



'''simple docstring'''

import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
	from test_module.custom_tokenization_fast import CustomTokenizerFast






class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						int			)			->					List[Any]:
										snake_case:    Any      							=0


							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
													snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(a_			)
													self.assertIsNotNone(a_			)
													self.assertIsInstance(a_      ,						(BertTokenizer, BertTokenizerFast)			)
													self.assertGreater(len(a_			)      ,						0			)

										for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
													snake_case:    List[str]      							=AutoTokenizer.from_pretrained(a_			)
													self.assertIsNotNone(a_			)
													self.assertIsInstance(a_      ,						(GPTaTokenizer, GPTaTokenizerFast)			)
													self.assertGreater(len(a_			)      ,						0			)


							def    UpperCamelCase     (				self   :						List[Any]			)			->					Optional[int]:
										snake_case:    Any      							=AutoTokenizer.from_pretrained(a_			)
										self.assertIsInstance(a_      ,						(BertTokenizer, BertTokenizerFast)			)
										self.assertEqual(tokenizer.vocab_size      ,						1_2			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(a_			)
										self.assertIsInstance(a_      ,						(RobertaTokenizer, RobertaTokenizerFast)			)
										self.assertEqual(tokenizer.vocab_size      ,						2_0			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					int:
										snake_case:    int      							=AutoConfig.from_pretrained(a_			)
										self.assertIsInstance(a_      ,						a_			)
										# Check that tokenizer_type ≠ model_type
										snake_case:    Union[str, Any]      							=AutoTokenizer.from_pretrained(a_      ,						config=a_			)
										self.assertIsInstance(a_      ,						(BertTokenizer, BertTokenizerFast)			)
										self.assertEqual(tokenizer.vocab_size      ,						1_2			)


							def    UpperCamelCase     (				self   :						Dict			)			->					List[str]:
										with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('./tests/fixtures/vocab.txt'      ,						os.path.join(a_      ,						'vocab.txt'			)			)

													snake_case:    Any      							=AutoTokenizer.from_pretrained(a_      ,						tokenizer_type='bert'      ,						use_fast=a_			)
													self.assertIsInstance(a_      ,						a_			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('./tests/fixtures/vocab.json'      ,						os.path.join(a_      ,						'vocab.json'			)			)
													shutil.copy('./tests/fixtures/merges.txt'      ,						os.path.join(a_      ,						'merges.txt'			)			)

													snake_case:    Any      							=AutoTokenizer.from_pretrained(a_      ,						tokenizer_type='gpt2'      ,						use_fast=a_			)
													self.assertIsInstance(a_      ,						a_			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('./tests/fixtures/vocab.txt'      ,						os.path.join(a_      ,						'vocab.txt'			)			)

													snake_case:    int      							=AutoTokenizer.from_pretrained(a_      ,						tokenizer_type='bert'			)
													self.assertIsInstance(a_      ,						a_			)

										with tempfile.TemporaryDirectory() as tmp_dir:
													shutil.copy('./tests/fixtures/vocab.json'      ,						os.path.join(a_      ,						'vocab.json'			)			)
													shutil.copy('./tests/fixtures/merges.txt'      ,						os.path.join(a_      ,						'merges.txt'			)			)

													snake_case:    Optional[int]      							=AutoTokenizer.from_pretrained(a_      ,						tokenizer_type='gpt2'			)
													self.assertIsInstance(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Any			)			->					Any:
										with pytest.raises(a_			):
													AutoTokenizer.from_pretrained('./'      ,						tokenizer_type='xxx'			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
													snake_case:    Optional[int]      							=tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased'			)
													self.assertIsInstance(a_      ,						(BertTokenizer, BertTokenizerFast)			)

													if isinstance(a_      ,						a_			):
																self.assertEqual(tokenizer.basic_tokenizer.do_lower_case      ,						a_			)
													else:
																self.assertEqual(tokenizer.do_lower_case      ,						a_			)

													self.assertEqual(tokenizer.model_max_length      ,						5_1_2			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[int]:
										for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
													with self.assertRaisesRegex(
													    a_      ,						'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'      ,						):
																snake_case:    Union[str, Any]      							=tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Any:
										# tests: https://github.com/huggingface/transformers/pull/13251
										# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
										# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
										snake_case:    int      							=TOKENIZER_MAPPING.values()
										snake_case:    str      							=[]

										for slow_tok, fast_tok in tokenizers:
													if slow_tok is not None:
																tokenizer_names.append(slow_tok.__name__			)

													if fast_tok is not None:
																tokenizer_names.append(fast_tok.__name__			)

