
"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




class  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Tuple          =    CanineTokenizer
				a__:      Tuple          =    False




				def        UpperCAmelCase__						(				self   ):
							super().setUp()
							lowerCamelCase_					=  CanineTokenizer()
							tokenizer.save_pretrained(self.tmpdirname   )




				@cached_property
				def        UpperCAmelCase__						(				self   ):
							return CanineTokenizer.from_pretrained('''google/canine-s'''   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )
							lowerCamelCase_					=  1024
							return tokenizer




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.canine_tokenizer
							lowerCamelCase_					=  ['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
							# fmt: off
							lowerCamelCase_					=  [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
							# fmt: on
							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
							self.assertIsInstance(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  list(batch.input_ids.numpy()[0]   )

							self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )

							self.assertEqual((2, 39)							,						batch.input_ids.shape   )
							self.assertEqual((2, 39)							,						batch.attention_mask.shape   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.canine_tokenizer
							lowerCamelCase_					=  ['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
							lowerCamelCase_					=  tokenizer(UpperCAmelCase							,						padding=UpperCAmelCase							,						return_tensors='''pt'''   )
							# check if input_ids, attention_mask and token_type_ids are returned
							self.assertIn('''input_ids'''							,						UpperCAmelCase   )
							self.assertIn('''attention_mask'''							,						UpperCAmelCase   )
							self.assertIn('''token_type_ids'''							,						UpperCAmelCase   )




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.canine_tokenizer
							lowerCamelCase_					=  [
							    '''What\'s the weater?''',
							    '''It\'s about 25 degrees.''',
							]
							lowerCamelCase_					=  tokenizer(
							    text_target=UpperCAmelCase							,						max_length=32							,						padding='''max_length'''							,						truncation=UpperCAmelCase							,						return_tensors='''pt'''   )
							self.assertEqual(32							,						targets['''input_ids'''].shape[1]   )




				def        UpperCAmelCase__						(				self   ):
							# safety check on max_len default value so we are sure the test works
							lowerCamelCase_					=  self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													self.assertNotEqual(tokenizer.model_max_length							,						42   )

        # Now let's start the test
							lowerCamelCase_					=  self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													# Isolate this from the other tests because we save additional tokens/etc
													lowerCamelCase_					=  tempfile.mkdtemp()

													lowerCamelCase_					=  ''' He is very happy, UNwant\u00E9d,running'''
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													tokenizer.save_pretrained(UpperCAmelCase   )

													lowerCamelCase_					=  tokenizer.__class__.from_pretrained(UpperCAmelCase   )
													lowerCamelCase_					=  after_tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )

													shutil.rmtree(UpperCAmelCase   )

							lowerCamelCase_					=  self.get_tokenizers(model_max_length=42   )
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													# Isolate this from the other tests because we save additional tokens/etc
													lowerCamelCase_					=  tempfile.mkdtemp()

													lowerCamelCase_					=  ''' He is very happy, UNwant\u00E9d,running'''

													lowerCamelCase_					=  tokenizer.additional_special_tokens

													# We can add a new special token for Canine as follows:
													lowerCamelCase_					=  chr(0Xe_007   )
													additional_special_tokens.append(UpperCAmelCase   )
													tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}   )
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													tokenizer.save_pretrained(UpperCAmelCase   )

													lowerCamelCase_					=  tokenizer.__class__.from_pretrained(UpperCAmelCase   )
													lowerCamelCase_					=  after_tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertListEqual(UpperCAmelCase							,						UpperCAmelCase   )
													self.assertIn(UpperCAmelCase							,						after_tokenizer.additional_special_tokens   )
													self.assertEqual(after_tokenizer.model_max_length							,						42   )

													lowerCamelCase_					=  tokenizer.__class__.from_pretrained(UpperCAmelCase							,						model_max_length=43   )
													self.assertEqual(tokenizer.model_max_length							,						43   )

													shutil.rmtree(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizers(do_lower_case=UpperCAmelCase   )
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													lowerCamelCase_		, lowerCamelCase_					=  self.get_clean_sequence(UpperCAmelCase   )

