





'''simple docstring'''




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase            =       get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__UpperCAmelCase            =       256047
__UpperCAmelCase            =       256145




@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	NllbTokenizer
					SCREAMING_SNAKE_CASE__				=	NllbTokenizerFast
					SCREAMING_SNAKE_CASE__				=	True
					SCREAMING_SNAKE_CASE__				=	True
					SCREAMING_SNAKE_CASE__				=	{}


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							SCREAMING_SNAKE_CASE :				List[Any]		=			NllbTokenizer(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)
							tokenizer.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			NllbTokenizer(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer.tokenize("""This is a test"""						)
							self.assertListEqual(lowerCamelCase_      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

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

							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[
							        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""",
							        """.""",
							    ]      ,						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer.convert_tokens_to_ids(lowerCamelCase_						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[
							        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]
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer.convert_ids_to_tokens(lowerCamelCase_						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[
							        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 lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
											SCREAMING_SNAKE_CASE :				Any		=			self.rust_tokenizer_class.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Any		=			self.tokenizer_class.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

											SCREAMING_SNAKE_CASE :				Optional[Any]		=			tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer_r.save_pretrained(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer_p.save_pretrained(lowerCamelCase_						)

											# 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						)						)
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f						)
											self.assertSequenceEqual(lowerCamelCase_      ,						lowerCamelCase_						)

											# Checks everything loads correctly in the same way
											SCREAMING_SNAKE_CASE :				Any		=			tokenizer_r.from_pretrained(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Any		=			tokenizer_p.from_pretrained(lowerCamelCase_						)

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

											shutil.rmtree(lowerCamelCase_						)

											# Save tokenizer rust, legacy_format=True
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE :				str		=			tokenizer_r.save_pretrained(lowerCamelCase_      ,						legacy_format=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				int		=			tokenizer_p.save_pretrained(lowerCamelCase_						)

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

											# Checks everything loads correctly in the same way
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer_r.from_pretrained(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer_p.from_pretrained(lowerCamelCase_						)

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

											shutil.rmtree(lowerCamelCase_						)

											# Save tokenizer rust, legacy_format=False
											SCREAMING_SNAKE_CASE :				str		=			tempfile.mkdtemp()

											SCREAMING_SNAKE_CASE :				Any		=			tokenizer_r.save_pretrained(lowerCamelCase_      ,						legacy_format=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer_p.save_pretrained(lowerCamelCase_						)

											# 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
											SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer_r.from_pretrained(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer_p.from_pretrained(lowerCamelCase_						)

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

											shutil.rmtree(lowerCamelCase_						)


					@require_torch
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							if not self.test_seqaseq:
									return

							SCREAMING_SNAKE_CASE :				Any		=			self.get_tokenizers()
							for tokenizer in tokenizers:
									with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
											# Longer text that will definitely require truncation.
											SCREAMING_SNAKE_CASE :				List[str]		=			[
											    """ UN Chief Says There Is No Military Solution in Syria""",
											    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
											    """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
											    """ will only worsen the violence and misery for millions of people.""",
											]
											SCREAMING_SNAKE_CASE :				List[Any]		=			[
											    """Şeful ONU declară că nu există o soluţie militară în Siria""",
											    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
											    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
											    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
											]
											try:
													SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer.prepare_seqaseq_batch(
													    src_texts=lowerCamelCase_      ,						tgt_texts=lowerCamelCase_      ,						max_length=3      ,						max_target_length=10      ,						return_tensors="""pt"""      ,						src_lang="""eng_Latn"""      ,						tgt_lang="""ron_Latn"""      ,						)
											except NotImplementedError:
													return
											self.assertEqual(batch.input_ids.shape[1]      ,						3						)
											self.assertEqual(batch.labels.shape[1]      ,						10						)
											# max_target_length will default to max_length if not specified
											SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer.prepare_seqaseq_batch(
											    lowerCamelCase_      ,						tgt_texts=lowerCamelCase_      ,						max_length=3      ,						return_tensors="""pt"""						)
											self.assertEqual(batch.input_ids.shape[1]      ,						3						)
											self.assertEqual(batch.labels.shape[1]      ,						3						)