										for tokenizer_name in tokenizer_names:
													# must find the right class
													tokenizer_class_from_name(a_			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Optional[int]:
										self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'      ,						use_fast=a_			)      ,						a_			)
										self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'			)      ,						a_			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    Union[str, Any]      							=AutoTokenizer.from_pretrained('distilbert-base-uncased'      ,						do_lower_case=a_			)
										snake_case:    str      							='Hello, world. How are you?'
										snake_case:    Union[str, Any]      							=tokenizer.tokenize(a_			)
										self.assertEqual('[UNK]'      ,						tokens[0]			)

										snake_case:    Any      							=AutoTokenizer.from_pretrained('microsoft/mpnet-base'      ,						do_lower_case=a_			)
										snake_case:    Any      							=tokenizer.tokenize(a_			)
										self.assertEqual('[UNK]'      ,						tokens[0]			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						Any			)			->					str:
										snake_case:    Any      							=AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config'			)
										self.assertEqual(type(a_			)      ,						a_			)
										self.assertEqual(tokenizer.model_max_length      ,						5_1_2			)
										self.assertEqual(tokenizer.vocab_size      ,						3_0_0_0_0			)
										self.assertEqual(tokenizer.unk_token      ,						'[UNK]'			)
										self.assertEqual(tokenizer.padding_side      ,						'right'			)
										self.assertEqual(tokenizer.truncation_side      ,						'right'			)


							def    UpperCamelCase     (				self   :						Any			)			->					str:
										snake_case:    List[str]      							=AutoTokenizer.from_pretrained(a_			)
										self.assertIsInstance(a_      ,						(BertTokenizer, BertTokenizerFast)			)
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(a_			)
													snake_case:    Optional[int]      							=AutoTokenizer.from_pretrained(a_			)

										self.assertIsInstance(a_      ,						tokenizer.__class__			)
										self.assertEqual(tokenizera.vocab_size      ,						1_2			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Optional[Any]:
										snake_case:    int      							=AutoTokenizer.from_pretrained('ctrl'			)
										# There is no fast CTRL so this always gives us a slow tokenizer.
										self.assertIsInstance(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										# Check we can load the tokenizer config of an online model.
										snake_case:    Tuple      							=get_tokenizer_config('bert-base-cased'			)
										snake_case:    int      							=config.pop('_commit_hash'      ,						a_			)
										# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
										self.assertEqual(a_      ,						{'do_lower_case': False}			)

										# This model does not have a tokenizer_config so we get back an empty dict.
										snake_case:    Dict      							=get_tokenizer_config(a_			)
										self.assertDictEqual(a_      ,						{}			)

										# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
										snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained(a_			)
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(a_			)
													snake_case:    Any      							=get_tokenizer_config(a_			)

										# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
										self.assertEqual(config['tokenizer_class']      ,						'BertTokenizer'			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[str]:
										try:
													AutoConfig.register('custom'      ,						a_			)

													AutoTokenizer.register(a_      ,						slow_tokenizer_class=a_			)
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(a_			):
																AutoTokenizer.register(a_      ,						slow_tokenizer_class=a_			)

													snake_case:    int      							=CustomTokenizer.from_pretrained(a_			)
													with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(a_			)

																snake_case:    Any      							=AutoTokenizer.from_pretrained(a_			)
																self.assertIsInstance(a_      ,						a_			)

										finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																del TOKENIZER_MAPPING._extra_content[CustomConfig]


							@require_tokenizers
							def    UpperCamelCase     (				self   :						int			)			->					Union[str, Any]:
										try:
													AutoConfig.register('custom'      ,						a_			)

													# Can register in two steps
													AutoTokenizer.register(a_      ,						slow_tokenizer_class=a_			)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,						(CustomTokenizer, None)			)
													AutoTokenizer.register(a_      ,						fast_tokenizer_class=a_			)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,						(CustomTokenizer, CustomTokenizerFast)			)

													del TOKENIZER_MAPPING._extra_content[CustomConfig]
													# Can register in one step
													AutoTokenizer.register(
													    a_      ,						slow_tokenizer_class=a_      ,						fast_tokenizer_class=a_			)
													self.assertEqual(TOKENIZER_MAPPING[CustomConfig]      ,						(CustomTokenizer, CustomTokenizerFast)			)

													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(a_			):
																AutoTokenizer.register(a_      ,						fast_tokenizer_class=a_			)

													# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
													# and that model does not have a tokenizer.json
													with tempfile.TemporaryDirectory() as tmp_dir:
																snake_case:    List[str]      							=BertTokenizerFast.from_pretrained(a_			)
																bert_tokenizer.save_pretrained(a_			)
																snake_case:    Any      							=CustomTokenizerFast.from_pretrained(a_			)

													with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(a_			)

																snake_case:    int      							=AutoTokenizer.from_pretrained(a_			)
																self.assertIsInstance(a_      ,						a_			)

																snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(a_      ,						use_fast=a_			)
																self.assertIsInstance(a_      ,						a_			)

										finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																del TOKENIZER_MAPPING._extra_content[CustomConfig]


							def    UpperCamelCase     (				self   :						Any			)			->					Tuple:
										# If remote code is not set, we will time out when asking whether to load the model.
										with self.assertRaises(a_			):
													snake_case:    Any      							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'			)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(a_			):
													snake_case:    int      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_			)

										snake_case:    int      							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_			)
										self.assertTrue(tokenizer.special_attribute_present			)
										# Test tokenizer can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer.save_pretrained(a_			)
													snake_case:    int      							=AutoTokenizer.from_pretrained(a_      ,						trust_remote_code=a_			)
										self.assertTrue(reloaded_tokenizer.special_attribute_present			)

										if is_tokenizers_available():
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)
													self.assertEqual(reloaded_tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)

													# Test we can also load the slow version
													snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_      ,						use_fast=a_			)
													self.assertTrue(tokenizer.special_attribute_present			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													# Test tokenizer can be reloaded.
													with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(a_			)
																snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained(a_      ,						trust_remote_code=a_      ,						use_fast=a_			)
													self.assertEqual(reloaded_tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													self.assertTrue(reloaded_tokenizer.special_attribute_present			)
										else:
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													self.assertEqual(reloaded_tokenizer.__class__.__name__      ,						'NewTokenizer'			)


							@require_tokenizers
							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Dict:






										class 						a_		(     snake_case    ):
																	UpperCAmelCase			:		Tuple        =				False






										class 						a_		(     snake_case    ):
																	UpperCAmelCase			:		Tuple        =				NewTokenizer
																	UpperCAmelCase			:		Optional[int]        =				False

										try:
													AutoConfig.register('custom'      ,						a_			)
													AutoTokenizer.register(a_      ,						slow_tokenizer_class=a_			)
													AutoTokenizer.register(a_      ,						fast_tokenizer_class=a_			)
													# If remote code is not set, the default is to use local
													snake_case:    Optional[int]      							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)
													self.assertFalse(tokenizer.special_attribute_present			)
													snake_case:    Tuple      							=AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'      ,						use_fast=a_			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													self.assertFalse(tokenizer.special_attribute_present			)

													# If remote code is disabled, we load the local one.
													snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)
													self.assertFalse(tokenizer.special_attribute_present			)
													snake_case:    Tuple      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_      ,						use_fast=a_			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													self.assertFalse(tokenizer.special_attribute_present			)

													# If remote is enabled, we load from the Hub
													snake_case:    Dict      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)
													self.assertTrue(tokenizer.special_attribute_present			)
													snake_case:    Tuple      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer'      ,						trust_remote_code=a_      ,						use_fast=a_			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
													self.assertTrue(tokenizer.special_attribute_present			)

										finally:
													if "custom" in CONFIG_MAPPING._extra_content:
																del CONFIG_MAPPING._extra_content["custom"]
													if CustomConfig in TOKENIZER_MAPPING._extra_content:
																del TOKENIZER_MAPPING._extra_content[CustomConfig]


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Optional[Any]:
										snake_case:    Optional[int]      							=AutoTokenizer.from_pretrained(
										    'hf-internal-testing/test_dynamic_tokenizer_legacy'      ,						trust_remote_code=a_			)
										self.assertTrue(tokenizer.special_attribute_present			)
										if is_tokenizers_available():
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizerFast'			)

													# Test we can also load the slow version
													snake_case:    str      							=AutoTokenizer.from_pretrained(
													    'hf-internal-testing/test_dynamic_tokenizer_legacy'      ,						trust_remote_code=a_      ,						use_fast=a_			)
													self.assertTrue(tokenizer.special_attribute_present			)
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)
										else:
													self.assertEqual(tokenizer.__class__.__name__      ,						'NewTokenizer'			)


							def    UpperCamelCase     (				self   :						List[str]			)			->					Tuple:
										with self.assertRaisesRegex(
										    a_      ,						'bert-base is not a local folder and is not a valid model identifier'			):
													snake_case:    Union[str, Any]      							=AutoTokenizer.from_pretrained('bert-base'			)


							def    UpperCamelCase     (				self   :						Dict			)			->					List[Any]:
										with self.assertRaisesRegex(
										    a_      ,						r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'			):
													snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained(a_      ,						revision='aaaaaa'			)




							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					str:
										# Make sure we have cached the tokenizer.
										snake_case:    Optional[Any]      							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'			)
										with RequestCounter() as counter:
													snake_case:    Tuple      							=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'			)
													self.assertEqual(counter.get_request_count      ,						0			)
													self.assertEqual(counter.head_request_count      ,						1			)
													self.assertEqual(counter.other_request_count      ,						0			)









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

										self.assertEqual(output_a.shape      ,						sample.shape			)
										self.assertEqual(output_a.shape      ,						output_a.shape			)


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

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


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

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




