
"""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        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    0



		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
												_UpperCamelCase									=    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():
												_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
												self.assertIsNotNone(__a)
												self.assertIsInstance(__a							,						(GPTaTokenizer, GPTaTokenizerFast))
												self.assertGreater(len(__a)							,						0)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							self.assertIsInstance(__a							,						(BertTokenizer, BertTokenizerFast))
							self.assertEqual(tokenizer.vocab_size							,						12)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							self.assertIsInstance(__a							,						(RobertaTokenizer, RobertaTokenizerFast))
							self.assertEqual(tokenizer.vocab_size							,						20)



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoConfig.from_pretrained(__a)
							self.assertIsInstance(__a							,						__a)
							# Check that tokenizer_type ≠ model_type
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a							,						config=__a)
							self.assertIsInstance(__a							,						(BertTokenizer, BertTokenizerFast))
							self.assertEqual(tokenizer.vocab_size							,						12)



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy('''./tests/fixtures/vocab.txt'''							,						os.path.join(__a							,						'''vocab.txt'''))

												_UpperCamelCase									=    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'''))

												_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a							,						tokenizer_type='''gpt2'''							,						use_fast=__a)
												self.assertIsInstance(__a							,						__a)



		@require_tokenizers
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy('''./tests/fixtures/vocab.txt'''							,						os.path.join(__a							,						'''vocab.txt'''))

												_UpperCamelCase									=    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'''))

												_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a							,						tokenizer_type='''gpt2''')
												self.assertIsInstance(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							with pytest.raises(__a):
												AutoTokenizer.from_pretrained('''./'''							,						tokenizer_type='''xxx''')



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






							'''simple docstring'''
							for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
												_UpperCamelCase									=    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_12)



		@require_tokenizers
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							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'''							,						):
																	_UpperCamelCase									=    tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists''')



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							# 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
							_UpperCamelCase									=    TOKENIZER_MAPPING.values()
							_UpperCamelCase									=    []

							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)			->			List[str]:






							'''simple docstring'''
							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)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''distilbert-base-uncased'''							,						do_lower_case=__a)
							_UpperCamelCase									=    '''Hello, world. How are you?'''
							_UpperCamelCase									=    tokenizer.tokenize(__a)
							self.assertEqual('''[UNK]'''							,						tokens[0])

							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''							,						do_lower_case=__a)
							_UpperCamelCase									=    tokenizer.tokenize(__a)
							self.assertEqual('''[UNK]'''							,						tokens[0])



		@require_tokenizers
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''')
							self.assertEqual(type(__a)							,						__a)
							self.assertEqual(tokenizer.model_max_length							,						5_12)
							self.assertEqual(tokenizer.vocab_size							,						3_00_00)
							self.assertEqual(tokenizer.unk_token							,						'''[UNK]''')
							self.assertEqual(tokenizer.padding_side							,						'''right''')
							self.assertEqual(tokenizer.truncation_side							,						'''right''')



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							self.assertIsInstance(__a							,						(BertTokenizer, BertTokenizerFast))
							with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__a)
												_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)

							self.assertIsInstance(__a							,						tokenizer.__class__)
							self.assertEqual(tokenizera.vocab_size							,						12)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    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:






							'''simple docstring'''
							# Check we can load the tokenizer config of an online model.
							_UpperCamelCase									=    get_tokenizer_config('''bert-base-cased''')
							_UpperCamelCase									=    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.
							_UpperCamelCase									=    get_tokenizer_config(__a)
							self.assertDictEqual(__a							,						{})

							# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
							_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
							with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__a)
												_UpperCamelCase									=    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)			->			List[str]:






							'''simple docstring'''
							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)

												_UpperCamelCase									=    CustomTokenizer.from_pretrained(__a)
												with tempfile.TemporaryDirectory() as tmp_dir:
																	tokenizer.save_pretrained(__a)

																	_UpperCamelCase									=    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)			->			str:






							'''simple docstring'''
							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:
																	_UpperCamelCase									=    BertTokenizerFast.from_pretrained(__a)
																	bert_tokenizer.save_pretrained(__a)
																	_UpperCamelCase									=    CustomTokenizerFast.from_pretrained(__a)

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

																	_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a)
																	self.assertIsInstance(__a							,						__a)

																	_UpperCamelCase									=    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)			->			Optional[int]:






							'''simple docstring'''
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(__a):
												_UpperCamelCase									=    AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''')
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(__a):
												_UpperCamelCase									=    AutoTokenizer.from_pretrained(
												    '''hf-internal-testing/test_dynamic_tokenizer'''							,						trust_remote_code=__a)

							_UpperCamelCase									=    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)
												_UpperCamelCase									=    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
												_UpperCamelCase									=    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)
																	_UpperCamelCase									=    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)			->			Tuple:






							'''simple docstring'''






							class        _UpperCAmelCase(       lowerCamelCase      ):
									lowercase__         = False






							class        _UpperCAmelCase(       lowerCamelCase      ):
									lowercase__         = NewTokenizer
									lowercase__         = 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
												_UpperCamelCase									=    AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''')
												self.assertEqual(tokenizer.__class__.__name__							,						'''NewTokenizerFast''')
												self.assertFalse(tokenizer.special_attribute_present)
												_UpperCamelCase									=    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.
												_UpperCamelCase									=    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)
												_UpperCamelCase									=    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
												_UpperCamelCase									=    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)
												_UpperCamelCase									=    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]:






							'''simple docstring'''
							_UpperCamelCase									=    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
												_UpperCamelCase									=    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)			->			int:






							'''simple docstring'''
							with self.assertRaisesRegex(
							    __a							,						'''bert-base is not a local folder and is not a valid model identifier'''):
												_UpperCamelCase									=    AutoTokenizer.from_pretrained('''bert-base''')



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							with self.assertRaisesRegex(
							    __a							,						R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
												_UpperCamelCase									=    AutoTokenizer.from_pretrained(__a							,						revision='''aaaaaa''')




		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							# Make sure we have cached the tokenizer.
							_UpperCamelCase									=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''')
							with RequestCounter() as counter:
												_UpperCamelCase									=    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 DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''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=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






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



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

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

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

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

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

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

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

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

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

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

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

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

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

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



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

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




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [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)






