





'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




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




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	LongformerTokenizer
					SCREAMING_SNAKE_CASE__				=	True
					SCREAMING_SNAKE_CASE__				=	LongformerTokenizerFast
					SCREAMING_SNAKE_CASE__				=	True


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							SCREAMING_SNAKE_CASE :				str		=			[
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							]
							SCREAMING_SNAKE_CASE :				Dict		=			dict(zip(lowerCamelCase_      ,						range(len(lowerCamelCase_						)						)						)						)
							SCREAMING_SNAKE_CASE :				List[str]		=			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							SCREAMING_SNAKE_CASE :				List[Any]		=			{"""unk_token""": """<unk>"""}

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""vocab_file"""]						)
							SCREAMING_SNAKE_CASE :				Tuple		=			os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""merges_file"""]						)
							with open(self.vocab_file      ,						"""w"""      ,						encoding="""utf-8"""						) as fp:
									fp.write(json.dumps(lowerCamelCase_						) + """\n"""						)
							with open(self.merges_file      ,						"""w"""      ,						encoding="""utf-8"""						) as fp:
									fp.write("""\n""".join(lowerCamelCase_						)						)


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							kwargs.update(self.special_tokens_map						)
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Dict      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							kwargs.update(self.special_tokens_map						)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			"""lower newer"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""lower newer"""
							return input_text, output_text


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.tokenizer_class(self.vocab_file      ,						self.merges_file      ,						**self.special_tokens_map						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			"""lower newer"""
							SCREAMING_SNAKE_CASE :				Tuple		=			["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer.tokenize(lowerCamelCase_						)  # , add_prefix_space=True)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokens + [tokenizer.unk_token]
							SCREAMING_SNAKE_CASE :				Tuple		=			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_						)      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("""Hello world!"""      ,						add_special_tokens=lowerCamelCase_						)      ,						[0, 3_14_14, 2_32, 3_28, 2]						)
							self.assertListEqual(
							    tokenizer.encode("""Hello world! cécé herlolip 418"""      ,						add_special_tokens=lowerCamelCase_						)      ,						[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]      ,						)


					@slow
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""						)

							SCREAMING_SNAKE_CASE :				Any		=			tokenizer.encode("""sequence builders"""      ,						add_special_tokens=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer.encode("""multi-sequence build"""      ,						add_special_tokens=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.encode(
							    """sequence builders"""      ,						add_special_tokens=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			tokenizer.encode(
							    """sequence builders"""      ,						"""multi-sequence build"""      ,						add_special_tokens=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      ,						lowerCamelCase_						)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			self.get_tokenizer()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			"""Encode this sequence."""
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer.byte_encoder[""" """.encode("""utf-8"""						)[0]]

							# Testing encoder arguments
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertNotEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							tokenizer.add_special_tokens({"""bos_token""": """<s>"""}						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
							self.assertNotEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							# Testing spaces after special tokens
							SCREAMING_SNAKE_CASE :				Any		=			"""<mask>"""
							tokenizer.add_special_tokens(
							    {"""mask_token""": AddedToken(lowerCamelCase_      ,						lstrip=lowerCamelCase_      ,						rstrip=lowerCamelCase_						)}						)  # mask token has a left space
							SCREAMING_SNAKE_CASE :				Dict		=			tokenizer.convert_tokens_to_ids(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			"""Encode <mask> sequence"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			"""Encode <mask>sequence"""

							SCREAMING_SNAKE_CASE :				str		=			tokenizer.encode(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			encoded.index(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.encode(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			encoded.index(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
							self.assertNotEqual(lowerCamelCase_      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
											SCREAMING_SNAKE_CASE :				Dict		=			self.rust_tokenizer_class.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Any		=			self.tokenizer_class.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Dict		=			"""A, <mask> AllenNLP sentence."""
											SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer_r.encode_plus(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						return_token_type_ids=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer_p.encode_plus(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_      ,						return_token_type_ids=lowerCamelCase_						)

											# token_type_ids should put 0 everywhere
											self.assertEqual(sum(tokens_r["""token_type_ids"""]						)      ,						sum(tokens_p["""token_type_ids"""]						)						)

											# attention_mask should put 1 everywhere, so sum over length should be 1
											self.assertEqual(
											    sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						)      ,						sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						)      ,						)

											SCREAMING_SNAKE_CASE :				int		=			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
											SCREAMING_SNAKE_CASE :				str		=			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)

											# Rust correctly handles the space before the mask while python doesnt
											self.assertSequenceEqual(tokens_p["""input_ids"""]      ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)
											self.assertSequenceEqual(tokens_r["""input_ids"""]      ,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)

											self.assertSequenceEqual(
											    lowerCamelCase_      ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
											self.assertSequenceEqual(
											    lowerCamelCase_      ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							for trim_offsets, add_prefix_space in itertools.product([True, False]      ,						repeat=2						):
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)

									SCREAMING_SNAKE_CASE :				List[str]		=			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
									SCREAMING_SNAKE_CASE :				Tuple		=			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

									self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]      ,						lowerCamelCase_						)

									self.assertEqual(post_processor_state["""add_prefix_space"""]      ,						lowerCamelCase_						)
									self.assertEqual(post_processor_state["""trim_offsets"""]      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
											SCREAMING_SNAKE_CASE :				Any		=			"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
											SCREAMING_SNAKE_CASE :				Any		=			f'''{text_of_1_token} {text_of_1_token}'''

											SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Any		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(len(lowerCamelCase_						) + 1, len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				List[Any]		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(len(lowerCamelCase_						) + 1, len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				int		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(len(lowerCamelCase_						), len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				Dict		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Any		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(len(lowerCamelCase_						), len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				Optional[Any]		=			f''' {text}'''

											# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
											#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
											# )
											# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
											# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
											# self.assertEqual(
											#     encoding.offset_mapping[1],
											#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
											# )

											SCREAMING_SNAKE_CASE :				str		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				int		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(1, 1 + len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(1 + len(lowerCamelCase_						) + 1, 1 + len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				List[str]		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Dict		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, 1 + len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(1 + len(lowerCamelCase_						), 1 + len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)

											SCREAMING_SNAKE_CASE :				Tuple		=			self.rust_tokenizer_class.from_pretrained(
											    lowerCamelCase_      ,						use_fast=lowerCamelCase_      ,						add_prefix_space=lowerCamelCase_      ,						trim_offsets=lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer_r(lowerCamelCase_      ,						return_offsets_mapping=lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
											self.assertEqual(encoding.offset_mapping[0]      ,						(0, 1 + len(lowerCamelCase_						))						)
											self.assertEqual(
											    encoding.offset_mapping[1]      ,						(1 + len(lowerCamelCase_						), 1 + len(lowerCamelCase_						) + 1 + len(lowerCamelCase_						))      ,						)










'''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



