


'''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


__snake_case											=					get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class        lowercase     (							A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    XLMRobertaTokenizer
		_a   			=    XLMRobertaTokenizerFast
		_a   			=    True
		_a   			=    True




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							super().setUp()

							# We have a SentencePiece fixture for testing
							UpperCamelCase__       :Dict							 =			XLMRobertaTokenizer(UpperCamelCase_   ,       keep_accents=UpperCamelCase_							)
							tokenizer.save_pretrained(self.tmpdirname							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			'''<pad>'''
							UpperCamelCase__       :Optional[int]							 =			1

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			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(UpperCamelCase_							)   ,       1002							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.assertEqual(self.get_tokenizer().vocab_size   ,       1002							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			XLMRobertaTokenizer(UpperCamelCase_   ,       keep_accents=UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			tokenizer.tokenize('''This is a test'''							)
							self.assertListEqual(UpperCamelCase_   ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(UpperCamelCase_							)   ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,       )

							UpperCamelCase__       :Optional[int]							 =			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
							self.assertListEqual(
							    UpperCamelCase_   ,       [
							        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''',
							        '''.''',
							    ]   ,       )
							UpperCamelCase__       :Optional[int]							 =			tokenizer.convert_tokens_to_ids(UpperCamelCase_							)
							self.assertListEqual(
							    UpperCamelCase_   ,       [
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]   ,       )

							UpperCamelCase__       :Any							 =			tokenizer.convert_ids_to_tokens(UpperCamelCase_							)
							self.assertListEqual(
							    UpperCamelCase_   ,       [
							        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							):

							'''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

							UpperCamelCase__       :int							 =			(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})'''							):
																	UpperCamelCase__       :Union[str, Any]							 =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase_   ,       **UpperCamelCase_							)
																	UpperCamelCase__       :str							 =			self.tokenizer_class.from_pretrained(UpperCamelCase_   ,       **UpperCamelCase_							)

																	UpperCamelCase__       :List[Any]							 =			tempfile.mkdtemp()

																	UpperCamelCase__       :List[Any]							 =			tokenizer_r.save_pretrained(UpperCamelCase_							)
																	UpperCamelCase__       :List[Any]							 =			tokenizer_p.save_pretrained(UpperCamelCase_							)

																	# 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							)							)
																	UpperCamelCase__       :Dict							 =			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f							)
																	self.assertSequenceEqual(UpperCamelCase_   ,       UpperCamelCase_							)

																	# Checks everything loads correctly in the same way
																	UpperCamelCase__       :str							 =			tokenizer_r.from_pretrained(UpperCamelCase_							)
																	UpperCamelCase__       :int							 =			tokenizer_p.from_pretrained(UpperCamelCase_							)

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

																	shutil.rmtree(UpperCamelCase_							)

																	# Save tokenizer rust, legacy_format=True
																	UpperCamelCase__       :Optional[int]							 =			tempfile.mkdtemp()

																	UpperCamelCase__       :Dict							 =			tokenizer_r.save_pretrained(UpperCamelCase_   ,       legacy_format=UpperCamelCase_							)
																	UpperCamelCase__       :List[str]							 =			tokenizer_p.save_pretrained(UpperCamelCase_							)

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

																	# Checks everything loads correctly in the same way
																	UpperCamelCase__       :List[Any]							 =			tokenizer_r.from_pretrained(UpperCamelCase_							)
																	UpperCamelCase__       :Tuple							 =			tokenizer_p.from_pretrained(UpperCamelCase_							)

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

																	shutil.rmtree(UpperCamelCase_							)

																	# Save tokenizer rust, legacy_format=False
																	UpperCamelCase__       :Dict							 =			tempfile.mkdtemp()

																	UpperCamelCase__       :Dict							 =			tokenizer_r.save_pretrained(UpperCamelCase_   ,       legacy_format=UpperCamelCase_							)
																	UpperCamelCase__       :Optional[int]							 =			tokenizer_p.save_pretrained(UpperCamelCase_							)

																	# 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
																	UpperCamelCase__       :Optional[Any]							 =			tokenizer_r.from_pretrained(UpperCamelCase_							)
																	UpperCamelCase__       :Dict							 =			tokenizer_p.from_pretrained(UpperCamelCase_							)

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

																	shutil.rmtree(UpperCamelCase_							)




		@cached_property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							with tempfile.NamedTemporaryFile() as f:
												shutil.copyfile(UpperCamelCase_   ,       f.name							)
												UpperCamelCase__       :str							 =			XLMRobertaTokenizer(f.name   ,       keep_accents=UpperCamelCase_							)
												UpperCamelCase__       :Optional[Any]							 =			pickle.dumps(UpperCamelCase_							)
							pickle.loads(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if not self.test_rust_tokenizer:
												return

							UpperCamelCase__       :str							 =			self.get_tokenizer()
							UpperCamelCase__       :str							 =			self.get_rust_tokenizer()

							UpperCamelCase__       :int							 =			'''I was born in 92000, and this is falsé.'''

							UpperCamelCase__       :Tuple							 =			tokenizer.tokenize(UpperCamelCase_							)
							UpperCamelCase__       :str							 =			rust_tokenizer.tokenize(UpperCamelCase_							)
							self.assertListEqual(UpperCamelCase_   ,       UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			tokenizer.encode(UpperCamelCase_   ,       add_special_tokens=UpperCamelCase_							)
							UpperCamelCase__       :Any							 =			rust_tokenizer.encode(UpperCamelCase_   ,       add_special_tokens=UpperCamelCase_							)
							self.assertListEqual(UpperCamelCase_   ,       UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			self.get_rust_tokenizer()
							UpperCamelCase__       :Union[str, Any]							 =			tokenizer.encode(UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			rust_tokenizer.encode(UpperCamelCase_							)
							self.assertListEqual(UpperCamelCase_   ,       UpperCamelCase_							)




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			'''Hello World!'''
							UpperCamelCase__       :Union[str, Any]							 =			[0, 35378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			(
							    '''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'''
							)
							UpperCamelCase__       :int							 =			[
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    179459,
							    124850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    10114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    22376,
							    642,
							    1221,
							    15190,
							    34153,
							    450,
							    5608,
							    959,
							    1119,
							    57702,
							    136,
							    186,
							    47,
							    1098,
							    29367,
							    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,
							    6044,
							    237,
							    6284,
							    50901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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




		@slow
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			{'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=UpperCamelCase_   ,       model_name='''xlm-roberta-base'''   ,       revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''   ,       )



'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3