
      self.assertEqual(nested_simplify(_a   )     ,nested_simplify(_a   )   )

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

      _lowerCAmelCase							: Optional[int]	=     tokenizer.get_vocab()
      _lowerCAmelCase							: List[str]	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )

      # top_k=2, ntargets=3
      _lowerCAmelCase							: Optional[int]	=     sorted(vocab.keys()   )[:3]
      _lowerCAmelCase							: Dict	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,top_k=2     ,targets=_a   )

      # If we use the most probably targets, and filter differently, we should still
      # have the same results
      _lowerCAmelCase							: Tuple	=     [el["token_str"] for el in sorted(_a     ,key=lambda _A   : x["score"]     ,reverse=_a   )]
      # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
      if set(_a   ).issubset(_a   ):
          _lowerCAmelCase							: Optional[Any]	=     fill_masker(F"""This is a {tokenizer.mask_token}"""     ,top_k=3     ,targets=_a   )
          # They should yield exactly the same result
          self.assertEqual(nested_simplify(_a   )     ,nested_simplify(_a   )   )

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

      _lowerCAmelCase							: Any	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )
      _lowerCAmelCase							: Union[str, Any]	=     tokenizer.get_vocab()
      # String duplicates + id duplicates
      _lowerCAmelCase							: Tuple	=     sorted(vocab.keys()   )[:3]
      _lowerCAmelCase							: int	=     [targets[0], targets[1], targets[0], targets[2], targets[1]]
      _lowerCAmelCase							: Union[str, Any]	=     fill_masker(F"""My name is {tokenizer.mask_token}"""     ,targets=_a     ,top_k=10   )

      # The target list contains duplicates, so we can't output more
      # than them
      self.assertEqual(len(_a   )     ,3   )







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

      _lowerCAmelCase							: List[str]	=     FillMaskPipeline(model=_a     ,tokenizer=_a   )

      _lowerCAmelCase							: Any	=     fill_masker(
          F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""     ,top_k=2   )
      self.assertEqual(
          _a     ,[
              [
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              ],
              [
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              ],
              [
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
                  {'sequence': ANY(_a   ), 'score': ANY(_a   ), 'token': ANY(_a   ), 'token_str': ANY(_a   )},
              ],
          ]     ,)







"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

          lowercase       :							str									=				self.dummy_model()
          lowercase       :							Optional[int]									=				self.dummy_sample_deter
          lowercase       :							List[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Optional[Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							List[str]									=				pred_prev_sample

          lowercase       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

          lowercase       :							Optional[int]									=				self.dummy_model()
          lowercase       :							List[str]									=				self.dummy_sample_deter
          lowercase       :							Optional[Any]									=				torch.manual_seed(0							)

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							Optional[Any]									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 lowercase       :							Dict									=				pred_prev_sample

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

          for i, timestep in enumerate(_a							):
                 if i == len(_a							) - 1:
                        lowercase       :							Any									=				-1
                 else:
                        lowercase       :							Union[str, Any]									=				timesteps[i + 1]

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

          with self.assertRaises(_a      ,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."							):
                 scheduler.set_timesteps(num_inference_steps=_a      ,       timesteps=_a							)

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _a      ,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"      ,       ):
                 scheduler.set_timesteps(timesteps=_a							)