											SCREAMING_SNAKE_CASE :				int		=			tokenizer.prepare_seqaseq_batch(
											    src_texts=lowerCamelCase_      ,						max_length=3      ,						max_target_length=10      ,						return_tensors="""pt"""						)
											self.assertEqual(batch_encoder_only.input_ids.shape[1]      ,						3						)
											self.assertEqual(batch_encoder_only.attention_mask.shape[1]      ,						3						)
											self.assertNotIn("""decoder_input_ids"""      ,						lowerCamelCase_						)


					@unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""						)
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
											SCREAMING_SNAKE_CASE :				List[str]		=			[AddedToken("""<special>"""      ,						lstrip=lowerCamelCase_						)]

											SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						additional_special_tokens=lowerCamelCase_      ,						**lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer_r.encode("""Hey this is a <special> token"""						)

											SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer_r.encode("""<special>"""      ,						add_special_tokens=lowerCamelCase_						)[0]

											self.assertTrue(special_token_id in r_output						)

											if self.test_slow_tokenizer:
													SCREAMING_SNAKE_CASE :				Optional[int]		=			self.rust_tokenizer_class.from_pretrained(
													    lowerCamelCase_      ,						additional_special_tokens=lowerCamelCase_      ,						**lowerCamelCase_      ,						)
													SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer_class.from_pretrained(
													    lowerCamelCase_      ,						additional_special_tokens=lowerCamelCase_      ,						**lowerCamelCase_						)

													SCREAMING_SNAKE_CASE :				str		=			tokenizer_p.encode("""Hey this is a <special> token"""						)

													SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer_cr.encode("""Hey this is a <special> token"""						)

													self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)
													self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)
													self.assertTrue(special_token_id in p_output						)
													self.assertTrue(special_token_id in cr_output						)







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




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''facebook/nllb-200-distilled-600M'''
					SCREAMING_SNAKE_CASE__				=	[
					    ''' UN Chief Says There Is No Military Solution in Syria''',
					    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
					]
					SCREAMING_SNAKE_CASE__				=	[
					    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
					    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
					    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
					    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
					]
					SCREAMING_SNAKE_CASE__				=	[
					    25_6047,
					    1_6297,
					    13_4408,
					    8165,
					    24_8066,
					    1_4734,
					    950,
					    1135,
					    10_5721,
					    3573,
					    83,
					    2_7352,
					    108,
					    4_9486,
					    2,
					]


					@classmethod
					def lowerCamelCase_			(						cls				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				NllbTokenizer		=			NllbTokenizer.from_pretrained(
							    cls.checkpoint_name      ,						src_lang="""eng_Latn"""      ,						tgt_lang="""ron_Latn"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1
							return cls


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]      ,						25_60_01						)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]      ,						25_60_02						)
							self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]      ,						25_60_57						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
							self.assertListEqual(self.expected_src_tokens      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							self.assertIn(lowerCamelCase_      ,						self.tokenizer.all_special_ids						)
							# fmt: off
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
							# fmt: on

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.tokenizer.decode(lowerCamelCase_      ,						skip_special_tokens=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.tokenizer.decode(generated_ids[1:]      ,						skip_special_tokens=lowerCamelCase_						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)
							self.assertNotIn(self.tokenizer.eos_token      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			["""this is gunna be a long sentence """ * 20]
							assert isinstance(src_text[0]      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			10
							SCREAMING_SNAKE_CASE :				List[str]		=			self.tokenizer(lowerCamelCase_      ,						max_length=lowerCamelCase_      ,						truncation=lowerCamelCase_						).input_ids[0]
							self.assertEqual(ids[-1]      ,						2						)
							self.assertEqual(ids[0]      ,						lowerCamelCase_						)
							self.assertEqual(len(lowerCamelCase_						)      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]						)      ,						[25_62_03, 3]						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.tokenizer.fairseq_tokens_to_ids
							self.tokenizer.save_pretrained(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			NllbTokenizer.from_pretrained(lowerCamelCase_						)
							self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,						lowerCamelCase_						)