													# a special token for Canine can be defined as follows:
													lowerCamelCase_					=  0Xe_005
													lowerCamelCase_					=  chr(UpperCAmelCase   )

													tokenizer.add_special_tokens({'''cls_token''': special_token}   )
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(len(UpperCAmelCase   )							,						1   )

													lowerCamelCase_					=  tokenizer.decode(ids + encoded_special_token							,						clean_up_tokenization_spaces=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )

													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													self.assertEqual(UpperCAmelCase							,						input_encoded + special_token_id   )

													lowerCamelCase_					=  tokenizer.decode(UpperCAmelCase							,						skip_special_tokens=UpperCAmelCase   )
													self.assertTrue(special_token not in decoded   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizers(do_lower_case=UpperCAmelCase   )
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													lowerCamelCase_					=  chr(0Xe_005   )
													lowerCamelCase_					=  chr(0Xe_006   )

													# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
													tokenizer.add_tokens([SPECIAL_TOKEN_1]							,						special_tokens=UpperCAmelCase   )
													# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
													# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
													tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}   )

													lowerCamelCase_					=  tokenizer.tokenize(UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer.tokenize(UpperCAmelCase   )

													self.assertEqual(len(UpperCAmelCase   )							,						1   )
													self.assertEqual(len(UpperCAmelCase   )							,						1   )
													self.assertEqual(token_a[0]							,						UpperCAmelCase   )
													self.assertEqual(token_a[0]							,						UpperCAmelCase   )




				@require_tokenizers
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizers(do_lower_case=UpperCAmelCase   )
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													# a special token for Canine can be defined as follows:
													lowerCamelCase_					=  0Xe_006
													lowerCamelCase_					=  chr(UpperCAmelCase   )

													lowerCamelCase_					=  AddedToken(UpperCAmelCase							,						lstrip=UpperCAmelCase   )
													tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}   )

													with tempfile.TemporaryDirectory() as tmp_dir_name:
																tokenizer.save_pretrained(UpperCAmelCase   )
																tokenizer.from_pretrained(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  []
							if self.test_slow_tokenizer:
										tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

							if self.test_rust_tokenizer:
										tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

							for tokenizer_class, tokenizer_utils in tokenizer_list:
										with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer_utils.save_pretrained(UpperCAmelCase   )

													with open(os.path.join(UpperCAmelCase							,						'''special_tokens_map.json'''   )							,						encoding='''utf-8'''   ) as json_file:
																lowerCamelCase_					=  json.load(UpperCAmelCase   )

													with open(os.path.join(UpperCAmelCase							,						'''tokenizer_config.json'''   )							,						encoding='''utf-8'''   ) as json_file:
																lowerCamelCase_					=  json.load(UpperCAmelCase   )

													# a special token for Canine can be defined as follows:
													lowerCamelCase_					=  0Xe_006
													lowerCamelCase_					=  chr(UpperCAmelCase   )

													lowerCamelCase_					=  [new_token_a]
													lowerCamelCase_					=  [new_token_a]

													with open(os.path.join(UpperCAmelCase							,						'''special_tokens_map.json'''   )							,						'''w'''							,						encoding='''utf-8'''   ) as outfile:
																json.dump(UpperCAmelCase							,						UpperCAmelCase   )
													with open(os.path.join(UpperCAmelCase							,						'''tokenizer_config.json'''   )							,						'''w'''							,						encoding='''utf-8'''   ) as outfile:
																json.dump(UpperCAmelCase							,						UpperCAmelCase   )

													# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
													# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
													# "special_tokens_map.json" files
													lowerCamelCase_					=  tokenizer_class.from_pretrained(UpperCAmelCase							,						extra_ids=0   )
													self.assertIn(UpperCAmelCase							,						tokenizer_without_change_in_init.additional_special_tokens   )
													# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]							,						tokenizer_without_change_in_init.convert_ids_to_tokens(
													        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]   )   )							,						)

													lowerCamelCase_					=  0Xe_007
													lowerCamelCase_					=  chr(UpperCAmelCase   )
													# Now we test that we can change the value of additional_special_tokens in the from_pretrained
													lowerCamelCase_					=  [AddedToken(UpperCAmelCase							,						lstrip=UpperCAmelCase   )]
													lowerCamelCase_					=  tokenizer_class.from_pretrained(
													    UpperCAmelCase							,						additional_special_tokens=UpperCAmelCase							,						extra_ids=0   )

													self.assertIn(UpperCAmelCase							,						tokenizer.additional_special_tokens   )
													# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]							,						tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]   )   )   )




