

'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase						:  Optional[Any]							       =				get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class 				_snake_case					(   a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       str				=     XLMRobertaTokenizer
  SCREAMING_SNAKE_CASE			:       str				=     XLMRobertaTokenizerFast
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     True
  SCREAMING_SNAKE_CASE			:       int				=     True

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase 						=							XLMRobertaTokenizer(_SCREAMING_SNAKE_CASE      , keep_accents=_SCREAMING_SNAKE_CASE	)
      tokenizer.save_pretrained(self.tmpdirname	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							'<pad>'
      lowerCAmelCase 						=							1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE	)      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							list(self.get_tokenizer().get_vocab().keys()	)

      self.assertEqual(vocab_keys[0]      , '<s>'	)
      self.assertEqual(vocab_keys[1]      , '<pad>'	)
      self.assertEqual(vocab_keys[-1]      , '<mask>'	)
      self.assertEqual(len(_SCREAMING_SNAKE_CASE	)      , 10_02	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertEqual(self.get_tokenizer().vocab_size      , 10_02	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							XLMRobertaTokenizer(_SCREAMING_SNAKE_CASE      , keep_accents=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tokenizer.tokenize('This is a test'	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , ['▁This', '▁is', '▁a', '▁t', 'est']	)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)      , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      , )

      lowerCAmelCase 						=							tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , [
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]      , )
      lowerCAmelCase 						=							tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]      , )

      lowerCAmelCase 						=							tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(
          _SCREAMING_SNAKE_CASE      , [
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

      lowerCAmelCase 						=							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
              lowerCAmelCase 						=							self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

              lowerCAmelCase 						=							tempfile.mkdtemp()

              lowerCAmelCase 						=							tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE	)

              # Checks it save with the same files + the tokenizer.json file for the fast one
              self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files	)	)
              lowerCAmelCase 						=							tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f	)
              self.assertSequenceEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

              # Checks everything loads correctly in the same way
              lowerCAmelCase 						=							tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE	)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

              shutil.rmtree(_SCREAMING_SNAKE_CASE	)

              # Save tokenizer rust, legacy_format=True
              lowerCAmelCase 						=							tempfile.mkdtemp()

              lowerCAmelCase 						=							tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE      , legacy_format=_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE	)

              # Checks it save with the same files
              self.assertSequenceEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

              # Checks everything loads correctly in the same way
              lowerCAmelCase 						=							tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE	)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)

              shutil.rmtree(_SCREAMING_SNAKE_CASE	)

              # Save tokenizer rust, legacy_format=False
              lowerCAmelCase 						=							tempfile.mkdtemp()

              lowerCAmelCase 						=							tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE      , legacy_format=_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE	)

              # Checks it saved the tokenizer.json file
              self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files	)	)

              # Checks everything loads correctly in the same way
              lowerCAmelCase 						=							tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE	)
              lowerCAmelCase 						=							tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE	)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)	)

              shutil.rmtree(_SCREAMING_SNAKE_CASE	)

  @cached_property
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      with tempfile.NamedTemporaryFile() as f:
          shutil.copyfile(_SCREAMING_SNAKE_CASE      , f.name	)
          lowerCAmelCase 						=							XLMRobertaTokenizer(f.name      , keep_accents=_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							pickle.dumps(_SCREAMING_SNAKE_CASE	)
      pickle.loads(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if not self.test_rust_tokenizer:
          return

      lowerCAmelCase 						=							self.get_tokenizer()
      lowerCAmelCase 						=							self.get_rust_tokenizer()

      lowerCAmelCase 						=							'I was born in 92000, and this is falsé.'

      lowerCAmelCase 						=							tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tokenizer.encode(_SCREAMING_SNAKE_CASE      , add_special_tokens=_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							rust_tokenizer.encode(_SCREAMING_SNAKE_CASE      , add_special_tokens=_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.get_rust_tokenizer()
      lowerCAmelCase 						=							tokenizer.encode(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							rust_tokenizer.encode(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							'Hello World!'
      lowerCAmelCase 						=							[0, 3_53_78, 66_61, 38, 2]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(_SCREAMING_SNAKE_CASE      , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE	)	)

  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							(
          'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
          ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
      )
      lowerCAmelCase 						=							[
          0,
          32_93,
          83,
          10,
          45_52,
          49_89,
          79_86,
          6_78,
          10,
          59_15,
          1_11,
          17_94_59,
          12_48_50,
          4,
          60_44,
          2_37,
          12,
          6,
          5,
          6,
          4,
          67_80,
          7_05,
          15,
          13_88,
          44,
          3_78,
          1_01_14,
          7_11,
          1_52,
          20,
          6,
          5,
          2_23_76,
          6_42,
          12_21,
          1_51_90,
          3_41_53,
          4_50,
          56_08,
          9_59,
          11_19,
          5_77_02,
          1_36,
          1_86,
          47,
          10_98,
          2_93_67,
          47,
          # 4426, # What fairseq tokenizes from "<unk>": "_<"
          # 3678, # What fairseq tokenizes from "<unk>": "unk"
          # 2740, # What fairseq tokenizes from "<unk>": ">"
          3,  # What we tokenize from "<unk>": "<unk>"
          6,  # Residue from the tokenization: an extra sentencepiece underline
          4,
          60_44,
          2_37,
          62_84,
          5_09_01,
          5_28,
          31,
          90,
          34,
          9_27,
          2,
      ]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(_SCREAMING_SNAKE_CASE      , self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE	)	)


  @slow
  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_SCREAMING_SNAKE_CASE      , model_name='xlm-roberta-base'      , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'      , )








'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (DDPMParallelScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							self.dummy_sample_deter + 0.1
      lowerCAmelCase 						=							self.dummy_sample_deter - 0.1

      lowerCAmelCase 						=							samplea.shape[0]
      lowerCAmelCase 						=							torch.stack([samplea, samplea, samplea]      , dim=0	)
      lowerCAmelCase 						=							torch.arange(_SCREAMING_SNAKE_CASE	)[0:3, None].repeat(1      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(samples.flatten(0      , 1	)      , timesteps.flatten(0      , 1	)	)
      lowerCAmelCase 						=							scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE      , timesteps.flatten(0      , 1	)      , samples.flatten(0      , 1	)	)

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
      assert abs(result_mean.item() - 0.5_005	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='`custom_timesteps` must be in descending order.'	):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

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