					@require_torch
					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.tokenizer(
							    self.src_text      ,						text_target=self.tgt_text      ,						padding=lowerCamelCase_      ,						truncation=lowerCamelCase_      ,						max_length=len(self.expected_src_tokens						)      ,						return_tensors="""pt"""      ,						)
							SCREAMING_SNAKE_CASE :				Tuple		=			shift_tokens_right(
							    batch["""labels"""]      ,						self.tokenizer.pad_token_id      ,						self.tokenizer.lang_code_to_id["""ron_Latn"""]						)

							self.assertIsInstance(lowerCamelCase_      ,						lowerCamelCase_						)

							self.assertEqual((2, 15)      ,						batch.input_ids.shape						)
							self.assertEqual((2, 15)      ,						batch.attention_mask.shape						)
							SCREAMING_SNAKE_CASE :				List[str]		=			batch.input_ids.tolist()[0]
							self.assertListEqual(self.expected_src_tokens      ,						lowerCamelCase_						)
							self.assertEqual(lowerCamelCase_      ,						batch.decoder_input_ids[0, 0]						)  # EOS
							# Test that special tokens are reset
							self.assertEqual(self.tokenizer.prefix_tokens      ,						[EN_CODE]						)
							self.assertEqual(self.tokenizer.suffix_tokens      ,						[self.tokenizer.eos_token_id]						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.tokenizer(self.src_text      ,						padding=lowerCamelCase_      ,						truncation=lowerCamelCase_      ,						max_length=3      ,						return_tensors="""pt"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.tokenizer(
							    text_target=self.tgt_text      ,						padding=lowerCamelCase_      ,						truncation=lowerCamelCase_      ,						max_length=10      ,						return_tensors="""pt"""						)
							SCREAMING_SNAKE_CASE :				int		=			targets["""input_ids"""]
							SCREAMING_SNAKE_CASE :				List[Any]		=			shift_tokens_right(
							    lowerCamelCase_      ,						self.tokenizer.pad_token_id      ,						decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]      ,						)

							self.assertEqual(batch.input_ids.shape[1]      ,						3						)
							self.assertEqual(batch.decoder_input_ids.shape[1]      ,						10						)


					@require_torch
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.tokenizer._build_translation_inputs(
							    """A test"""      ,						return_tensors="""pt"""      ,						src_lang="""eng_Latn"""      ,						tgt_lang="""fra_Latn"""						)

							self.assertEqual(
							    nested_simplify(lowerCamelCase_						)      ,						{
							        # A, test, EOS, en_XX
							        """input_ids""": [[25_60_47, 70, 73_56, 2]],
							        """attention_mask""": [[1, 1, 1, 1]],
							        # ar_AR
							        """forced_bos_token_id""": 25_60_57,
							    }      ,						)



					@require_torch
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			True
							SCREAMING_SNAKE_CASE :				int		=			self.tokenizer(
							    """UN Chief says there is no military solution in Syria"""      ,						src_lang="""eng_Latn"""      ,						tgt_lang="""fra_Latn"""						)
							self.assertEqual(
							    inputs.input_ids      ,						[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]						)

							SCREAMING_SNAKE_CASE :				List[str]		=			False
							SCREAMING_SNAKE_CASE :				str		=			self.tokenizer(
							    """UN Chief says there is no military solution in Syria"""      ,						src_lang="""eng_Latn"""      ,						tgt_lang="""fra_Latn"""						)
							self.assertEqual(
							    inputs.input_ids      ,						[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]						)










'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

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

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