				@require_tokenizers
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizers(do_lower_case=UpperCAmelCase   )
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													lowerCamelCase_					=  '''hello world'''
													if self.space_between_special_tokens:
																lowerCamelCase_					=  '''[CLS] hello world [SEP]'''
													else:
																lowerCamelCase_					=  input
													lowerCamelCase_					=  tokenizer.encode(UpperCAmelCase							,						add_special_tokens=UpperCAmelCase   )
													lowerCamelCase_					=  tokenizer.decode(UpperCAmelCase							,						spaces_between_special_tokens=self.space_between_special_tokens   )
													self.assertIn(UpperCAmelCase							,						[output, output.lower()]   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizers()
							for tokenizer in tokenizers:
										with self.subTest(f"{tokenizer.__class__.__name__}"   ):
													lowerCamelCase_					=  [
													    '''bos_token''',
													    '''eos_token''',
													    '''unk_token''',
													    '''sep_token''',
													    '''pad_token''',
													    '''cls_token''',
													    '''mask_token''',
													]

							lowerCamelCase_					=  '''a'''
							lowerCamelCase_					=  ord(UpperCAmelCase   )

							for attr in attributes_list:
										setattr(UpperCAmelCase							,						attr + '''_id'''							,						UpperCAmelCase   )
										self.assertEqual(getattr(UpperCAmelCase							,						UpperCAmelCase   )							,						UpperCAmelCase   )
										self.assertEqual(getattr(UpperCAmelCase							,						attr + '''_id'''   )							,						UpperCAmelCase   )

										setattr(UpperCAmelCase							,						attr + '''_id'''							,						UpperCAmelCase   )
										self.assertEqual(getattr(UpperCAmelCase							,						UpperCAmelCase   )							,						UpperCAmelCase   )
										self.assertEqual(getattr(UpperCAmelCase							,						attr + '''_id'''   )							,						UpperCAmelCase   )

							setattr(UpperCAmelCase							,						'''additional_special_tokens_ids'''							,						[]   )
							self.assertListEqual(getattr(UpperCAmelCase							,						'''additional_special_tokens'''   )							,						[]   )
							self.assertListEqual(getattr(UpperCAmelCase							,						'''additional_special_tokens_ids'''   )							,						[]   )

							lowerCamelCase_					=  0Xe_006
							lowerCamelCase_					=  chr(UpperCAmelCase   )
							setattr(UpperCAmelCase							,						'''additional_special_tokens_ids'''							,						[additional_special_token_id]   )
							self.assertListEqual(getattr(UpperCAmelCase							,						'''additional_special_tokens'''   )							,						[additional_special_token]   )
							self.assertListEqual(getattr(UpperCAmelCase							,						'''additional_special_tokens_ids'''   )							,						[additional_special_token_id]   )




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							pass





				def        UpperCAmelCase__						(				self   ):
							pass





"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    (DDPMScheduler,)




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,						[0.0_0_2, 0.0_2, 0.2, 2]   ):
										self.check_over_configs(beta_start=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




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




				def        UpperCAmelCase__						(				self   ):
							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCAmelCase   )




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




				def        UpperCAmelCase__						(				self   ):
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(prediction_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for t in [0, 500, 999]:
										self.check_over_forward(time_step=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0_9_7_9   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.0_2   )   ) < 1e-5




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

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

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

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

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

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

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

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

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

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

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=UpperCAmelCase   )

							lowerCamelCase_					=  scheduler.timesteps

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

										lowerCamelCase_					=  scheduler.previous_timestep(UpperCAmelCase   )
										lowerCamelCase_					=  prev_t.item()

										self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 51, 0]

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]
							lowerCamelCase_					=  len(UpperCAmelCase   )

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





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [scheduler.config.num_train_timesteps]

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



